Ieee-Caa Journal of Automatica Sinica最新文献_第10页

Data-Driven Active Disturbance Rejection Control of Plant-Protection Unmanned Ground Vehicle Prototype: A Fuzzy Indirect Iterative Learning Approach 植物保护无人地面飞行器原型的数据驱动主动干扰抑制控制：模糊间接迭代学习法

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2024-04-01 DOI: 10.1109/JAS.2023.124158

Tao Chen;Ruiyuan Zhao;Jian Chen;Zichao Zhang

引用次数: 0

Constrained Multi-Objective Optimization With Deep Reinforcement Learning Assisted Operator Selection 利用深度强化学习辅助算子选择进行受限多目标优化

IF 11.8 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2024-03-28 DOI: 10.1109/JAS.2023.123687

Fei Ming;Wenyin Gong;Ling Wang;Yaochu Jin

{"title":"Constrained Multi-Objective Optimization With Deep Reinforcement Learning Assisted Operator Selection","authors":"Fei Ming;Wenyin Gong;Ling Wang;Yaochu Jin","doi":"10.1109/JAS.2023.123687","DOIUrl":"https://doi.org/10.1109/JAS.2023.123687","url":null,"abstract":"Solving constrained multi-objective optimization problems with evolutionary algorithms has attracted considerable attention. Various constrained multi-objective optimization evolutionary algorithms (CMOEAs) have been developed with the use of different algorithmic strategies, evolutionary operators, and constraint-handling techniques. The performance of CMOEAs may be heavily dependent on the operators used, however, it is usually difficult to select suitable operators for the problem at hand. Hence, improving operator selection is promising and necessary for CMOEAs. This work proposes an online operator selection framework assisted by Deep Reinforcement Learning. The dynamics of the population, including convergence, diversity, and feasibility, are regarded as the state; the candidate operators are considered as actions; and the improvement of the population state is treated as the reward. By using a Q-network to learn a policy to estimate the Q-values of all actions, the proposed approach can adaptively select an operator that maximizes the improvement of the population according to the current state and thereby improve the algorithmic performance. The framework is embedded into four popular CMOEAs and assessed on 42 benchmark problems. The experimental results reveal that the proposed Deep Reinforcement Learning-assisted operator selection significantly improves the performance of these CMOEAs and the resulting algorithm obtains better versatility compared to nine state-of-the-art CMOEAs.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"11 4","pages":"919-931"},"PeriodicalIF":11.8,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140321685","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

Detection of Oscillations in Process Control Loops from Visual Image Space Using Deep Convolutional Networks 利用深度卷积网络从视觉图像空间检测过程控制回路中的振荡

IF 11.8 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2024-03-27 DOI: 10.1109/JAS.2023.124170

Tao Wang;Qiming Chen;Xun Lang;Lei Xie;Peng Li;Hongye Su

{"title":"Detection of Oscillations in Process Control Loops from Visual Image Space Using Deep Convolutional Networks","authors":"Tao Wang;Qiming Chen;Xun Lang;Lei Xie;Peng Li;Hongye Su","doi":"10.1109/JAS.2023.124170","DOIUrl":"https://doi.org/10.1109/JAS.2023.124170","url":null,"abstract":"Oscillation detection has been a hot research topic in industries due to the high incidence of oscillation loops and their negative impact on plant profitability. Although numerous automatic detection techniques have been proposed, most of them can only address part of the practical difficulties. An oscillation is heuristically defined as a visually apparent periodic variation. However, manual visual inspection is labor-intensive and prone to missed detection. Convolutional neural networks (CNNs), inspired by animal visual systems, have been raised with powerful feature extraction capabilities. In this work, an exploration of the typical CNN models for visual oscillation detection is performed. Specifically, we tested MobileNet-V1, ShuffleNet-V2, EfficientNet-B0, and GhostNet models, and found that such a visual framework is well-suited for oscillation detection. The feasibility and validity of this framework are verified utilizing extensive numerical and industrial cases. Compared with state-of-the-art oscillation detectors, the suggested framework is more straightforward and more robust to noise and mean-nonstationarity. In addition, this framework generalizes well and is capable of handling features that are not present in the training data, such as multiple oscillations and outliers.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"11 4","pages":"982-995"},"PeriodicalIF":11.8,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140310080","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 Local-Global Attention Fusion Framework with Tensor Decomposition for Medical Diagnosis 采用张量分解的局部-全局注意力融合框架用于医学诊断

