Ieee-Caa Journal of Automatica Sinica最新文献_第2页

DeepSeek: Paradigm Shifts and Technical Evolution in Large AI Models DeepSeek：大型人工智能模型中的范式转变和技术进化

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-03-16 DOI: 10.1109/JAS.2025.125495

Luolin Xiong;Haofen Wang;Xi Chen;Lu Sheng;Yun Xiong;Jingping Liu;Yanghua Xiao;Huajun Chen;Qing-Long Han;Yang Tang

{"title":"DeepSeek: Paradigm Shifts and Technical Evolution in Large AI Models","authors":"Luolin Xiong;Haofen Wang;Xi Chen;Lu Sheng;Yun Xiong;Jingping Liu;Yanghua Xiao;Huajun Chen;Qing-Long Han;Yang Tang","doi":"10.1109/JAS.2025.125495","DOIUrl":"https://doi.org/10.1109/JAS.2025.125495","url":null,"abstract":"DeepSeek, a Chinese artificial intelligence (AI) startup, has released their V3 and R1 series models, which attracted global attention due to their low cost, high performance, and open-source advantages. This paper begins by reviewing the evolution of large AI models focusing on paradigm shifts, the mainstream large language model (LLM) paradigm, and the DeepSeek paradigm. Subsequently, the paper highlights novel algorithms introduced by DeepSeek, including multi-head latent attention (MLA), mixture-of-experts (MoE), multi-token prediction (MTP), and group relative policy optimization (GRPO). The paper then explores DeepSeek's engineering breakthroughs in LLM scaling, training, inference, and system-level optimization architecture. Moreover, the impact of DeepSeek models on the competitive AI landscape is analyzed, comparing them to mainstream LLMs across various fields. Finally, the paper reflects on the insights gained from DeepSeek's innovations and discusses future trends in the technical and engineering development of large AI models, particularly in data, training, and reasoning.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 5","pages":"841-858"},"PeriodicalIF":15.3,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072836","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

Precision Synchronous Control of Multiple Motion Systems: A Tube-Based MPC Approach 多运动系统的精确同步控制：一种基于管的MPC方法

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-03-16 DOI: 10.1109/JAS.2025.125222

Shuaiqi Chen;Fazhi Song;Yue Dong;Ning Cui;Yang Liu;Xinkai Chen

{"title":"Precision Synchronous Control of Multiple Motion Systems: A Tube-Based MPC Approach","authors":"Shuaiqi Chen;Fazhi Song;Yue Dong;Ning Cui;Yang Liu;Xinkai Chen","doi":"10.1109/JAS.2025.125222","DOIUrl":"https://doi.org/10.1109/JAS.2025.125222","url":null,"abstract":"Lithography machines operate in scanning mode for the fabrication of large-scale integrated circuits (ICs), requiring high-precision synchronous motion between the reticle and wafer stages. Disturbances generated by each stage during high-acceleration movements are transmitted through the base frame, resulting in degradation of synchronization performance. To address this challenge, this paper proposes a tube-based model predictive control (tube-MPC) approach for synchronization in lithography machines. First, the proposed modeling method accurately characterizes the coupling disturbances and synchronization dynamics. Subsequently, a tube-MPC approach is developed to ensure that the states of the nominal system are constrained within the terminal constraint set. To reduce the complexity of online computations, an approach is employed to transform online optimization problems into offline problems by creating an online lookup table. This enables the determination of optimal control inputs via a simplified online optimization algorithm. The robustness and trajectory tracking performance of the proposed approach are verified through simulation experiments, demonstrating its effectiveness in enhancing the synchronization performance of multiple motion systems.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 5","pages":"974-988"},"PeriodicalIF":15.3,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072839","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 Recursive Method to Encryption-Decryption-Based Distributed Set-Membership Filtering for Time-Varying Saturated Systems Over Sensor Networks 传感器网络上时变饱和系统基于加解密分布式集隶属度滤波的递归方法

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-03-16 DOI: 10.1109/JAS.2023.123915

Jun Hu;Jiaxing Li;Chaoqing Jia;Xiaojian Yi;Hongjian Liu

引用次数: 0

Prescribed Performance Bipartite Consensus Control for MASs Under Data-Driven Strategy 数据驱动策略下质量的规定性能二部一致性控制

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-03-16 DOI: 10.1109/JAS.2024.124956

Qi Zhou;Caiyun Yin;Hui Ma;Hongru Ren;Hongyi Li

引用次数: 0

Exploring DeepSeek: A Survey on Advances, Applications, Challenges and Future Directions 探索深度搜索：进展、应用、挑战和未来方向综述

