Ieee-Caa Journal of Automatica Sinica最新文献_第7页

MEET: A Million-Scale Dataset for Fine-Grained Geospatial Scene Classification With Zoom-Free Remote Sensing Imagery 基于无变焦遥感图像的百万尺度细粒度地理空间场景分类数据集

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-03-15 DOI: 10.1109/JAS.2025.125324

Yansheng Li;Yuning Wu;Gong Cheng;Chao Tao;Bo Dang;Yu Wang;Jiahao Zhang;Chuge Zhang;Yiting Liu;Xu Tang;Jiayi Ma;Yongjun Zhang

{"title":"MEET: A Million-Scale Dataset for Fine-Grained Geospatial Scene Classification With Zoom-Free Remote Sensing Imagery","authors":"Yansheng Li;Yuning Wu;Gong Cheng;Chao Tao;Bo Dang;Yu Wang;Jiahao Zhang;Chuge Zhang;Yiting Liu;Xu Tang;Jiayi Ma;Yongjun Zhang","doi":"10.1109/JAS.2025.125324","DOIUrl":"https://doi.org/10.1109/JAS.2025.125324","url":null,"abstract":"Accurate fine-grained geospatial scene classification using remote sensing imagery is essential for a wide range of applications. However, existing approaches often rely on manually zooming remote sensing images at different scales to create typical scene samples. This approach fails to adequately support the fixed-resolution image interpretation requirements in real-world scenarios. To address this limitation, we introduce the million-scale fine-grained geospatial scene classification dataset (MEET), which contains over 1.03 million zoom-free remote sensing scene samples, manually annotated into 80 fine-grained categories. In MEET, each scene sample follows a scene-in-scene layout, where the central scene serves as the reference, and auxiliary scenes provide crucial spatial context for fine-grained classification. Moreover, to tackle the emerging challenge of scene-in-scene classification, we present the context-aware transformer (CAT), a model specifically designed for this task, which adaptively fuses spatial context to accurately classify the scene samples. CAT adaptively fuses spatial context to accurately classify the scene samples by learning attentional features that capture the relationships between the center and auxiliary scenes. Based on MEET, we establish a comprehensive benchmark for fine-grained geospatial scene classification, evaluating CAT against 11 competitive baselines. The results demonstrate that CAT significantly outperforms these baselines, achieving a 1.88% higher balanced accuracy (BA) with the Swin-Large backbone, and a notable 7.87% improvement with the Swin-Huge backbone. Further experiments validate the effectiveness of each module in CAT and show the practical applicability of CAT in the urban functional zone mapping. The source code and dataset will be publicly available at https://jerrywyn.github.io/project/MEET.html.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 5","pages":"1004-1023"},"PeriodicalIF":15.3,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072848","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

In Memory of Wolter J. Fabrycky: A Pioneer of Systems Engineering and US-Sino Academic Exchange 纪念法布赖基：系统工程与中美学术交流的先驱

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-03-15 DOI: 10.1109/JAS.2025.125468

Fei-Yue Wang

引用次数: 0

Cooperation Under Stochastic Punishment in Social Dilemma Situations 社会困境下随机惩罚下的合作

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-03-15 DOI: 10.1109/JAS.2023.123912