IF 11.8 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2024-03-27 DOI: 10.1109/JAS.2023.124167

Peishu Wu;Han Li;Liwei Hu;Jirong Ge;Nianyin Zeng

{"title":"A Local-Global Attention Fusion Framework with Tensor Decomposition for Medical Diagnosis","authors":"Peishu Wu;Han Li;Liwei Hu;Jirong Ge;Nianyin Zeng","doi":"10.1109/JAS.2023.124167","DOIUrl":"https://doi.org/10.1109/JAS.2023.124167","url":null,"abstract":"Dear Editor, In this letter, a novel hierarchical fusion framework is proposed to address the imperfect data property in complex medical image analysis (MIA) scenes. In particular, by combining the strengths of convolutional neural networks (CNNs) and transformers, the enhanced feature extraction, spatial modeling, and sequential context learning are realized to provide comprehensive insights on the complex data patterns. Integration of information in different level is enabled via a multi-attention fusion mechanism, and the tensor decomposition methods are adopted so that compact and distinctive representation of the underlying and high-dimensional medical image features can be accomplished [1]. It is shown from the evaluation results that the proposed framework is competitive and superior as compared with some other advanced algorithms, which effectively handles the imperfect property of inter-class similarity and intra-class differences in diseases, and meanwhile, the model complexity is reduced within an acceptable level, which benefits the deployment in clinic practice.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"11 6","pages":"1536-1538"},"PeriodicalIF":11.8,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10539345","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141164701","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

Mapping Network-Coordinated Stacked Gated Recurrent Units for Turbulence Prediction 绘制用于湍流预测的网络协调堆叠门控循环单元图

IF 11.8 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2024-03-27 DOI: 10.1109/JAS.2024.124335

Zhiming Zhang;Shangce Gao;MengChu Zhou;Mengtao Yan;Shuyang Cao

{"title":"Mapping Network-Coordinated Stacked Gated Recurrent Units for Turbulence Prediction","authors":"Zhiming Zhang;Shangce Gao;MengChu Zhou;Mengtao Yan;Shuyang Cao","doi":"10.1109/JAS.2024.124335","DOIUrl":"https://doi.org/10.1109/JAS.2024.124335","url":null,"abstract":"Accurately predicting fluid forces acting on the surface of a structure is crucial in engineering design. However, this task becomes particularly challenging in turbulent flow, due to the complex and irregular changes in the flow field. In this study, we propose a novel deep learning method, named mapping network-coordinated stacked gated recurrent units (MSU), for predicting pressure on a circular cylinder from velocity data. Specifically, our coordinated learning strategy is designed to extract the most critical velocity point for prediction, a process that has not been explored before. In our experiments, MSU extracts one point from a velocity field containing 121 points and utilizes this point to accurately predict 100 pressure points on the cylinder. This method significantly reduces the workload of data measurement in practical engineering applications. Our experimental results demonstrate that MSU predictions are highly similar to the real turbulent data in both spatio-temporal and individual aspects. Furthermore, the comparison results show that MSU predicts more precise results, even outperforming models that use all velocity field points. Compared with state-of-the-art methods, MSU has an average improvement of more than 45% in various indicators such as root mean square error (RMSE). Through comprehensive and authoritative physical verification, we established that MSU's prediction results closely align with pressure field data obtained in real turbulence fields. This confirmation underscores the considerable potential of MSU for practical applications in real engineering scenarios. The code is available at https://github.com/zhangzm0128/MSU.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"11 6","pages":"1331-1341"},"PeriodicalIF":11.8,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141182046","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

The Journey/DAO/TAO of Embodied Intelligence: From Large Models to Foundation Intelligence and Parallel Intelligence 体现智能的旅程/DAO/TAO：从大型模型到基础智能和并行智能

IF 11.8 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2024-03-27 DOI: 10.1109/JAS.2024.124407

Tianyu Shen;Jinlin Sun;Shihan Kong;Yutong Wang;Juanjuan Li;Xuan Li;Fei-Yue Wang

引用次数: 0

A Non-Parametric Scheme for Identifying Data Characteristic Based on Curve Similarity Matching 基于曲线相似性匹配的非参数数据特征识别方案

IF 11.8 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2024-03-27 DOI: 10.1109/JAS.2024.124359

