Pub Date : 2024-12-03DOI: 10.1109/tcyb.2024.3502790
Li-Wei Mao, Guang-Hong Yang
{"title":"Optimal Stealthy Attack With Side Information Against Remote State Estimation: A Corrupted Innovation-Based Strategy","authors":"Li-Wei Mao, Guang-Hong Yang","doi":"10.1109/tcyb.2024.3502790","DOIUrl":"https://doi.org/10.1109/tcyb.2024.3502790","url":null,"abstract":"","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"31 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142776724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-27DOI: 10.1109/TCYB.2024.3499297
{"title":"IEEE Transactions on Cybernetics","authors":"","doi":"10.1109/TCYB.2024.3499297","DOIUrl":"https://doi.org/10.1109/TCYB.2024.3499297","url":null,"abstract":"","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"54 12","pages":"C3-C3"},"PeriodicalIF":9.4,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10770076","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142736225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-27DOI: 10.1109/TCYB.2024.3498100
Rafael Ayllón-Gavilán;David Guijo-Rubio;Pedro Antonio Gutiérrez;Anthony Bagnall;César Hervás-Martínez
Time-series classification (TSC) covers the supervised learning problem where input data is provided in the form of series of values observed through repeated measurements over time, and whose objective is to predict the category to which they belong. When the class values are ordinal, classifiers that take this into account can perform better than nominal classifiers. Time-series ordinal classification (TSOC) is the field bridging this gap, yet unexplored in the literature. There are a wide range of time-series problems showing an ordered label structure, and TSC techniques that ignore the order relationship discard useful information. Hence, this article presents the first benchmarking of TSOC methodologies, exploiting the ordering of the target labels to boost the performance of current TSC state of the art. Both convolutional- and deep-learning-based methodologies (among the best performing alternatives for nominal TSC) are adapted for TSOC. For the experiments, a selection of 29 ordinal problems has been made. In this way, this article contributes to the establishment of the state of the art in TSOC. The results obtained by ordinal versions are found to be significantly better than current nominal TSC techniques in terms of ordinal performance metrics, outlining the importance of considering the ordering of the labels when dealing with this kind of problems.
{"title":"Convolutional- and Deep Learning-Based Techniques for Time Series Ordinal Classification","authors":"Rafael Ayllón-Gavilán;David Guijo-Rubio;Pedro Antonio Gutiérrez;Anthony Bagnall;César Hervás-Martínez","doi":"10.1109/TCYB.2024.3498100","DOIUrl":"10.1109/TCYB.2024.3498100","url":null,"abstract":"Time-series classification (TSC) covers the supervised learning problem where input data is provided in the form of series of values observed through repeated measurements over time, and whose objective is to predict the category to which they belong. When the class values are ordinal, classifiers that take this into account can perform better than nominal classifiers. Time-series ordinal classification (TSOC) is the field bridging this gap, yet unexplored in the literature. There are a wide range of time-series problems showing an ordered label structure, and TSC techniques that ignore the order relationship discard useful information. Hence, this article presents the first benchmarking of TSOC methodologies, exploiting the ordering of the target labels to boost the performance of current TSC state of the art. Both convolutional- and deep-learning-based methodologies (among the best performing alternatives for nominal TSC) are adapted for TSOC. For the experiments, a selection of 29 ordinal problems has been made. In this way, this article contributes to the establishment of the state of the art in TSOC. The results obtained by ordinal versions are found to be significantly better than current nominal TSC techniques in terms of ordinal performance metrics, outlining the importance of considering the ordering of the labels when dealing with this kind of problems.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 2","pages":"537-549"},"PeriodicalIF":9.4,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10769513","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142752821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-27DOI: 10.1109/TCYB.2024.3507252
{"title":"IEEE Foundation - Reflecting on 50 Years of Impact","authors":"","doi":"10.1109/TCYB.2024.3507252","DOIUrl":"https://doi.org/10.1109/TCYB.2024.3507252","url":null,"abstract":"","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"54 12","pages":"8000-8000"},"PeriodicalIF":9.4,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10770078","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142736560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-27DOI: 10.1109/TCYB.2020.3045819
Maolong Lv;Wenwu Yu;Jinde Cao;Simone Baldi
Presents corrections to the paper, (Correction for “Consensus in High-Power Multiagent Systems With Mixed Unknown Control Directions via Hybrid Nussbaum-Based Control”).
