Pub Date : 2025-11-19DOI: 10.1109/TSMC.2025.3627725
{"title":"IEEE Systems, Man, and Cybernetics Society Information","authors":"","doi":"10.1109/TSMC.2025.3627725","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3627725","url":null,"abstract":"","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 12","pages":"C3-C3"},"PeriodicalIF":8.7,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11260918","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145546966","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 : 2025-11-19DOI: 10.1109/TSMC.2025.3627737
{"title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems Information for Authors","authors":"","doi":"10.1109/TSMC.2025.3627737","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3627737","url":null,"abstract":"","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 12","pages":"C4-C4"},"PeriodicalIF":8.7,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11260920","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145546969","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 : 2025-11-14DOI: 10.1109/TSMC.2025.3630653
Tengbo Li;Huorong Ren;Yihui Hu;Xu Lu;Zhiwu Li
This study tackles the challenge of robust fault diagnosis in discrete event systems (DESs) that experience permanent observation losses using labeled Petri nets (LPNs). We consider the scenario that the initially observable transitions may become unobservable before their firings. Especially, the case that some, instead of all, of the transitions with a shared label may become unobservable is also taken into account. In such a scenario, the diagnosers in the existing methods may not report correct diagnostic results. This article presents a novel notion to ensure robust diagnosability for LPNs, aimed at overcoming the issue of permanent observation loss. To avert enumerating all the reachable markings, a structure called a tagged basis reachability graph (t-BRG) is developed, based on which all subsets of observable transitions, called diagnosis transition sets (DTSs), that ensure the diagnosability of the plant independently are calculated. Then, a special class of verifiers to assess the robust diagnosability of a system experiencing permanent observation loss is developed. Finally, an online diagnosis method performed by a set of diagnosers is presented and demonstrated by examples.
{"title":"Robust Fault Diagnosis Against Permanent Loss of Observations Using Labeled Petri Nets","authors":"Tengbo Li;Huorong Ren;Yihui Hu;Xu Lu;Zhiwu Li","doi":"10.1109/TSMC.2025.3630653","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3630653","url":null,"abstract":"This study tackles the challenge of robust fault diagnosis in discrete event systems (DESs) that experience permanent observation losses using labeled Petri nets (LPNs). We consider the scenario that the initially observable transitions may become unobservable before their firings. Especially, the case that some, instead of all, of the transitions with a shared label may become unobservable is also taken into account. In such a scenario, the diagnosers in the existing methods may not report correct diagnostic results. This article presents a novel notion to ensure robust diagnosability for LPNs, aimed at overcoming the issue of permanent observation loss. To avert enumerating all the reachable markings, a structure called a tagged basis reachability graph (t-BRG) is developed, based on which all subsets of observable transitions, called diagnosis transition sets (DTSs), that ensure the diagnosability of the plant independently are calculated. Then, a special class of verifiers to assess the robust diagnosability of a system experiencing permanent observation loss is developed. Finally, an online diagnosis method performed by a set of diagnosers is presented and demonstrated by examples.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 1","pages":"737-750"},"PeriodicalIF":8.7,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145760915","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 study investigates a scheduling problem involving dual-arm cluster tools (CTs) that simultaneously handle two types of wafers, considering both wafer priority and residency time constraints. The two types of wafers have their own processing routes and processing times at each step. To fully utilize the resources of the CTs, we use the fewest processing modules (PMs) to produce one type of wafers with maximum productivity, and use the available PMs to produce the other type of wafers. Based on this, we introduce a swap sequence for scheduling a dual-arm robot, which is simple to implement and supports periodic operations. Without affecting the priority wafer production, we provide the necessary and sufficient conditions for scheduling a CT that processes two types of wafers, and present the optimal PM configuration. A high-performance algorithm is developed to determine an optimal periodic schedule, with its practicality and feasibility illustrated through several examples.