Quanbo Ge;Yang Cheng;Hong Li;Ziyi Ye;Yi Zhu;Gang Yao

引用次数: 0

Hyperbolic Tangent Function-Based Protocols for Global/Semi-Global Finite-Time Consensus of Multi-Agent Systems 基于双曲切函数的多代理系统全局/半全局有限时间共识协议

IF 11.8 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2024-03-27 DOI: 10.1109/JAS.2024.124485

Zongyu Zuo;Jingchuan Tang;Ruiqi Ke;Qing-Long Han

引用次数: 0

Attention Markets of Blockchain-Based Decentralized Autonomous Organizations 关注基于区块链的去中心化自治组织市场

IF 11.8 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2024-03-27 DOI: 10.1109/JAS.2024.124491

Juanjuan Li;Rui Qin;Sangtian Guan;Wenwen Ding;Fei Lin;Fei-Yue Wang

{"title":"Attention Markets of Blockchain-Based Decentralized Autonomous Organizations","authors":"Juanjuan Li;Rui Qin;Sangtian Guan;Wenwen Ding;Fei Lin;Fei-Yue Wang","doi":"10.1109/JAS.2024.124491","DOIUrl":"https://doi.org/10.1109/JAS.2024.124491","url":null,"abstract":"The attention is a scarce resource in decentralized autonomous organizations (DAOs), as their self-governance relies heavily on the attention-intensive decision-making process of “proposal and voting”. To prevent the negative effects of proposers' attention-capturing strategies that contribute to the “tragedy of the commons” and ensure an efficient distribution of attention among multiple proposals, it is necessary to establish a market-driven allocation scheme for DAOs' attention. First, the Harberger tax-based attention markets are designed to facilitate its allocation via continuous and automated trading, where the individualized Harberger tax rate (HTR) determined by the proposers' reputation is adopted. Then, the Stackelberg game model is formulated in these markets, casting attention to owners in the role of leaders and other competitive proposers as followers. Its equilibrium trading strategies are also discussed to unravel the intricate dynamics of attention pricing. Moreover, utilizing the single-round Stackelberg game as an illustrative example, the existence of Nash equilibrium trading strategies is demonstrated. Finally, the impact of individualized HTR on trading strategies is investigated, and results suggest that it has a negative correlation with leaders' self-accessed prices and ownership duration, but its effect on their revenues varies under different conditions. This study is expected to provide valuable insights into leveraging attention resources to improve DAOs' governance and decision-making process.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"11 6","pages":"1370-1380"},"PeriodicalIF":11.8,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141181853","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

Industry-Oriented Detection Method of PCBA Defects Using Semantic Segmentation Models 利用语义分割模型检测 PCBA 缺陷的行业导向方法

IF 11.8 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2024-03-27 DOI: 10.1109/JAS.2024.124422

Yang Li;Xiao Wang;Zhifan He;Ze Wang;Ke Cheng;Sanchuan Ding;Yijing Fan;Xiaotao Li;Yawen Niu;Shanpeng Xiao;Zhenqi Hao;Bin Gao;Huaqiang Wu

{"title":"Industry-Oriented Detection Method of PCBA Defects Using Semantic Segmentation Models","authors":"Yang Li;Xiao Wang;Zhifan He;Ze Wang;Ke Cheng;Sanchuan Ding;Yijing Fan;Xiaotao Li;Yawen Niu;Shanpeng Xiao;Zhenqi Hao;Bin Gao;Huaqiang Wu","doi":"10.1109/JAS.2024.124422","DOIUrl":"https://doi.org/10.1109/JAS.2024.124422","url":null,"abstract":"Automated optical inspection (AOI) is a significant process in printed circuit board assembly (PCBA) production lines which aims to detect tiny defects in PCBAs. Existing AOI equipment has several deficiencies including low throughput, large computation cost, high latency, and poor flexibility, which limits the efficiency of online PCBA inspection. In this paper, a novel PCBA defect detection method based on a lightweight deep convolution neural network is proposed. In this method, the semantic segmentation model is combined with a rule-based defect recognition algorithm to build up a defect detection framework. To improve the performance of the model, extensive real PCBA images are collected from production lines as datasets. Some optimization methods have been applied in the model according to production demand and enable integration in lightweight computing devices. Experiment results show that the production line using our method realizes a throughput more than three times higher than traditional methods. Our method can be integrated into a lightweight inference system and promote the flexibility of AOI. The proposed method builds up a general paradigm and excellent example for model design and optimization oriented towards industrial requirements.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"11 6","pages":"1438-1446"},"PeriodicalIF":11.8,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141181875","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