Shiping Gao;Jinghui Suo;Nan Li

{"title":"Cooperation Under Stochastic Punishment in Social Dilemma Situations","authors":"Shiping Gao;Jinghui Suo;Nan Li","doi":"10.1109/JAS.2023.123912","DOIUrl":"https://doi.org/10.1109/JAS.2023.123912","url":null,"abstract":"Dear Editor, This letter is concerned with the evolutionary dynamics of cooperative strategies in social dilemma situations. Stochastic punishment has been proposed, in which whether an individual acts as a punisher or not is stochastic and depends on its preference for punishment. Meanwhile, both the cost of punishment and whether a defector would be punished are also stochastic. In previous models, the cost of punishment is considered to be either constant or proportional to the number of individuals to be punished. Furthermore, the hypothesis that all defectors should be penalized is frequently adopted. Actually, some defectors may refrain from being punished due to the presence of noise or the limitation of the punishment cost, and the cost of punishment is also dependent on the number of punishers. Thus, we establish an analytic model of stochastic punishment for infinite and well-mixed populations, investigate the effects of stochastic punishment on the evolution of cooperation, and analyze how to support the evolution of cooperation effectively when the stochastic punishment is possible. The objective of this letter is to design a cooperation-promoting stochastic punishment that will allow the system to evolve to full cooperation. The replicator equations have been used to explore the evolutionary dynamics of cooperation under stochastic punishment, and the conditions under which cooperation is favored by natural selection have been specified.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 5","pages":"1050-1052"},"PeriodicalIF":15.3,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11005754","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073010","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}

引用次数: 0

Data-Driven Two-Stage Robust Optimization Allocation and Loading for Salt Lake Chemical Enterprise Products Under Demand Uncertainty 需求不确定性下盐湖化工企业产品数据驱动的两阶段鲁棒优化配置与装载

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-03-15 DOI: 10.1109/JAS.2025.125204

Yiyin Tang;Yalin Wang;Chenliang Liu;Qingkai Sui;Yishun Liu;Keke Huang;Weihua Gui

{"title":"Data-Driven Two-Stage Robust Optimization Allocation and Loading for Salt Lake Chemical Enterprise Products Under Demand Uncertainty","authors":"Yiyin Tang;Yalin Wang;Chenliang Liu;Qingkai Sui;Yishun Liu;Keke Huang;Weihua Gui","doi":"10.1109/JAS.2025.125204","DOIUrl":"https://doi.org/10.1109/JAS.2025.125204","url":null,"abstract":"Most enterprises rely on railway transportation to deliver their products to customers, particularly in the salt lake chemical industry. Notably, allocating products to freight spaces and their assembly on transport vehicles are critical pre-transportation processes. However, due to demand fluctuations from changing product orders and unforeseen railway scheduling delays, manually adjusted allocation and loading may lead to excessive loading and unloading distances and times, ultimately increasing transportation costs for enterprises. To address these issues, this paper proposes a data-driven two-stage robust optimization (TSRO) framework embedding with the gated stacked temporal autoencoder clustering based on the attention mechanism (GSTAC-AM), which aims to overcome demand uncertainty and enhance the efficiency of freight allocation and loading. Specifically, GSTAC-AM is developed to help predict the deviation level of demand uncertainty and mitigate the impact of potential outliers. Then, a robust counterpart model is formulated to ensure computational tractability. In addition, a multi-stage hybrid heuristic algorithm is designed to handle the large scale and complexity inherent in the freight space allocation and loading processes. Finally, the effectiveness and applicability of the proposed framework are validated through a real case study conducted in a large salt lake chemical enterprise.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 5","pages":"989-1003"},"PeriodicalIF":15.3,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073014","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

Evolutionary Algorithm Based on Surrogate and Inverse Surrogate Models for Expensive Multiobjective Optimization 基于代理和逆代理模型的昂贵多目标优化进化算法

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-03-15 DOI: 10.1109/JAS.2025.125111

Qi Deng;Qi Kang;MengChu Zhou;Xiaoling Wang;Shibing Zhao;Siqi Wu;Mohammadhossein Ghahramani

