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

CMo-IABA: Constrained Multi-Objective Invisible and Adaptive Backdoor Attack for Deep Neural Networks-Based SAR Image Classification CMo-IABA：基于深度神经网络的SAR图像分类约束多目标不可见自适应后门攻击

IF 18.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2026-07-01 Epub Date: 2026-08-03 DOI: 10.1109/JAS.2025.125888

Guo-Qiang Zeng;Hai-Nan Wei;Kang-Di Lu;Guang-Gang Geng

{"title":"CMo-IABA: Constrained Multi-Objective Invisible and Adaptive Backdoor Attack for Deep Neural Networks-Based SAR Image Classification","authors":"Guo-Qiang Zeng;Hai-Nan Wei;Kang-Di Lu;Guang-Gang Geng","doi":"10.1109/JAS.2025.125888","DOIUrl":"https://doi.org/10.1109/JAS.2025.125888","url":null,"abstract":"Deep neural networks (DNNs) have been widely applied in the field of synthetic aperture radar (SAR) image while they are facing more and more serious threats from a variety of malicious attacks. As one of the malicious attacks with strong destructiveness and stealth, backdoor attacks have severely affected DNNs, but there are no related research studies concerning the backdoor attacks against the DNNs-based SAR image classification models. In this work, we make the first attempt to automatically design a constrained multi-objective invisible and adaptive backdoor attack termed as CMo-IABA for DNNs-based SAR image classification. In the CMo-IABA, we firstly generate an initial trigger-based backdoor attack randomly by a random combination of pixels with random noise conforming to the Gaussian distribution. Then, we design multi-objective functions by considering the trade-off between maximizing the attack success rate and minimizing <tex>$L_{2}$</tex> distance-based invisibility. The classification error between the backdoor DNN and the clean model is considered as the constraint to maintain the original performance of the model. To solve the optimization problem, a discrete non-dominated sorting genetic algorithm-II is introduced as the search engine with the developed crossover operation and mutation operation. The superiority of the proposed CMo-IABA to five state-of-the-art backdoor attacks on six different types of DNNs-based SAR image classification models has been demonstrated by the experimental results on Fudan University SAR (FUSAR)-ship and moving and stationary target acquisition and recognition (MSTAR) datasets in terms of attack success rate and <tex>$L_{2}$</tex> distance-based invisibility.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"13 7","pages":"1731-1746"},"PeriodicalIF":18.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148661839","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

Multi-Time-Scale Modeling for Day-Ahead Forecasting of Passenger Travel-Time and Destinations Distribution 旅客出行时间及目的地分布的多时间尺度模型

IF 18.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2026-07-01 Epub Date: 2026-08-03 DOI: 10.1109/JAS.2026.126170

Yiren Liu;Lishuai Li;Yang Zhao;Yining Dong;S. Joe Qin

引用次数: 0

A Two-Step Iterative Local Search for the Harvester Scheduling Problem with Splittable Workloads 负载可分的收割机调度问题的两步迭代局部搜索

IF 18.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2026-07-01 Epub Date: 2026-08-03 DOI: 10.1109/JAS.2025.125768

Hongyu Lin;Jing Liang;Bo Jin;Caitong Yue;Yaonan Wang

{"title":"A Two-Step Iterative Local Search for the Harvester Scheduling Problem with Splittable Workloads","authors":"Hongyu Lin;Jing Liang;Bo Jin;Caitong Yue;Yaonan Wang","doi":"10.1109/JAS.2025.125768","DOIUrl":"https://doi.org/10.1109/JAS.2025.125768","url":null,"abstract":"With the growing emphasis on intelligent agriculture, scheduling problems involving multiple harvesters in large-scale croplands have attracted increasing attention. In these scenarios, harvesters collaboratively perform tasks in expansive croplands, with each harvester taking on a portion of the workload. This study defines such problems as the harvester scheduling problem with splittable workloads (HSPSW). Unlike most multi-robot task allocation problems, HSPSW requires collaboration among several harvesters and involves more harvesters than croplands. These features increase the complexity of har-vester-cropland interactions. To address these challenges, this paper proposes a novel two-step iterative local search (TSILS) algorithm. A greedy strategy that prioritizes croplands with higher workloads is proposed to generate initial solutions. Subsequently, a two-step iterative search strategy is employed to obtain high-quality solutions: the first step uses a predefined objective to facilitate rapid workload allocation for feasible solutions, and the second step integrates specific neighborhood operators and fine-grained local search to improve its search capability. Experimental results demonstrate that the proposed method TSILS significantly outperforms existing approaches in both algorithmic performance and computational efficiency.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"13 7","pages":"1584-1599"},"PeriodicalIF":18.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148661783","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