{"title":"A Periodic Scheduling Method for Dual-Arm Cluster Tools Considering Wafer Priority and Residency Time Constraint","authors":"Jufeng Wang;Chunfeng Liu;MengChu Zhou;Abdullah Abusorrah","doi":"10.1109/TSMC.2025.3629134","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3629134","url":null,"abstract":"This study investigates a scheduling problem involving dual-arm cluster tools (CTs) that simultaneously handle two types of wafers, considering both wafer priority and residency time constraints. The two types of wafers have their own processing routes and processing times at each step. To fully utilize the resources of the CTs, we use the fewest processing modules (PMs) to produce one type of wafers with maximum productivity, and use the available PMs to produce the other type of wafers. Based on this, we introduce a swap sequence for scheduling a dual-arm robot, which is simple to implement and supports periodic operations. Without affecting the priority wafer production, we provide the necessary and sufficient conditions for scheduling a CT that processes two types of wafers, and present the optimal PM configuration. A high-performance algorithm is developed to determine an optimal periodic schedule, with its practicality and feasibility illustrated through several examples.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 1","pages":"779-788"},"PeriodicalIF":8.7,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145760889","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 : 2025-11-14DOI: 10.1109/TSMC.2025.3629555
Guojie Li;Ziwei Fan;Zhiwen Yu;Kaixiang Yang;C. L. Philip Chen
Due to its exceptional feature representation capabilities and high computational efficiency, the broad learning system (BLS) has been widely employed in various classification tasks. Nevertheless, BLS encounters considerable challenges in semi-supervised classification tasks involving complex heterogeneous data, given the data’s high-dimensional and noisy nature, coupled with a limited number of available labeled samples. To tackle these challenges, this article introduces a semi-supervised BLS based on distance constraint regularization (DRBLS) and a semi-supervised broad ensemble method (E-DRBLS) for high-dimensional data. Specifically, we present a distance constraint regularization (DR) that utilizes both labeled and unlabeled data to derive an optimal projection matrix, which maximizes the preservation of the original data’s intrinsic distribution structure. DR is designed to minimize intraclass distance, maximize interclass distance, and minimize the distance between neighboring samples. To boost the performance of BLS in semi-supervised classification, we integrate DR and BLS to construct the semi-supervised classifier DRBLS. Finally, we propose a mixed dimensionality reduction space generation (MDRSG) method that generates multiple high-quality and diverse mixed dimensionality reduction spaces (MDRSs). Based on MDRS, an ensemble framework, E-DRBLS, is developed for semi-supervised classification tasks targeting high-dimensional data. Comprehensive experiments confirm the superiority of the proposed methods.
{"title":"Semi-Supervised Ensemble Classifier Based on Distance Constraint for High-Dimensional Data","authors":"Guojie Li;Ziwei Fan;Zhiwen Yu;Kaixiang Yang;C. L. Philip Chen","doi":"10.1109/TSMC.2025.3629555","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3629555","url":null,"abstract":"Due to its exceptional feature representation capabilities and high computational efficiency, the broad learning system (BLS) has been widely employed in various classification tasks. Nevertheless, BLS encounters considerable challenges in semi-supervised classification tasks involving complex heterogeneous data, given the data’s high-dimensional and noisy nature, coupled with a limited number of available labeled samples. To tackle these challenges, this article introduces a semi-supervised BLS based on distance constraint regularization (DRBLS) and a semi-supervised broad ensemble method (E-DRBLS) for high-dimensional data. Specifically, we present a distance constraint regularization (DR) that utilizes both labeled and unlabeled data to derive an optimal projection matrix, which maximizes the preservation of the original data’s intrinsic distribution structure. DR is designed to minimize intraclass distance, maximize interclass distance, and minimize the distance between neighboring samples. To boost the performance of BLS in semi-supervised classification, we integrate DR and BLS to construct the semi-supervised classifier DRBLS. Finally, we propose a mixed dimensionality reduction space generation (MDRSG) method that generates multiple high-quality and diverse mixed dimensionality reduction spaces (MDRSs). Based on MDRS, an ensemble framework, E-DRBLS, is developed for semi-supervised classification tasks targeting high-dimensional data. Comprehensive experiments confirm the superiority of the proposed methods.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 1","pages":"724-736"},"PeriodicalIF":8.7,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778221","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 : 2025-11-11DOI: 10.1109/TSMC.2025.3628874
