Dear Editor, This letter proposes a fuzzy indirect iterative learning (FIIL) active disturbance rejection control (ADRC) scheme to address the impact of uncertain factors of plant-protection unmanned ground vehicle (UGV), in which ADRC is a data-driven model-free control algorithm that only relies on the input and output data of the system. Based on the established nonlinear time-varying dynamic model including dynamic load (medicine box), the FIIL technology is adopted to turn the bandwidth and control channel gain online, in which the fuzzy logic system is used to update the gain parameters of iterative learning in real time. Simulation and experiment show the FIIL-ADRC scheme has better control performance.
{"title":"Data-Driven Active Disturbance Rejection Control of Plant-Protection Unmanned Ground Vehicle Prototype: A Fuzzy Indirect Iterative Learning Approach","authors":"Tao Chen;Ruiyuan Zhao;Jian Chen;Zichao Zhang","doi":"10.1109/JAS.2023.124158","DOIUrl":"https://doi.org/10.1109/JAS.2023.124158","url":null,"abstract":"Dear Editor, This letter proposes a fuzzy indirect iterative learning (FIIL) active disturbance rejection control (ADRC) scheme to address the impact of uncertain factors of plant-protection unmanned ground vehicle (UGV), in which ADRC is a data-driven model-free control algorithm that only relies on the input and output data of the system. Based on the established nonlinear time-varying dynamic model including dynamic load (medicine box), the FIIL technology is adopted to turn the bandwidth and control channel gain online, in which the fuzzy logic system is used to update the gain parameters of iterative learning in real time. Simulation and experiment show the FIIL-ADRC scheme has better control performance.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":null,"pages":null},"PeriodicalIF":15.3,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10488093","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141729904","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}
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.
{"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":null,"pages":null},"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}
Tao Wang;Qiming Chen;Xun Lang;Lei Xie;Peng Li;Hongye Su
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.
{"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":null,"pages":null},"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}
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.
{"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":null,"pages":null},"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}
Zhiming Zhang;Shangce Gao;MengChu Zhou;Mengtao Yan;Shuyang Cao
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.
{"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":null,"pages":null},"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}
Quanbo Ge;Yang Cheng;Hong Li;Ziyi Ye;Yi Zhu;Gang Yao
For accurately identifying the distribution characteristic of Gaussian-like noises in unmanned aerial vehicle (UAV) state estimation, this paper proposes a non-parametric scheme based on curve similarity matching. In the framework of the proposed scheme, a Parzen window (kernel density estimation, KDE) method on sliding window technology is applied for roughly estimating the sample probability density, a precise data probability density function (PDF) model is constructed with the least square method on K-fold cross validation, and the testing result based on evaluation method is obtained based on some data characteristic analyses of curve shape, abruptness and symmetry. Some comparison simulations with classical methods and UAV flight experiment shows that the proposed scheme has higher recognition accuracy than classical methods for some kinds of Gaussian-like data, which provides better reference for the design of Kalman filter (KF) in complex water environment.
