Pub Date : 2024-10-16DOI: 10.1109/TSMC.2024.3479668
{"title":"IEEE Transactions on Systems, Man, and Cybernetics publication information","authors":"","doi":"10.1109/TSMC.2024.3479668","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3479668","url":null,"abstract":"","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"54 11","pages":"C2-C2"},"PeriodicalIF":8.6,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10720542","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142443118","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 : 2024-10-16DOI: 10.1109/TSMC.2024.3479613
{"title":"Information For Authors","authors":"","doi":"10.1109/TSMC.2024.3479613","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3479613","url":null,"abstract":"","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"54 11","pages":"C4-C4"},"PeriodicalIF":8.6,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10720577","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142447024","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 : 2024-10-16DOI: 10.1109/TSMC.2024.3479689
{"title":"TechRxiv: Share Your Preprint Research with the World!","authors":"","doi":"10.1109/TSMC.2024.3479689","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3479689","url":null,"abstract":"","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"54 11","pages":"7156-7156"},"PeriodicalIF":8.6,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10720547","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142450970","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 : 2024-10-16DOI: 10.1109/TSMC.2024.3479670
{"title":"IEEE Systems, Man, and Cybernetics Society Information","authors":"","doi":"10.1109/TSMC.2024.3479670","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3479670","url":null,"abstract":"","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"54 11","pages":"C3-C3"},"PeriodicalIF":8.6,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10720546","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142450971","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 : 2024-10-08DOI: 10.1109/TSMC.2024.3467057
Yuxiang Zhang;Shuzhi Sam Ge;Ruihang Ji
The classical optimal control of the linear system assumes that the system is stabilizable, thereby deriving the optimal control with the outcome that the solution inherently stabilizes the system. Such optimization does not distinctly address stabilization and optimization as separate concerns, leading to a situation where, as the system expands in size and complexity, the optimal controller suffers performance decreases and becomes increasingly sensitive and fragile. In this article, nested optimized control (NOC) and nested optimized adaptive control (NOAC) are introduced to explicitly handle the stabilization, optimization/adaptation for unknown parameters separately in an effort to strike a balance between guaranteed stability and optimal control. The robustness of the classical optimal control is inherent in the design itself, and the stability margin is relatively small subject to parameter uncertainties. In our NOC, the robustness is explicitly handled by the state feedback control and its stability margin is larger than the classical one, because of the introduction of the explicit state feedback control loop, the next optimized control loop is introduced for system performance. Note that, the term optimized rather than optimal is used here as it is not the classical optimal control anymore, but a fundamental change in design methodology. To further improve the stability margin due to parameter uncertainties, adaptive control is introduced to approximate the parameters in an effort to further improve the stability margin. The effectiveness of the proposed method is demonstrated through comparative examples that highlight its advantages.