Wei Song;Mingshuo Song;Haojie Zhou;Xiaoyan Sun;Yaochu Jin;Songbai Liu;Qiuzhen Lin;Shengxiang Yang
Recently, machine learning-embedded large-scale multiobjective evolutionary algorithms (LMOEAs) have shown great promise in solving large-scale multiobjective optimization problems (LMOPs). However, the fast convergence of the population to the true Pareto-optimal front (POF) and even distribution of the obtained Pareto-optimal solutions (POSs) on the POF are not adequately considered when tackling an LMOP. Besides, existing LMOEAs typically pair solutions with a matching rule and employ a network to learn the evolution pattern among the obtained solution pairs. It is difficult to learn various evolution patterns through a simple network, which hinders the collaboration of different patterns for enhancing the search capability. Facing such difficulties, this article proposes an LMOEA with multipattern learning and collaboration (LMOEA-MLC), where a single-hidden-layer multioutput network (SMN) is established to learn inductive and hybrid evolution patterns. Specifically, two inductive ones can be learned with the solution pairs built by two matching rules toward fast convergence and even distribution, respectively. Moreover, the solution pairs considering the fusion of the two inductive ones are collected, enabling SMN to learn a hybrid one and thus making a tradeoff between fast convergence and even distribution. Besides, the learned evolution patterns collaborate to enhance the search capability due to the distinct patterns. To enhance learning speed, SMN’s parameters are updated by an incremental random vector functional link (IRVFL). In our experiments, comprehensive comparisons with eight state-of-the-art LMOEAs demonstrate the significant performance improvement of LMOEA-MLC in handling LMOPs.
{"title":"Multipattern Learning and Collaboration-Based Evolutionary Optimizer for Large-Scale Multiobjective Optimization","authors":"Wei Song;Mingshuo Song;Haojie Zhou;Xiaoyan Sun;Yaochu Jin;Songbai Liu;Qiuzhen Lin;Shengxiang Yang","doi":"10.1109/TSMC.2025.3628874","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3628874","url":null,"abstract":"Recently, machine learning-embedded large-scale multiobjective evolutionary algorithms (LMOEAs) have shown great promise in solving large-scale multiobjective optimization problems (LMOPs). However, the fast convergence of the population to the true Pareto-optimal front (POF) and even distribution of the obtained Pareto-optimal solutions (POSs) on the POF are not adequately considered when tackling an LMOP. Besides, existing LMOEAs typically pair solutions with a matching rule and employ a network to learn the evolution pattern among the obtained solution pairs. It is difficult to learn various evolution patterns through a simple network, which hinders the collaboration of different patterns for enhancing the search capability. Facing such difficulties, this article proposes an LMOEA with multipattern learning and collaboration (LMOEA-MLC), where a single-hidden-layer multioutput network (SMN) is established to learn inductive and hybrid evolution patterns. Specifically, two inductive ones can be learned with the solution pairs built by two matching rules toward fast convergence and even distribution, respectively. Moreover, the solution pairs considering the fusion of the two inductive ones are collected, enabling SMN to learn a hybrid one and thus making a tradeoff between fast convergence and even distribution. Besides, the learned evolution patterns collaborate to enhance the search capability due to the distinct patterns. To enhance learning speed, SMN’s parameters are updated by an incremental random vector functional link (IRVFL). In our experiments, comprehensive comparisons with eight state-of-the-art LMOEAs demonstrate the significant performance improvement of LMOEA-MLC in handling LMOPs.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 1","pages":"751-765"},"PeriodicalIF":8.7,"publicationDate":"2025-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145760907","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 : 2025-11-11DOI: 10.1109/TSMC.2025.3628968
Shuo-Qiu Zhang;Wei-Wei Che;Zheng-Guang Wu