{"title":"Correction for “Consensus in High-Power Multiagent Systems With Mixed Unknown Control Directions via Hybrid Nussbaum-Based Control”","authors":"Maolong Lv;Wenwu Yu;Jinde Cao;Simone Baldi","doi":"10.1109/TCYB.2020.3045819","DOIUrl":"https://doi.org/10.1109/TCYB.2020.3045819","url":null,"abstract":"Presents corrections to the paper, (Correction for “Consensus in High-Power Multiagent Systems With Mixed Unknown Control Directions via Hybrid Nussbaum-Based Control”).","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"54 12","pages":"7999-7999"},"PeriodicalIF":9.4,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10770080","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142736472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-27DOI: 10.1109/tcyb.2024.3496794
Niu Huo, Dong Shen, Daniel W. C. Ho
{"title":"Encoding–Decoding-Based Quantized Learning Control Using Spherical Polar Coordinates","authors":"Niu Huo, Dong Shen, Daniel W. C. Ho","doi":"10.1109/tcyb.2024.3496794","DOIUrl":"https://doi.org/10.1109/tcyb.2024.3496794","url":null,"abstract":"","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"72 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142752942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-27DOI: 10.1109/TCYB.2024.3499299
{"title":"IEEE Transactions on Cybernetics","authors":"","doi":"10.1109/TCYB.2024.3499299","DOIUrl":"https://doi.org/10.1109/TCYB.2024.3499299","url":null,"abstract":"","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"54 12","pages":"C4-C4"},"PeriodicalIF":9.4,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10770075","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142736242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-27DOI: 10.1109/TCYB.2024.3498323
Renyongkang Zhang;Ge Guo;Zeng-Di Zhou
This article proposes a distributed optimization algorithm with a convergence time that can be assigned in advance according to task requirements. To this end, a sliding manifold is introduced to achieve the sum of local gradients approaching zero, based on which a distributed protocol is derived to reach a consensus minimizing the global cost. A novel approach for convergence analysis is derived in a unified settling time framework, resulting in an algorithm that can precisely converge to the optimal solution at the prescribed time. The method is interesting as it simply requires the primal states to be shared over the network, which implies less communication requirements. The result is extended to scenarios with time-varying objective function, by introducing local gradients prediction and nonsmooth consensus terms. Numerical simulations are provided to corroborate the effectiveness of the proposed algorithms.
{"title":"Balance of Communication and Convergence: Predefined-Time Distributed Optimization Based on Zero-Gradient-Sum","authors":"Renyongkang Zhang;Ge Guo;Zeng-Di Zhou","doi":"10.1109/TCYB.2024.3498323","DOIUrl":"10.1109/TCYB.2024.3498323","url":null,"abstract":"This article proposes a distributed optimization algorithm with a convergence time that can be assigned in advance according to task requirements. To this end, a sliding manifold is introduced to achieve the sum of local gradients approaching zero, based on which a distributed protocol is derived to reach a consensus minimizing the global cost. A novel approach for convergence analysis is derived in a unified settling time framework, resulting in an algorithm that can precisely converge to the optimal solution at the prescribed time. The method is interesting as it simply requires the primal states to be shared over the network, which implies less communication requirements. The result is extended to scenarios with time-varying objective function, by introducing local gradients prediction and nonsmooth consensus terms. Numerical simulations are provided to corroborate the effectiveness of the proposed algorithms.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 2","pages":"661-671"},"PeriodicalIF":9.4,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142752828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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