Causal Representation Learning for Trustworthy Industrial Process Modeling 可信工业过程建模的因果表示学习

IF 18.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2026-07-01 Epub Date: 2026-08-03 DOI: 10.1109/JAS.2025.125678

Liang Cao;Fan Yang;Youqing Wang

引用次数: 0

Multi-Agent Swarm Optimization Method with Contribution-Based Cooperation for Distributed Multi-Target Localization and Data Association 分布式多目标定位与数据关联的基于贡献协作的多智能体群优化方法

IF 18.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2026-07-01 Epub Date: 2026-08-03 DOI: 10.1109/JAS.2025.125150

Taiyou Chen;Xiaomin Hu;Qiuzhen Lin;Weineng Chen

{"title":"Multi-Agent Swarm Optimization Method with Contribution-Based Cooperation for Distributed Multi-Target Localization and Data Association","authors":"Taiyou Chen;Xiaomin Hu;Qiuzhen Lin;Weineng Chen","doi":"10.1109/JAS.2025.125150","DOIUrl":"https://doi.org/10.1109/JAS.2025.125150","url":null,"abstract":"With the development of communication and computation capabilities on terminal hardware, it is promising to apply distributed optimization methods to wireless sensor networks to improve the autonomous collaboration ability of sensors. In this work, we study distributed multi-target localization problem with measurement-to-measurement association (DM2M), where each sensor only accesses its own measurement data without the association of measurements from other sensors. We first reformulate DM2M into a distributed bilevel optimization problem to reduce the search space of negotiated variables caused by the data association among sensors. Then, we propose a multi-agent swarm optimization method with contribution-based cooperation (MASTER). In MASTER, each sensor maintains a particle swarm to represent candidate solutions of target positions. Sensors evolve their particle swarms through two phases of local optimization and neighbor cooperation to locate the target cooperatively. To address the bilevel local objective function, we combine the Kuhn-Munkres algorithm and the competitive swarm optimization for local optimization. To promote sensors to optimize the global objective, we design a contribution-based cooperation method to guide sensors to learn from their neighbors. Through localization experiments for different target numbers and localization dimensions, the proposed algorithm achieves smaller localization errors and more stable consensus than existing algorithms.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"13 7","pages":"1673-1688"},"PeriodicalIF":18.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148661862","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

Spatiotemporal Graph Neural Network-Incorporated Latent Factorization of Tensors for Dynamic QoS Estimation 基于时空图神经网络的张量潜在分解动态QoS估计

IF 18.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2026-07-01 Epub Date: 2026-08-03 DOI: 10.1109/JAS.2025.125750

Xin Luo;Fanghui Bi;Tiantian He

{"title":"Spatiotemporal Graph Neural Network-Incorporated Latent Factorization of Tensors for Dynamic QoS Estimation","authors":"Xin Luo;Fanghui Bi;Tiantian He","doi":"10.1109/JAS.2025.125750","DOIUrl":"https://doi.org/10.1109/JAS.2025.125750","url":null,"abstract":"Quality of service (QoS) data that characterize historical user-service invocations that vary over time are vital to discovering patterns of cloud services and understanding user behaviors. Though effective, prevalent approaches never consider higher-order spatiotemporal connectivity within QoS data, thus suffering from inferior performance. To address this critical issue, this paper presents spatiotemporal graph convolutional network (GCN) that is equipped with the functionality of latent factorization of tensors (SGLFT). It is achieved by introducing three key innovations: 1) Proposing a tensor graph convolution based on the generalized tensor product technique for uniformly modeling the temporal and spatial patterns within dynamic user-service graphs; 2) Incorporating the built layer-wise graph convolution into tensor factorization for efficiently capturing the implied spatiotemporal high-order connectivity; and 3) Developing a node-level attention pooling mechanism to perceive feature differences among neighbors and across time slots. Theoretical derivations are conducted to demonstrate that the expressivity of the graph neural network proposed in this paper is evidently higher than that of vanilla GCNs. Empirical studies on eight large-scale testing cases arising from two real-world dynamic QoS datasets show that SGLFT substantially outperforms state-of-the-art QoS estimators regarding estimation accuracy for missing dynamic QoS data.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"13 7","pages":"1642-1656"},"PeriodicalIF":18.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148661684","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