For unknown continuous-time heterogeneous linear multiagent systems (MASs) under mixed denial-of-service (DoS) attacks, a novel reinforcement learning (RL) algorithm named hybrid iterative (HI) is proposed in this article to solve the secure output tracking problem based on a prescribed-time observer. Considering the scenario that MASs are subjected to mixed DoS attacks that can cause the connectivity maintained or broken of the network communication topology, a distributed resilient prescribed-time observer is designed to accurately estimate the leader’s state and output within a prescribed time. Then, the secure output tracking problem of heterogeneous MASs is converted into the optimal linear quadratic tracking (LQT) problem by introducing a discounted performance function, and inhomogeneous algebraic Riccati equations (AREs) are further derived to solve it. Meanwhile, an HI-based data-driven RL algorithm independent of the initial admissible control policy and the system dynamics knowledge is proposed to learn the optimal solution of inhomogeneous AREs. Compared with the traditional RL algorithms, that is, policy iteration (PI) and value iteration (VI), HI can not only remove the restrictions of the initial admissible policy in PI but also converge to the optimal solution faster than the VI. Finally, comparative simulation verifies the effectiveness of the theoretical results.
{"title":"Prescribed-Time Observer-Based HI-RL Secure Output Tracking Control for Heterogeneous MASs Under DoS Attacks","authors":"Shuo-Qiu Zhang;Wei-Wei Che;Zheng-Guang Wu","doi":"10.1109/TSMC.2025.3628968","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3628968","url":null,"abstract":"For unknown continuous-time heterogeneous linear multiagent systems (MASs) under mixed denial-of-service (DoS) attacks, a novel reinforcement learning (RL) algorithm named hybrid iterative (HI) is proposed in this article to solve the secure output tracking problem based on a prescribed-time observer. Considering the scenario that MASs are subjected to mixed DoS attacks that can cause the connectivity maintained or broken of the network communication topology, a distributed resilient prescribed-time observer is designed to accurately estimate the leader’s state and output within a prescribed time. Then, the secure output tracking problem of heterogeneous MASs is converted into the optimal linear quadratic tracking (LQT) problem by introducing a discounted performance function, and inhomogeneous algebraic Riccati equations (AREs) are further derived to solve it. Meanwhile, an HI-based data-driven RL algorithm independent of the initial admissible control policy and the system dynamics knowledge is proposed to learn the optimal solution of inhomogeneous AREs. Compared with the traditional RL algorithms, that is, policy iteration (PI) and value iteration (VI), HI can not only remove the restrictions of the initial admissible policy in PI but also converge to the optimal solution faster than the VI. Finally, comparative simulation verifies the effectiveness of the theoretical results.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 1","pages":"709-723"},"PeriodicalIF":8.7,"publicationDate":"2025-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778260","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 : 2025-11-03DOI: 10.1109/TSMC.2025.3624888
Yao Huang;Yinan Guo;Hong Wei;Jianbin Xin;Shengxiang Yang
Flexible job shop co-scheduling problems (FJSCSPs) normally adopt single-load automated guided vehicles (AGVs) for transportation, possibly causing the waste of load capacity. To enhance the transportation efficiency, a multiload AGVs (MAGVs) that carry more than one job simultaneously within its load capacity come into use in flexible manufacturing systems (FMSs). In this scenario, transit throughput can achieve the obvious improvement without increasing the vehicle fleet size, having become more prevalent gradually. However, co-scheduling machines and MAGVs is seldom investigated, which is crucial for maximizing production efficiency due to the inherent interdependence between transporting and processing. Considering constraints on load capacity, task assignment, and transportation sequence, this co-scheduling problem is formulated by minimizing the makespan as an optimization objective. Subsequently, a collaboration–competition estimation of distribution algorithm (CCEDA) is put forward to solve the difficulties caused by the flexible sequence for pickup and delivery tasks of MAGVs. In particular, two problem-related heuristic rules for selecting AGVs and machines are designed, and then a hybrid initialization strategy is developed to produce high-quality initial individuals. To comprehensively describe the landscape of the problem, multiple probability models are established by learning the elite solutions, and then a collaboration–competition mechanism adaptively samples using different models to maintain the high-efficiency exploration. Furthermore, a local search based on variable neighborhood is introduced to enhance the exploitation in promising regions. The experimental results on 30 instances expose that the proposed algorithm outperforms the other state-of-the-art algorithms significantly. Also, the analysis on the impact of AGV load capacity on production confirms that its increase effectively reduces the makespan, thereby demonstrating the practical value of MAGVs.