为准确识别无人飞行器（UAV）状态估计中类高斯噪声的分布特征，本文提出了一种基于曲线相似性匹配的非参数方案。在该方案框架内，采用滑动窗口技术的 Parzen 窗口（核密度估计，KDE）方法对样本概率密度进行粗略估计，利用 K 倍交叉验证的最小二乘法构建精确的数据概率密度函数（PDF）模型，并基于对曲线形状、突变性和对称性等数据特征的分析，得出基于评估方法的测试结果。通过与经典方法的对比模拟和无人机飞行实验表明，对于某些类高斯数据，所提出的方案比经典方法具有更高的识别精度，为复杂水环境下卡尔曼滤波器（KF）的设计提供了更好的参考。
{"title":"A Non-Parametric Scheme for Identifying Data Characteristic Based on Curve Similarity Matching","authors":"Quanbo Ge;Yang Cheng;Hong Li;Ziyi Ye;Yi Zhu;Gang Yao","doi":"10.1109/JAS.2024.124359","DOIUrl":"https://doi.org/10.1109/JAS.2024.124359","url":null,"abstract":"For accurately identifying the distribution characteristic of Gaussian-like noises in unmanned aerial vehicle (UAV) state estimation, this paper proposes a non-parametric scheme based on curve similarity matching. In the framework of the proposed scheme, a Parzen window (kernel density estimation, KDE) method on sliding window technology is applied for roughly estimating the sample probability density, a precise data probability density function (PDF) model is constructed with the least square method on K-fold cross validation, and the testing result based on evaluation method is obtained based on some data characteristic analyses of curve shape, abruptness and symmetry. Some comparison simulations with classical methods and UAV flight experiment shows that the proposed scheme has higher recognition accuracy than classical methods for some kinds of Gaussian-like data, which provides better reference for the design of Kalman filter (KF) in complex water environment.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":null,"pages":null},"PeriodicalIF":11.8,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141181856","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}
Tianyu Shen;Jinlin Sun;Shihan Kong;Yutong Wang;Juanjuan Li;Xuan Li;Fei-Yue Wang
The tremendous impact of large models represented by ChatGPT [1]–[3] makes it necessary to consider the practical applications of such models [4]. However, for an artificial intelligence (AI) to truly evolve, it needs to possess a physical “body” to transition from the virtual world to the real world and evolve through interaction with the real environments. In this context, “embodied intelligence” has sparked a new wave of research and technology, leading AI beyond the digital realm into a new paradigm that can actively act and perceive in a physical environment through tangible entities such as robots and automated devices [5].
{"title":"The Journey/DAO/TAO of Embodied Intelligence: From Large Models to Foundation Intelligence and Parallel Intelligence","authors":"Tianyu Shen;Jinlin Sun;Shihan Kong;Yutong Wang;Juanjuan Li;Xuan Li;Fei-Yue Wang","doi":"10.1109/JAS.2024.124407","DOIUrl":"https://doi.org/10.1109/JAS.2024.124407","url":null,"abstract":"The tremendous impact of large models represented by ChatGPT [1]–[3] makes it necessary to consider the practical applications of such models [4]. However, for an artificial intelligence (AI) to truly evolve, it needs to possess a physical “body” to transition from the virtual world to the real world and evolve through interaction with the real environments. In this context, “embodied intelligence” has sparked a new wave of research and technology, leading AI beyond the digital realm into a new paradigm that can actively act and perceive in a physical environment through tangible entities such as robots and automated devices [5].","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":null,"pages":null},"PeriodicalIF":11.8,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10539310","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141181852","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}
This paper investigates the problem of global/semi-global finite-time consensus for integrator-type multi-agent systems. New hyperbolic tangent function-based protocols are proposed to achieve global and semi-global finite-time consensus for both single-integrator and double-integrator multi-agent systems with leaderless undirected and leader-following directed communication topologies. These new protocols not only provide an explicit upper-bound estimate for the settling time, but also have a user-prescribed bounded control level. In addition, compared to some existing results based on the saturation function, the proposed approach considerably simplifies the protocol design and the stability analysis. Illustrative examples and an application demonstrate the effectiveness of the proposed protocols.
{"title":"Hyperbolic Tangent Function-Based Protocols for Global/Semi-Global Finite-Time Consensus of Multi-Agent Systems","authors":"Zongyu Zuo;Jingchuan Tang;Ruiqi Ke;Qing-Long Han","doi":"10.1109/JAS.2024.124485","DOIUrl":"https://doi.org/10.1109/JAS.2024.124485","url":null,"abstract":"This paper investigates the problem of global/semi-global finite-time consensus for integrator-type multi-agent systems. New hyperbolic tangent function-based protocols are proposed to achieve global and semi-global finite-time consensus for both single-integrator and double-integrator multi-agent systems with leaderless undirected and leader-following directed communication topologies. These new protocols not only provide an explicit upper-bound estimate for the settling time, but also have a user-prescribed bounded control level. In addition, compared to some existing results based on the saturation function, the proposed approach considerably simplifies the protocol design and the stability analysis. Illustrative examples and an application demonstrate the effectiveness of the proposed protocols.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":null,"pages":null},"PeriodicalIF":11.8,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141181869","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}
Juanjuan Li;Rui Qin;Sangtian Guan;Wenwen Ding;Fei Lin;Fei-Yue Wang
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.
{"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":null,"pages":null},"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}
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.
{"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":null,"pages":null},"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}
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