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.2025.125498

Zehang Deng;Wanlun Ma;Qing-Long Han;Wei Zhou;Xiaogang Zhu;Sheng Wen;Yang Xiang

{"title":"Exploring DeepSeek: A Survey on Advances, Applications, Challenges and Future Directions","authors":"Zehang Deng;Wanlun Ma;Qing-Long Han;Wei Zhou;Xiaogang Zhu;Sheng Wen;Yang Xiang","doi":"10.1109/JAS.2025.125498","DOIUrl":"https://doi.org/10.1109/JAS.2025.125498","url":null,"abstract":"The rapid advancement of large models has led to the development of increasingly sophisticated models capable of generating diverse, personalized, and high-quality content. Among these, DeepSeek has emerged as a pivotal open-source initiative, demonstrating high performance at significantly lower computation costs compared to closed-source counterparts. This survey provides a comprehensive overview of the DeepSeek family of models, including DeepSeek-V3 and DeepSeek-R1, covering their core innovations in architecture, system pipeline, algorithm, and infrastructure. We explore their practical applications across various domains, such as healthcare, finance, and education, highlighting their impact on both industry and society. Further-more, we examine potential security, privacy, and ethical concerns arising from the widespread deployment of these models, emphasizing the need for responsible AI development. Finally, we outline future research directions to enhance the performance, safety, and scalability of DeepSeek models, aiming to foster further advancements in the open-source large model community.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 5","pages":"872-893"},"PeriodicalIF":15.3,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072997","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

Federated Services: A Smart Service Ecology with Federated Security for Aligned Data Supply and Scenario-Oriented Demands 联合服务：具有联合安全的智能服务生态，用于一致的数据供应和面向场景的需求

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.2024.124860

Xiaofeng Jia;Juanjuan Li;Shouwen Wang;Hongwei Qi;Fei-Yue Wang;Rui Qin;Min Zhang;Xiaolong Liang

{"title":"Federated Services: A Smart Service Ecology with Federated Security for Aligned Data Supply and Scenario-Oriented Demands","authors":"Xiaofeng Jia;Juanjuan Li;Shouwen Wang;Hongwei Qi;Fei-Yue Wang;Rui Qin;Min Zhang;Xiaolong Liang","doi":"10.1109/JAS.2024.124860","DOIUrl":"https://doi.org/10.1109/JAS.2024.124860","url":null,"abstract":"This paper introduces federated services as a smart service ecology with federated security to align distributed data supply with diversified service demands spanning digital and societal contexts. It presents the comprehensive researches on the theoretical foundation and technical system of federated services, aiming at advancing our understanding and implementation of this novel service paradigm. First, a thorough examination of the characteristics of federated security within federated services is conducted. Then, a five-layer technical framework is formulated under a decentralized intelligent architecture, ensuring secure, agile, and adaptable service provision. On this basis, the operational mechanisms underlying data federation and service confederation is analyzed, with emphasis on the smart supply-demand matching model. Furthermore, a scenario-oriented taxonomy of federated services accompanied by illustrative examples is proposed. Our work offers actionable insights and roadmap for realizing and advancing federated services, contributing to the refinement and wider adoption of this transformative service paradigm in the digital era.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 5","pages":"925-936"},"PeriodicalIF":15.3,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073004","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

Release Power of Mechanism and Data Fusion: A Hierarchical Strategy for Enhanced MIQ-Related Modeling and Fault Detection in BFIP 机制释放力与数据融合：BFIP中增强miq相关建模和故障检测的分层策略