{"title":"Collaboration–Competition Estimation of Distribution Algorithm for Flexible Job Shop Co-Scheduling With Multiload AGVs","authors":"Yao Huang;Yinan Guo;Hong Wei;Jianbin Xin;Shengxiang Yang","doi":"10.1109/TSMC.2025.3624888","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3624888","url":null,"abstract":"Flexible job shop co-scheduling problems (FJSCSPs) normally adopt single-load automated guided vehicles (AGVs) for transportation, possibly causing the waste of load capacity. To enhance the transportation efficiency, a multiload AGVs (MAGVs) that carry more than one job simultaneously within its load capacity come into use in flexible manufacturing systems (FMSs). In this scenario, transit throughput can achieve the obvious improvement without increasing the vehicle fleet size, having become more prevalent gradually. However, co-scheduling machines and MAGVs is seldom investigated, which is crucial for maximizing production efficiency due to the inherent interdependence between transporting and processing. Considering constraints on load capacity, task assignment, and transportation sequence, this co-scheduling problem is formulated by minimizing the makespan as an optimization objective. Subsequently, a collaboration–competition estimation of distribution algorithm (CCEDA) is put forward to solve the difficulties caused by the flexible sequence for pickup and delivery tasks of MAGVs. In particular, two problem-related heuristic rules for selecting AGVs and machines are designed, and then a hybrid initialization strategy is developed to produce high-quality initial individuals. To comprehensively describe the landscape of the problem, multiple probability models are established by learning the elite solutions, and then a collaboration–competition mechanism adaptively samples using different models to maintain the high-efficiency exploration. Furthermore, a local search based on variable neighborhood is introduced to enhance the exploitation in promising regions. The experimental results on 30 instances expose that the proposed algorithm outperforms the other state-of-the-art algorithms significantly. Also, the analysis on the impact of AGV load capacity on production confirms that its increase effectively reduces the makespan, thereby demonstrating the practical value of MAGVs.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 1","pages":"321-335"},"PeriodicalIF":8.7,"publicationDate":"2025-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145760901","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}
Forecasting the power consumption of the spacecraft is critical for optimizing its lifespan and task allocation. However, the complex electromagnetic environment of outer space introduces unavoidable noise into the collected electrical signals. Moreover, the various subsystems of a multipower spacecraft are affected differently by internal and external noise, making it challenging for the existing methods to effectively capture the features of long-term power consumption sequences. We propose adaptive frequency-pruning-enhanced (AFPE)-iTransformer, a robust time-series forecasting model designed for spacecraft telemetry forecasting under noise and long-range dependency conditions. The model combines three key components: Legendre memory projection for historical compression, adaptive top-$k$ frequency pruning for per-channel denoising, and an improved inverted transformer for cross-subsystem attention. Evaluated on three years of Mars Express (MEX) data, our method consistently outperforms the state-of-the-art baselines in both within-year and cross-year forecasting. It also achieves competitive efficiency, with fast model load time and moderate parameter size. While focused on power forecasting, the model’s modular design supports broader applications in telemetry and industrial forecasting. Model code and configurations are open-sourced for reproducibility.