{"title":"Nested Optimized Adaptive Control for Linear Systems","authors":"Yuxiang Zhang;Shuzhi Sam Ge;Ruihang Ji","doi":"10.1109/TSMC.2024.3467057","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3467057","url":null,"abstract":"The classical optimal control of the linear system assumes that the system is stabilizable, thereby deriving the optimal control with the outcome that the solution inherently stabilizes the system. Such optimization does not distinctly address stabilization and optimization as separate concerns, leading to a situation where, as the system expands in size and complexity, the optimal controller suffers performance decreases and becomes increasingly sensitive and fragile. In this article, nested optimized control (NOC) and nested optimized adaptive control (NOAC) are introduced to explicitly handle the stabilization, optimization/adaptation for unknown parameters separately in an effort to strike a balance between guaranteed stability and optimal control. The robustness of the classical optimal control is inherent in the design itself, and the stability margin is relatively small subject to parameter uncertainties. In our NOC, the robustness is explicitly handled by the state feedback control and its stability margin is larger than the classical one, because of the introduction of the explicit state feedback control loop, the next optimized control loop is introduced for system performance. Note that, the term optimized rather than optimal is used here as it is not the classical optimal control anymore, but a fundamental change in design methodology. To further improve the stability margin due to parameter uncertainties, adaptive control is introduced to approximate the parameters in an effort to further improve the stability margin. The effectiveness of the proposed method is demonstrated through comparative examples that highlight its advantages.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"54 12","pages":"7756-7769"},"PeriodicalIF":8.6,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672056","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}
Since the inception of Industry 5.0 in 2021, a growing number of researchers have begun to pay their attention to the revolutionary shift it brings. The principles of Industry 5.0, including human-centric, sustainability, and emphasis on ecological and social values, will become the new paradigm for future industrial development. In this transformative landscape, artificial intelligence (AI) plays a pivotal role, and foundation models based on ChatGPT are set to reshape the organizational structure of industries. In this article, we introduce a multimodal perception and decision-making system built upon a foundational model. This system integrates image and point cloud data to enhance perception accuracy and provide ample information for decision making. It is designed to achieve a deep integration of AI and human-centric autonomous driving within the context of Industry 5.0. We introduce a cross-domain learning approach in the system architecture, along with a model training method from foundation models to handle complex road conditions. The proposed method enables road drivable area segmentation on complex unstructured roads. To address the issue of increased variance caused by the residual structure employed in previous works, this article introduces a distribution correction module, which effectively mitigates this problem. Furthermore, to achieve high-performance perception systems in intricate road scenarios, we put forth a multimodal perception fusion method in this study. The experiments demonstrate the superiority of this approach over single-sensor perception. This work contributes to the ongoing discourse on the convergence of AI, human-centric values, and advanced driving systems within the framework of Industry 5.0.
{"title":"Multimodal Perception and Decision-Making Systems for Complex Roads Based on Foundation Models","authors":"Lili Fan;Yutong Wang;Hui Zhang;Changxian Zeng;Yunjie Li;Chao Gou;Hui Yu","doi":"10.1109/TSMC.2024.3444277","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3444277","url":null,"abstract":"Since the inception of Industry 5.0 in 2021, a growing number of researchers have begun to pay their attention to the revolutionary shift it brings. The principles of Industry 5.0, including human-centric, sustainability, and emphasis on ecological and social values, will become the new paradigm for future industrial development. In this transformative landscape, artificial intelligence (AI) plays a pivotal role, and foundation models based on ChatGPT are set to reshape the organizational structure of industries. In this article, we introduce a multimodal perception and decision-making system built upon a foundational model. This system integrates image and point cloud data to enhance perception accuracy and provide ample information for decision making. It is designed to achieve a deep integration of AI and human-centric autonomous driving within the context of Industry 5.0. We introduce a cross-domain learning approach in the system architecture, along with a model training method from foundation models to handle complex road conditions. The proposed method enables road drivable area segmentation on complex unstructured roads. To address the issue of increased variance caused by the residual structure employed in previous works, this article introduces a distribution correction module, which effectively mitigates this problem. Furthermore, to achieve high-performance perception systems in intricate road scenarios, we put forth a multimodal perception fusion method in this study. The experiments demonstrate the superiority of this approach over single-sensor perception. This work contributes to the ongoing discourse on the convergence of AI, human-centric values, and advanced driving systems within the framework of Industry 5.0.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"54 11","pages":"6561-6569"},"PeriodicalIF":8.6,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142443111","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 : 2024-10-01DOI: 10.1109/TSMC.2024.3457031
Bin Wei;Junyong Zhai;Engang Tian;Ju H. Park
In this article, we address the quasi-bipartite consensus problem concerning a type of time-varying multiagent systems (MASs), which are subjected to energy harvesting protocols and random occurring faults. The creative aspects of this study can be emphasized as follows: first, the dynamics of the considered system are time-varying and stochastic, which is in accordance with the practical application closely. Meanwhile, a differential privacy protection mechanism is proposed, which realizes the goal of sensitive information protection via adding random noises following the Laplace probability distribution, and an energy collecting procedure is proposed to gather external energy for information exchange. Furthermore, the ultimate control objective is to implement an appropriate quasi-bipartite consensus controller to ensure that the probability of the state/output error residing within an allowable range exceed a fixed scalar. By means of recursive linear matrix inequalities (RLMIs) and the matrix analysis theory, sufficient conditions for guaranteeing quasi-bipartite consensus are derived, and an optimal constraint set is obtained by addressing a convex minimization problem. Finally, the availability of the employed approach are upheld by two illustrative examples.