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.2024.124821

Siwei Lou;Chunjie Yang;Zhe Liu;Shaoqi Wang;Hanwen Zhang;Ping Wu

{"title":"Release Power of Mechanism and Data Fusion: A Hierarchical Strategy for Enhanced MIQ-Related Modeling and Fault Detection in BFIP","authors":"Siwei Lou;Chunjie Yang;Zhe Liu;Shaoqi Wang;Hanwen Zhang;Ping Wu","doi":"10.1109/JAS.2024.124821","DOIUrl":"https://doi.org/10.1109/JAS.2024.124821","url":null,"abstract":"Data-driven techniques are reshaping blast furnace iron-making process (BFIP) modeling, but their “black-box” nature often obscures interpretability and accuracy. To overcome these limitations, our mechanism and data co-driven strategy (MDCDS) enhances model transparency and molten iron quality (MIQ) prediction. By zoning the furnace and applying mechanism-based features for material and thermal trends, coupled with a novel stationary broad feature learning system (StaBFLS), interference caused by nonstationary process characteristics are mitigated and the intrinsic information embedded in BFIP is mined. Subsequently, by integrating stationary feature representation with mechanism features, our temporal matching broad learning system (TMBLS) aligns process and quality variables using MIQ as the target. This integration allows us to establish process monitoring statistics using both mechanism and data-driven features, as well as detect modeling deviations. Validated against real-world BFIP data, our MDCDS model demonstrates consistent process alignment, robust feature extraction, and improved MIQ modeling—Yielding better fault detection. Additionally, we offer detailed insights into the validation process, including parameter baselining and optimization. Details of the code are available online.<sup>1</sup><sup>1</sup>https://github.com/SiweiLou/demo_BFIP","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 5","pages":"894-912"},"PeriodicalIF":15.3,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073006","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

Distributed Byzantine-Resilient Learning of Multi-UAV Systems via Filter-Based Centerpoint Aggregation Rules 基于过滤器中心点聚合规则的多无人机系统分布式拜占庭弹性学习

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.2024.124905

Yukang Cui;Linzhen Cheng;Michael Basin;Zongze Wu

{"title":"Distributed Byzantine-Resilient Learning of Multi-UAV Systems via Filter-Based Centerpoint Aggregation Rules","authors":"Yukang Cui;Linzhen Cheng;Michael Basin;Zongze Wu","doi":"10.1109/JAS.2024.124905","DOIUrl":"https://doi.org/10.1109/JAS.2024.124905","url":null,"abstract":"Dear Editor, Through distributed machine learning, multi-UAV systems can achieve global optimization goals without a centralized server, such as optimal target tracking, by leveraging local calculation and communication with neighbors. In this work, we implement the stochastic gradient descent algorithm (SGD) distributedly to optimize tracking errors based on local state and aggregation of the neighbors' estimation. However, Byzantine agents can mislead neighbors, causing deviations from optimal tracking. We prove that the swarm achieves resilient convergence if aggregated results lie within the normal neighbors' convex hull, which can be guaranteed by the introduced centerpoint-based aggregation rule. In the given simulated scenarios, distributed learning using average, geometric median (GM), and coordinate-wise median (CM) based aggregation rules fail to track the target. Compared to solely using the centerpoint aggregation method, our approach, which combines a pre-filter with the centroid aggregation rule, significantly enhances resilience against Byzantine attacks, achieving faster convergence and smaller tracking errors.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 5","pages":"1056-1058"},"PeriodicalIF":15.3,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11005753","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073012","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

CSDD: A Benchmark Dataset for Casting Surface Defect Detection and Segmentation CSDD：铸件表面缺陷检测和分割的基准数据集

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.2025.125228

Kai Mao;Ping Wei;Yangyang Wang;Meiqin Liu;Shuaijie Wang;Nanning Zheng

{"title":"CSDD: A Benchmark Dataset for Casting Surface Defect Detection and Segmentation","authors":"Kai Mao;Ping Wei;Yangyang Wang;Meiqin Liu;Shuaijie Wang;Nanning Zheng","doi":"10.1109/JAS.2025.125228","DOIUrl":"https://doi.org/10.1109/JAS.2025.125228","url":null,"abstract":"Automatic surface defect detection is a critical technique for ensuring product quality in industrial casting production. While general object detection techniques have made remarkable progress over the past decade, casting surface defect detection still has considerable room for improvement. Lack of sufficient and high-quality data has become one of the most challenging problems for casting surface defect detection. In this paper, we construct a new casting surface defect dataset (CSDD) containing 2100 high-resolution images of casting surface defects and 56 356 defects in total. The class and defect region for each defect are manually labeled. We conduct a series of experiments on this dataset using multiple state-of-the-art object detection methods, establishing a comprehensive set of baselines. We also propose a defect detection method based on YOLOv5 with the global attention mechanism and partial convolution. Our proposed method achieves superior performance compared to other object detection methods. Additionally, we also conduct a series of experiments with multiple state-of-the-art semantic segmentation methods, providing extensive baselines for defect segmentation. To the best of our knowledge, the CSDD has the largest number of defects for casting surface defect detection and segmentation. It would benefit both the industrial vision research and manufacturing applications. Dataset and code are available at https://github.com/Kerio99/CSDD.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 5","pages":"947-960"},"PeriodicalIF":15.3,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073009","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

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