IEEE Transactions on Systems Man Cybernetics-Systems最新文献_第2页

Latent Diffusion Model for Social Recommendation 社会推荐的潜在扩散模型

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-03-01 Epub Date: 2026-02-04 DOI: 10.1109/TSMC.2026.3657816

Qinyang He;Yihao Zhang;Kaibei Li;Xiaokang Li;Wei Zhou

{"title":"Latent Diffusion Model for Social Recommendation","authors":"Qinyang He;Yihao Zhang;Kaibei Li;Xiaokang Li;Wei Zhou","doi":"10.1109/TSMC.2026.3657816","DOIUrl":"https://doi.org/10.1109/TSMC.2026.3657816","url":null,"abstract":"Social recommendations assume that users with social networks tend to have similar pReferences and leverage the social network of users to improve personalized recommendations. However, the scarcity of interactive and social data, along with the presence of irrelevant or fake social connections, poses challenges in accurately predicting user preferences. Recent research has leveraged diffusion models to eliminate invalid social connections from the social relation graph, but this approach incurs high resource costs for large-scale item prediction. To address these issues, we propose an efficient latent space diffusion model for social recommendation named latent diffusion method for social recommendation (LDSR), which can reduce resource costs by clustering user social relationships and performing diffusion in a low-dimensional space. During the diffusion process, we inject and eliminate Gaussian noise and residuals in multiple steps, enhancing the model’s ability to recognize noise while ensuring output diversity and determinism. Additionally, we design a reconstruction strategy to capture latent social relationships, which helps to densify the social relation graph. The nonsmooth nature of the latent space can disrupt downstream task outputs, so we introduce variation constraints to smooth the latent space, reducing the impact of latent perturbations during generation. Furthermore, we incorporate user-item collaborative information to guide the reverse process, enhancing the controllability of the generated content to provide reasonable denoising. Extensive experiments on four publicly available datasets demonstrate that LDSR outperforms the state-of-the-art models, exhibiting superior training efficiency, robustness against sparsity and noise, and enhanced interpretability.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 5","pages":"3355-3369"},"PeriodicalIF":8.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147685350","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}

引用次数: 0

A Knowledge-Data Integrated Graph Convolutional Network for Fault Diagnosis in Industrial Processes 面向工业过程故障诊断的知识数据集成图卷积网络

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-03-01 Epub Date: 2026-01-29 DOI: 10.1109/TSMC.2026.3655034

Yan-Lin He;Zi-Yang Lu;Yuan Xu;Qun-Xiong Zhu;Xin Pan

引用次数: 0

IEEE Systems, Man, and Cybernetics Society Information IEEE系统、人与控制论学会信息

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-03-01 Epub Date: 2026-04-16 DOI: 10.1109/TSMC.2026.3679006

引用次数: 0

Time-Varying Aerodynamic Model and Adaptive Control of the High Angle-of-Attack Maneuvering Flight for Fixed-Wing AAVs 固定翼无人飞行器大攻角机动飞行时变气动模型及自适应控制

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-03-01 Epub Date: 2026-03-05 DOI: 10.1109/TSMC.2026.3657656

Yufan Peng;Su Cao;Xiangke Wang;Huangchao Yu

{"title":"Time-Varying Aerodynamic Model and Adaptive Control of the High Angle-of-Attack Maneuvering Flight for Fixed-Wing AAVs","authors":"Yufan Peng;Su Cao;Xiangke Wang;Huangchao Yu","doi":"10.1109/TSMC.2026.3657656","DOIUrl":"https://doi.org/10.1109/TSMC.2026.3657656","url":null,"abstract":"To enhance the autonomous maneuverability of aircraft, it is urgent to develop high angle-of-attack (AOA) maneuvering flight methods suitable for fixed-wing autonomous aerial vehicles (AAVs). Addressing the issue that most existing methods neglect the nonlinearity of the flow field under high AOA conditions, this article considers the hysteresis loop phenomenon that represents the unsteady aerodynamics due to stall. Inspired by the correlation between aerodynamic coefficients and the change rate of the AOA, a time-varying aerodynamic model is established. It utilizes time-varying parameter equations to describe the aerodynamic coefficients and combines them with the fixed-wing aircraft’s longitudinal dynamics model. To address the uncertainties arising from the established model, a Lyapunov-based adaptive maneuvering controller is developed for the nonlinear time-varying system. With the prior knowledge that aerodynamic coefficients are bounded, a projection operator based on convex set theory is designed to achieve real-time bounded estimation of the unknown time-varying parameters. Additionally, the designed switching cascaded control strategy enables the aircraft to restore steady flight autonomously. The comparing simulations under parameter perturbation demonstrate that the proposed controller can effectively enhance the AAV’s tracking performance for high AOA maneuver commands.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 5","pages":"3261-3271"},"PeriodicalIF":8.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147685466","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}