{"title":"Quasi-Bipartite Consensus Control of Cooperation-Competition Multiagent Systems: When Privacy Preservation Meets Energy Harvesting Protocols","authors":"Bin Wei;Junyong Zhai;Engang Tian;Ju H. Park","doi":"10.1109/TSMC.2024.3457031","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3457031","url":null,"abstract":"In this article, we address the quasi-bipartite consensus problem concerning a type of time-varying multiagent systems (MASs), which are subjected to energy harvesting protocols and random occurring faults. The creative aspects of this study can be emphasized as follows: first, the dynamics of the considered system are time-varying and stochastic, which is in accordance with the practical application closely. Meanwhile, a differential privacy protection mechanism is proposed, which realizes the goal of sensitive information protection via adding random noises following the Laplace probability distribution, and an energy collecting procedure is proposed to gather external energy for information exchange. Furthermore, the ultimate control objective is to implement an appropriate quasi-bipartite consensus controller to ensure that the probability of the state/output error residing within an allowable range exceed a fixed scalar. By means of recursive linear matrix inequalities (RLMIs) and the matrix analysis theory, sufficient conditions for guaranteeing quasi-bipartite consensus are derived, and an optimal constraint set is obtained by addressing a convex minimization problem. Finally, the availability of the employed approach are upheld by two illustrative examples.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"54 12","pages":"7697-7709"},"PeriodicalIF":8.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672071","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}
This article investigates the problem of cooperative guidance against maneuvering target under malicious attacks. In consideration of the false-data injection attacks (FDIAs), a reputation-based cooperative guidance law with fault tolerance is proposed to drive multiflight vehicles to reach a maneuvering target simultaneously. A novel prescribed performance function (PPF) with predefined-time convergence is presented by taking into account the limitation of available capacity. By incorporating the reputation system based on confidence factors and trust factors, which are leveraged to identify the attacked communication links or vehicle members, the fault-tolerant behavior can be achieved to resist the effects resulted from the FDIAs. The effectiveness of the reputation-based fault-tolerant cooperative guidance method is verified by numerical simulation.
{"title":"Performance Prescribed Cooperative Guidance Against Maneuvering Target Under Malicious Attacks","authors":"Guofei Li;Qilin Zhong;Zongyu Zuo;Yunjie Wu;Jinhu Lü","doi":"10.1109/TSMC.2024.3461816","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3461816","url":null,"abstract":"This article investigates the problem of cooperative guidance against maneuvering target under malicious attacks. In consideration of the false-data injection attacks (FDIAs), a reputation-based cooperative guidance law with fault tolerance is proposed to drive multiflight vehicles to reach a maneuvering target simultaneously. A novel prescribed performance function (PPF) with predefined-time convergence is presented by taking into account the limitation of available capacity. By incorporating the reputation system based on confidence factors and trust factors, which are leveraged to identify the attacked communication links or vehicle members, the fault-tolerant behavior can be achieved to resist the effects resulted from the FDIAs. The effectiveness of the reputation-based fault-tolerant cooperative guidance method is verified by numerical simulation.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"54 12","pages":"7770-7782"},"PeriodicalIF":8.6,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672028","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 : 2024-09-30DOI: 10.1109/TSMC.2024.3452713
Yige Guo;Qing Gao;Jinhu Lü;Gang Feng
The stabilization problem of two categories of discrete-time linear time-varying (LTV) systems subject to unbounded distributed input delays is investigated in this article. A truncated predictor feedback law is first built for a category of systems under some common assumptions. Then, under some weakened assumptions, a predictor-type feedback law is developed for the other category of more general systems. The global exponential stability of the closed-loop systems is proved. Furthermore, the result on the truncated predictor feedback control law includes many existing results on LTV systems subject to bounded input delays and linear time-invariant (LTI) systems subject to unbounded input delays as special cases. Finally, the results of simulations validate the effectiveness of the developed control laws.