{"title":"Evolutionary Algorithm Based on Surrogate and Inverse Surrogate Models for Expensive Multiobjective Optimization","authors":"Qi Deng;Qi Kang;MengChu Zhou;Xiaoling Wang;Shibing Zhao;Siqi Wu;Mohammadhossein Ghahramani","doi":"10.1109/JAS.2025.125111","DOIUrl":"https://doi.org/10.1109/JAS.2025.125111","url":null,"abstract":"When dealing with expensive multiobjective optimization problems, majority of existing surrogate-assisted evolutionary algorithms (SAEAs) generate solutions in decision space and screen candidate solutions mostly by using designed surrogate models. The generated solutions exhibit excessive randomness, which tends to reduce the likelihood of generating good-quality solutions and cause a long evolution to the optima. To improve SAEAs greatly, this work proposes an evolutionary algorithm based on surrogate and inverse surrogate models by 1) Employing a surrogate model in lieu of expensive (true) function evaluations; and 2) Proposing and using an inverse surrogate model to generate new solutions. By using the same training data but with its inputs and outputs being reversed, the latter is simple to train. It is then used to generate new vectors in objective space, which are mapped into decision space to obtain their corresponding solutions. Using a particular example, this work shows its advantages over existing SAEAs. The results of comparing it with state-of-the-art algorithms on expensive optimization problems show that it is highly competitive in both solution performance and efficiency.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 5","pages":"961-973"},"PeriodicalIF":15.3,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073005","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

Synchronous Membership Function Dependent Event-Triggered $boldsymbol{H}_{infty}$ Control of T-S Fuzzy Systems Under Network Communications 基于同步隶属函数的事件触发$boldsymbol{H}_{infty}$网络通信条件下T-S模糊系统控制

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-03-15 DOI: 10.1109/JAS.2023.123729

Bo-Lin Xu;Chen Peng;Wen-Bo Xie

引用次数: 0

Full Perception Head: Bridging the Gap Between Local and Global Features 全感知头：弥合局部和全局特征之间的差距

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-03-14 DOI: 10.1109/JAS.2025.125333

Jie Hua;Zhongyuan Wang;Xin Tian;Qin Zou;Jinsheng Xiao;Jiayi Ma

{"title":"Full Perception Head: Bridging the Gap Between Local and Global Features","authors":"Jie Hua;Zhongyuan Wang;Xin Tian;Qin Zou;Jinsheng Xiao;Jiayi Ma","doi":"10.1109/JAS.2025.125333","DOIUrl":"https://doi.org/10.1109/JAS.2025.125333","url":null,"abstract":"Object detection is a fundamental task in computer vision that involves identifying and localizing objects within an image. Local features extracted by convolutions, etc., capture fine-grained details such as edges and textures, while global features extracted by full connection layers, etc., represent the overall structure and long-range relationships within the image. These features are crucial for accurate object detection, yet most existing methods focus on aggregating local and global features, often overlooking the importance of medium-range dependencies. To address this gap, we propose a novel full perception module (FP-Module), a simple yet effective feature extraction module designed to simultaneously capture local details, medium-range dependencies, and long-range dependencies. Building on this, we construct a full perception head (FP-Head) by cascading multiple FP-Modules, enabling the prediction layer to leverage the most informative features. Experimental results in the MS COCO dataset demonstrate that our approach significantly enhances object recognition and localization, achieving 2.7-5.7 AP<inf>val</inf> gains when integrated into standard object detectors. Notably, the FP-Module is a universal solution that can be seamlessly incorporated into existing detectors to boost performance. The code will be released at https://github.com/Idcogroup/FP-Head.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 7","pages":"1391-1406"},"PeriodicalIF":15.3,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536530","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 Hierarchical Stochastic Network Approach for Fault Diagnosis of Complex Industrial Processes 复杂工业过程故障诊断的分层随机网络方法

IF 19.2 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-03-14 DOI: 10.1109/JAS.2025.125249