引用次数: 0

IEEE Transactions on Systems, Man, and Cybernetics: Systems Information for Authors IEEE系统、人与控制论汇刊：作者的系统信息

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-03-01 Epub Date: 2026-04-16 DOI: 10.1109/TSMC.2026.3678691

引用次数: 0

Distributed Flexible Job Shop Scheduling With Heterogeneous Transportation Resources Constraints via Deep Reinforcement Learning and Graph Neural Network 基于深度强化学习和图神经网络的异构运输资源约束下的分布式柔性作业车间调度

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-03-01 Epub Date: 2026-01-29 DOI: 10.1109/TSMC.2026.3656196

Kaikai Zhu;Xiaobin Li;Pei Jiang;Min Cheng;Yuanqing Wu;Kaizhou Gao;Lei Ren

{"title":"Distributed Flexible Job Shop Scheduling With Heterogeneous Transportation Resources Constraints via Deep Reinforcement Learning and Graph Neural Network","authors":"Kaikai Zhu;Xiaobin Li;Pei Jiang;Min Cheng;Yuanqing Wu;Kaizhou Gao;Lei Ren","doi":"10.1109/TSMC.2026.3656196","DOIUrl":"https://doi.org/10.1109/TSMC.2026.3656196","url":null,"abstract":"The distributed flexible job shop scheduling problem (DFJSP) has emerged as a critical challenge in the field of scheduling optimization due to its intricate resource allocation and the demand for production–logistics collaboration across multiple factories. However, most existing studies related to DFJSP only focus on the production and transportation process of jobs within a single factory, while neglecting the cross-factory logistics and the heterogeneous characteristics of transportation resources. Therefore, this article first investigates the distributed flexible job shop scheduling problem with heterogeneous transportation (DFJSPHT) resource constraints and proposes an end-to-end deep reinforcement learning (DRL) scheduling method to minimize the makespan. An innovative heterogeneous disjunctive graph model is constructed to uniformly represent the states of factories, machines, operations, and transportation resources in DFJSPHT, and the scheduling process is modeled as a Markov decision process (MDP). Next, a resource release strategy is developed to enhance the efficiency of transportation resources. To enhance the feature expression ability of the model, a graph neural network (GNN) is employed to capture the problem characteristics, and the policy network is trained using the proximal policy optimization. Comparative experiments are conducted on synthetic and benchmark instances demonstrate that the proposed method outperforms the classical priority scheduling rules and two popular DRL-based scheduling methods in solving DFJSPHT, with performance improvements exceeding 10% in most instances.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 5","pages":"3086-3098"},"PeriodicalIF":8.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147685521","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}

引用次数: 0

A Dual-Surrogate Competition-Assisted Evolutionary Algorithm With Triggered Constraint First Search for Expensive Constrained Optimization 基于触发约束优先搜索的双代理竞争辅助进化算法求解昂贵约束优化

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-03-01 Epub Date: 2026-02-03 DOI: 10.1109/TSMC.2026.3657034