Secure Control of Networked Control Systems Based on Multiplicative Watermarking-Based Detection and Data Compensation 基于乘性水印检测和数据补偿的网络控制系统安全控制

IF 18.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2026-07-01 Epub Date: 2026-08-03 DOI: 10.1109/JAS.2025.125753

Lang Wu;Dajun Du;Yang Xiao;Qing Sun;Minrui Fei

引用次数: 0

Zonotope-Based Distributed Fusion for 2-D Binary Sensor Systems Under FlexRay Protocols FlexRay协议下基于分区的二维二元传感器系统分布式融合

IF 18.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2026-07-01 Epub Date: 2026-08-03 DOI: 10.1109/JAS.2025.125744

Lan Lan;Guoliang Wei;Ying Sun

引用次数: 0

The Discipline of Control Science and Engineering in the Intelligent Era: Challenges, Paradigm Transformation, and Future Directions 智能时代的控制科学与工程学科：挑战、范式转换与未来方向

IF 18.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2026-07-01 Epub Date: 2026-08-03 DOI: 10.1109/JAS.2026.126254

Chenghui Zhang

{"title":"The Discipline of Control Science and Engineering in the Intelligent Era: Challenges, Paradigm Transformation, and Future Directions","authors":"Chenghui Zhang","doi":"10.1109/JAS.2026.126254","DOIUrl":"https://doi.org/10.1109/JAS.2026.126254","url":null,"abstract":"AT present, the world is experiencing profound transformations unseen in a century, with a new round of scientific and technological revolution increasingly inter-twined with major-power competition. Against this backdrop, artificial intelligence (AI) has emerged as a key strategic technology that is profoundly reshaping global economic, social, and military systems. Through top-level policy design and national development planning, China has been continuously promoting the deep integration of AI with industry, energy, education, and other key domains. Therefore, the intelligent era should not be regarded as a future prospect, but rather as an ongoing transformation that is already reshaping technological development and social progress. Control Science and Engineering (CSE) is a foundational, interdisciplinary, and cross-cutting discipline concerned with system modeling, dynamic regulation, feedback optimization, collaborative decision-making, and autonomous operation. It provides a bridge between the physical world and the information world, and it has long served as an enabling discipline for industrial modernization and technological progress. In the intelligent era, its importance is further amplified: increasingly complex systems must become not only automated, but also adaptive, trustworthy, resilient, explainable, and controllable. This paper aims to clarify the historical position, current challenges, and strategic development path of CSE in the intelligent era. The central argument is that the discipline is entering a critical transition from automation to autonomy. This transition is not a simple extension of existing technologies, but a deeper reconstruction of disciplinary paradigms, knowledge systems, organizational mechanisms, and innovation ecosystems. Against this background, this article further emphasizes the foundational role of control principles in building trustworthy intelligence.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"13 7","pages":"1533-1535"},"PeriodicalIF":18.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11639379","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148661705","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

$H_{infty}$ Optimal Output Regulation of Unknown Linear Systems via an Adaptive Dynamic Programming and Internal Model $H_{infty}$ 基于自适应动态规划和内部模型的未知线性系统最优输出调节

IF 18.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2026-06-01 Epub Date: 2026-07-03 DOI: 10.1109/JAS.2026.125777

Yong-Sheng Ma;Jian Sun;Yong Xu

引用次数: 0