Mingjie Lv;Yonggang Li;Huanzhi Gao;Bei Sun;Keke Huang;Chunhua Yang;Weihua Gui

{"title":"A Hierarchical Stochastic Network Approach for Fault Diagnosis of Complex Industrial Processes","authors":"Mingjie Lv;Yonggang Li;Huanzhi Gao;Bei Sun;Keke Huang;Chunhua Yang;Weihua Gui","doi":"10.1109/JAS.2025.125249","DOIUrl":"https://doi.org/10.1109/JAS.2025.125249","url":null,"abstract":"Complex industrial processes present typical uncertainty due to fluctuations in the composition of raw materials and frequently changing operating conditions. This poses three challenges for precise fault diagnosis, including random noise interference, less distinguishability between multi-class faults, and the new fault emerging. To address these issues, this study formulates fault diagnosis in uncertain industrial processes as a multi-level refined fault diagnosis problem. A hierarchical stochastic network approach is proposed to refine fault diagnosis of multi-class faults. This method considers the augmentation of fault categories as naturally following a hierarchical structure. At each hierarchical stage, stochastic network methods are designed according to the sources of uncertainty. For fault feature extraction, a doubly stochastic attention-based variational graph autoencoder is introduced to suppress noise during the message-passing process, ensuring the extraction of high-quality fault features and providing the provision of differentiated information. Subsequently, multiple stochastic configuration networks are deployed to realize multi-level fault diagnosis from coarse to fine granularity via a hierarchical structure rather than treating all faults equally. This approach effectively enhances the precision of multi-class fault diagnosis and ensures its robust generalization capability. Finally, the feasibility and effectiveness of the proposed method are validated using two industrial processes. The results demonstrate that the proposed method can effectively suppress the random noise interference and adapt to the emergence of small samples and imbalanced extreme fault-type data, achieving a satisfactory fault diagnosis performance.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 8","pages":"1683-1701"},"PeriodicalIF":19.2,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144880499","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

DKAMFormer: Domain Knowledge-Augmented Multiscale Transformer for Remaining Useful Life Prediction of Aeroengine DKAMFormer：航空发动机剩余使用寿命预测领域知识增强多尺度变压器

IF 19.2 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-03-14 DOI: 10.1109/JAS.2025.125126

Song Fu;Yue Wang;Lin Lin;Minghang Zhao;Lizheng Zu;Yifan Lu;Feng Guo;Shiwei Suo;Yikun Liu;Sihao Zhang;Shisheng Zhong

{"title":"DKAMFormer: Domain Knowledge-Augmented Multiscale Transformer for Remaining Useful Life Prediction of Aeroengine","authors":"Song Fu;Yue Wang;Lin Lin;Minghang Zhao;Lizheng Zu;Yifan Lu;Feng Guo;Shiwei Suo;Yikun Liu;Sihao Zhang;Shisheng Zhong","doi":"10.1109/JAS.2025.125126","DOIUrl":"https://doi.org/10.1109/JAS.2025.125126","url":null,"abstract":"Transformers have achieved promising results on aeroengine remaining useful life (RUL) prediction, but they still have several limitations: 1) Aeroengine domain knowledge, which contains rich information that can reflect the aeroengine's health statue, is largely ignored in modeling process; 2) Traditional transformer ignores the valuable degradation information from other time scales. To address these issues, a novel domain knowledge-augmented multiscale transformer (DKAMFormer) is developed by integrating domain knowledge and multiscale learning to improve the prognostic performance and reliability. First, to obtain rich and professional aeroengine domain knowledge, multiple detail and complete knowledge graphs (KGs) are established based on the working principle of aeroengine, including aeroengine structure, components working characteristics and sensor parameters. Second, the domain knowledge contained in KGs is convert to embedded vector by KG representative learning, which are then utilized to strengthen and enrich the original multidimensional time-series (MTS) monitoring data, aiming to intergrade domain knowledge and monitoring data to train DKAMFormer. Third, to learn rich and complementary degradation features, a novel multiscale time scale-guided self-attention (MTSGSA) mechanism is designed, which maps original MTS into different time-scale feature spaces, and then employs multiple independent self-attention head to extract the degradation features from different time-scale spaces. Finally, through a series of comparative experiments on the public CMAPSS and N-CMAPSS datasets and compared with 17 SOTA methods, the developed DKAMFormer significantly improves the RUL prediction performance under multiple operation conditions and degradation modes.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 8","pages":"1610-1635"},"PeriodicalIF":19.2,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144880604","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

Optimal Sensor Scheduling for Remote State Estimation with Partial Channel Observation 基于部分信道观测的远程状态估计传感器优化调度

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-03-14 DOI: 10.1109/JAS.2025.125180

Bowen Sun;Xianghui Cao

引用次数: 0