Kunjie Yu;Yuhan Chai;Fan Chen;Ke Chen;Rui Nie

{"title":"A Dual-Surrogate Competition-Assisted Evolutionary Algorithm With Triggered Constraint First Search for Expensive Constrained Optimization","authors":"Kunjie Yu;Yuhan Chai;Fan Chen;Ke Chen;Rui Nie","doi":"10.1109/TSMC.2026.3657034","DOIUrl":"https://doi.org/10.1109/TSMC.2026.3657034","url":null,"abstract":"Expensive constrained optimization problems (ECOPs), which frequently arise in real-world engineering optimization, are often limited by the number of evaluations. Using surrogate-assisted evolutionary algorithms (EAs) to reduce computational costs is a common approach. However, surrogate model errors are inevitable and often mislead the search direction of EAs. Most existing algorithms overlook the inevitability of such errors and attempt to minimize them through techniques like data selection, which might be ineffective for problems with highly complex constraint and objective functions. Therefore, we propose a dual-surrogate competition assisted EA with triggered constraint-first search (DC-TCFS) for ECOPs, aiming to reduce the misleading effects of surrogate models on the evolutionary process. In this study, two surrogate models are used to assist local searches through competition, effectively mitigating the impact of errors from a single surrogate model. A triggered constraint-first search method is proposed to quickly identify a feasible solution for problems with complex constraints. Additionally, an adaptive sampling criterion is designed to guide the algorithm toward solutions that are more beneficial to the evolutionary process. Experimental results demonstrate that the proposed algorithm outperforms state-of-the-art methods across various benchmark problems, real-world problems and a motor design optimization problem, highlighting its effectiveness in solving ECOPs.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 5","pages":"3035-3048"},"PeriodicalIF":8.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147685538","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}

引用次数: 0

IEEE Systems, Man, and Cybernetics Society Information IEEE系统、人与控制论学会信息

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-03-01 Epub Date: 2026-04-16 DOI: 10.1109/TSMC.2026.3678689

引用次数: 0

Fuzzy Logic Systems-Based Reinforcement Learning for Optimal Tracking Control of Multiagent Systems 基于模糊逻辑系统的强化学习多智能体系统最优跟踪控制

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-03-01 Epub Date: 2026-02-06 DOI: 10.1109/TSMC.2026.3655082

Xiaoyang Liu;Lu Fan;Sikai Shen;Wenwu Yu;Song Zhu

引用次数: 0

Prescribed-Time Consensus Tracking of Multiagent Systems Under Denial-of-Service Attacks With Application to Unmanned Vehicles 拒绝服务攻击下多智能体系统的约定时间一致性跟踪及其在无人驾驶车辆中的应用

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-03-01 Epub Date: 2026-01-30 DOI: 10.1109/TSMC.2026.3655087

Huanyu Yang;Yujuan Wang;Kun Jiang;Yew-Soon Ong;Yongduan Song

{"title":"Prescribed-Time Consensus Tracking of Multiagent Systems Under Denial-of-Service Attacks With Application to Unmanned Vehicles","authors":"Huanyu Yang;Yujuan Wang;Kun Jiang;Yew-Soon Ong;Yongduan Song","doi":"10.1109/TSMC.2026.3655087","DOIUrl":"https://doi.org/10.1109/TSMC.2026.3655087","url":null,"abstract":"This work addresses the prescribed-time consensus tracking problem for multiagent systems (MASs) under denial-of-service (DoS) attacks, where certain communication links between agents are intermittently disrupted or rendered unavailable. Due to the DoS attack, although appropriate defense mechanisms may be employed to recover some of the attacked or backup connections, the communication topology evolves from a fixed graph to a time-varying switching topology. This ever-changing network introduces significant challenges, leading to discontinuities in the Laplacian matrix, which complicates both prescribed-time controller design and stability analysis. To tackle these challenges, a prescribed-time stability lemma (Lemma 5) is developed along with a vital inequality (Lemma 6) that establishes the quantitative relationship between Lyapunov functions across switching instants. Building on these results, a novel distributed observer is designed to accurately estimate the state of the leader within the prescribed time, despite the occurrence of DoS attacks. Subsequently, a coordinate transformation and a fractional-power backstepping technique are introduced to construct a control protocol that achieves prescribed-time consensus tracking. The proposed approach is rigorously supported by theoretical analysis and is further validated through its application to multiple unmanned vehicles.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 5","pages":"3075-3085"},"PeriodicalIF":8.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147685302","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}

引用次数: 0