{"title":"Stabilization of Discrete-Time Time-Varying Systems Subject to Unbounded Distributed Input Delays","authors":"Yige Guo;Qing Gao;Jinhu Lü;Gang Feng","doi":"10.1109/TSMC.2024.3452713","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3452713","url":null,"abstract":"The stabilization problem of two categories of discrete-time linear time-varying (LTV) systems subject to unbounded distributed input delays is investigated in this article. A truncated predictor feedback law is first built for a category of systems under some common assumptions. Then, under some weakened assumptions, a predictor-type feedback law is developed for the other category of more general systems. The global exponential stability of the closed-loop systems is proved. Furthermore, the result on the truncated predictor feedback control law includes many existing results on LTV systems subject to bounded input delays and linear time-invariant (LTI) systems subject to unbounded input delays as special cases. Finally, the results of simulations validate the effectiveness of the developed control laws.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"54 12","pages":"7580-7591"},"PeriodicalIF":8.6,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672053","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}
Zero-knowledge proof systems based on Feige-Fiat–Shamir (FFS) protocol are an interactive protocol between two anonymous authentication parties. However, they require heavy computations because of many iterations for reducing the probability that an attacker can trick a remote server. The algorithm’s time complexity rapidly increases with the total number of the challenge values, which should be unpredictable. Hence, the FFS protocol is not suitable for practical zero-knowledge proof systems. In this study, we propose new zero-knowledge proof systems based on phase mask generation that are complex sinusoidal waveform versions of the FFS algorithm for efficient anonymous authentication in the diverse interactive systems. The proposed anonymous authentication schemes need a single iteration only, allowing for efficient uses of a random challenge mask with large bit-depth. The proposed schemes allow the verifier to verify that the prover knows the secret mask, such as binary pattern, visual image, or hologram, which are the prover’s secrets, without revealing any information about it to anyone else, including the verifier. Various numerical simulations demonstrate the proposed schemes’ feasibility and robustness.
{"title":"New Complex Sinusoidal Waveform-Based Zero-Knowledge Proof Systems for Efficient Anonymous Authentication","authors":"Youhyun Kim;Ongee Jeong;Kevin Choi;Inkyu Moon;Bahram Javidi","doi":"10.1109/TSMC.2024.3460801","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3460801","url":null,"abstract":"Zero-knowledge proof systems based on Feige-Fiat–Shamir (FFS) protocol are an interactive protocol between two anonymous authentication parties. However, they require heavy computations because of many iterations for reducing the probability that an attacker can trick a remote server. The algorithm’s time complexity rapidly increases with the total number of the challenge values, which should be unpredictable. Hence, the FFS protocol is not suitable for practical zero-knowledge proof systems. In this study, we propose new zero-knowledge proof systems based on phase mask generation that are complex sinusoidal waveform versions of the FFS algorithm for efficient anonymous authentication in the diverse interactive systems. The proposed anonymous authentication schemes need a single iteration only, allowing for efficient uses of a random challenge mask with large bit-depth. The proposed schemes allow the verifier to verify that the prover knows the secret mask, such as binary pattern, visual image, or hologram, which are the prover’s secrets, without revealing any information about it to anyone else, including the verifier. Various numerical simulations demonstrate the proposed schemes’ feasibility and robustness.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"54 12","pages":"7710-7720"},"PeriodicalIF":8.6,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672057","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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