{"title":"Power Consumption Forecasting of Spacecraft Based on Adaptive Frequency-Domain Pruning-Enhanced Transformer","authors":"Joey Chan;Shiyuan Piao;Huan Wang;Zhen Chen;Ershun Pan;Fugee Tsung","doi":"10.1109/TSMC.2025.3624401","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3624401","url":null,"abstract":"Forecasting the power consumption of the spacecraft is critical for optimizing its lifespan and task allocation. However, the complex electromagnetic environment of outer space introduces unavoidable noise into the collected electrical signals. Moreover, the various subsystems of a multipower spacecraft are affected differently by internal and external noise, making it challenging for the existing methods to effectively capture the features of long-term power consumption sequences. We propose adaptive frequency-pruning-enhanced (AFPE)-iTransformer, a robust time-series forecasting model designed for spacecraft telemetry forecasting under noise and long-range dependency conditions. The model combines three key components: Legendre memory projection for historical compression, adaptive top-<inline-formula> <tex-math>$k$ </tex-math></inline-formula> frequency pruning for per-channel denoising, and an improved inverted transformer for cross-subsystem attention. Evaluated on three years of Mars Express (MEX) data, our method consistently outperforms the state-of-the-art baselines in both within-year and cross-year forecasting. It also achieves competitive efficiency, with fast model load time and moderate parameter size. While focused on power forecasting, the model’s modular design supports broader applications in telemetry and industrial forecasting. Model code and configurations are open-sourced for reproducibility.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 1","pages":"336-349"},"PeriodicalIF":8.7,"publicationDate":"2025-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145760923","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}
In order to improve the environmental adaptation and safety of autonomous vehicles trajectory planning process in a complex driving environment, a novel trajectory planning method which meets the requirement of multidriving tasks and adapts to various driving conditions is proposed in this article. In the trajectory planning method, the optimal control problem considering multiple driving tasks is established based on the constructed performance function and constraint analysis of different driving tasks to ensure the accurate realization of driving tasks. Besides, the neural network empirical model, precollision detection model, and trajectory evaluation model are designed by the consideration of selecting the optimal planning parameters in different driving conditions to enhance the adaptability to traffic environment. The advantage of the proposed method is that it not only meets the requirements of a variety of driving tasks, but also able to select the optimal planning parameters according to different traffic conditions while existing methods usually only meet single planning task, such as lane change, and has the fixed and rigid parameter selection. Four different typical scenarios are given to verify the effectiveness of the proposed method and the results show that the proposed trajectory planning method is able to ensure the safety of the vehicle and adapt to different traffic environments flexibly.
{"title":"An Adaptive Trajectory Planning Method of Autonomous Vehicles Integrating Multiple Tasks","authors":"Haiyan Zhao;Hongbin Xie;Bingzhao Gao;Xinghao Lu;Hong Chen","doi":"10.1109/TSMC.2025.3624625","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3624625","url":null,"abstract":"In order to improve the environmental adaptation and safety of autonomous vehicles trajectory planning process in a complex driving environment, a novel trajectory planning method which meets the requirement of multidriving tasks and adapts to various driving conditions is proposed in this article. In the trajectory planning method, the optimal control problem considering multiple driving tasks is established based on the constructed performance function and constraint analysis of different driving tasks to ensure the accurate realization of driving tasks. Besides, the neural network empirical model, precollision detection model, and trajectory evaluation model are designed by the consideration of selecting the optimal planning parameters in different driving conditions to enhance the adaptability to traffic environment. The advantage of the proposed method is that it not only meets the requirements of a variety of driving tasks, but also able to select the optimal planning parameters according to different traffic conditions while existing methods usually only meet single planning task, such as lane change, and has the fixed and rigid parameter selection. Four different typical scenarios are given to verify the effectiveness of the proposed method and the results show that the proposed trajectory planning method is able to ensure the safety of the vehicle and adapt to different traffic environments flexibly.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 1","pages":"350-361"},"PeriodicalIF":8.7,"publicationDate":"2025-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145760881","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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