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A Low-Complexity and High-Accuracy Defect Detection Network

Journal of Systems Science & Complexity 2025年第2期38卷 573-596页

作者： ZHOU Xunkuai CHEN Xi CHEN Jie CHEN Ben M. School of Electronics and Information Engineering Tongji University Department of Mechanical and Automation Engineering The Chinese University of Hong Kong The Chinese University of Hong Kong Harbin Institute of Technology

Visual-based defect detection efficiently monitors the health and quality of construction and industrial products. However, current defect detection methods often improve detection accuracy at the cost of lower inference speeds or more parameters, struggle with complex data representation, emphasize target features while neglecting environmental information importance, and utilize convolutional or max pooling operations for downsampling, leading to more feature loss. To address these issues, this work presents a low complexity, accurate defect detection network augmented by environmental information-assisted and flexible activation functions to enhance the neural network performance on complex data representation. Environmental information-assisted module is designed for defect detection tasks to assist in accurately locating and predicting defects. Moreover, this work restructure features post-downsampling to mitigate feature loss and design a simple feature module called deep-global fusion that integrates deep and global features to enhance detection *** experiments validate the superiority of the proposed detection network. The deployment of the network on edge computing devices confirms its competitive advantage in portability and reliability.

关键词： Activation function defects detection environmental information-assisted low complexity

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Cloud control for IIoT in a cloud-edge environment

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Journal of Systems engineering and Electronics 2024年第4期35卷 1013-1027页

作者： YAN Ce XIA Yuanqing YANG Hongjiu ZHAN Yufeng School of Automation Beijing Institute of TechnologyBeijing 100081China School of Electrical and Information Engineering Tianjin UniversityTianjin 300072China

The industrial Internet of Things(IIoT)is a new indus-trial idea that combines the latest information and communica-tion technologies with the industrial *** this paper,a cloud control structure is designed for IIoT in cloud-edge envi-ronment with three modes of *** 5G based IIoT,the time sensitive network(TSN)service is introduced in transmission network.A 5G logical TSN bridge is designed to transport TSN streams over 5G framework to achieve end-to-end *** a transmission control protocol(TCP)model with nonlinear disturbance,time delay and uncertainties,a robust adaptive fuzzy sliding mode controller(AFSMC)is given with control rule *** workflows are made up of a series of subtasks that are linked by the dependencies between sensor datasets and task *** workflow scheduling is a non-deterministic polynomial(NP)-hard problem in cloud-edge *** adaptive and non-local-convergent particle swarm optimization(ANCPSO)is designed with nonlinear inertia weight to avoid falling into local optimum,which can reduce the makespan and cost *** and experiments demonstrate that ANCPSO has better performances than other classical algo-rithms.

关键词： 5G and time sensitive network(TSN) industrial Internet of Things(IIoT)workflow transmission control protocol(TCP)flows control cloud edge collaboration multi-objective optimal scheduling

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Multi-objective optimisation method of cutting parameters of CNC machine tools based on improved genetic algorithm

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International Journal of Manufacturing Technology and Management 2025年第3-5期39卷 195-215页

作者： Wang, Zhenzhuo Zhu, Yijie Department of Automation Engineering Henan Polytechnic Institute NanYang473000 China Department of Electronic Information Engineering Henan Polytechnic Institute NanYang473000 China

To improve the energy efficiency level of CNC machine tool processing, reduce production costs and energy consumption, a multi-objective optimisation method for cutting parameters of CNC machine tools based on improved genetic algorithm is proposed. Firstly, a multi-objective optimisation model for cutting parameters of CNC machine tools was constructed with the minimum energy consumption, maximum benefit, and maximum timeliness as objective functions, combined with three constraints of cutting speed, cutting power, and feed rate. Then, the objective function values of the cutting parameters of the CNC machine tool are sorted by non-dominated sorting based on the dominated effect. Finally, an improved Pareto genetic multi-objective optimisation method is designed using the improved niche technique and the vector modulus fitness function as the criterion for outliers, in order to obtain the optimal multi-objective optimisation of the cutting parameters of the CNC machine tool. The experimental results show that the method improves the feed speed, cutting speed and cutting depth, reduces the machining energy consumption, machining cost and machining time, and has good application effect. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Genetic algorithms

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Short-Term Power Load Forecasting Method Based on GRU-Transformer Combined Neural Network Model

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Journal of Computing and information Technology 2024年第1期32卷 1-14页

作者： Mao, Weiwei Yu, Suping Chen, Wenqing College of Computer and Information Engineering Luoyang Institute of Science and Technology Luoyang China School of Electrical Engineering and Automation Luoyang Institute of Technology Luoyang China

Load Forecast (LF) is an important task in the planning, control and application of public power systems. Accurate Short Term Load Forecast (STLF) is the premise of safe and economical operation of a power system. In the research of short-term power load forecasting, machine learning and deep learning are the most popular methods at present, but there still exists a problem that the single and simple structure of power load forecasting model leads to low accuracy of load forecasting. In order to improve the accuracy of STLF, a Gated Cycle Unit (GRU)-Transformer combined neural network model is proposed. Transformer encoder structure is used as feature extractor to mine the complex mapping relationships between the input features and load. The advantage of self-attention mechanism is used to solve the problem of information loss of long sequences in short-term power load forecasting. At the same time, the multivariate time series model of GRU is used for model training. The experimental results on the power load data set of a certain region in southwest China and Panama City show that the proposed combined model prediction method has higher accuracy than those proposed in other literatures, which further proves its feasibility and superiority. © 2024, University of Zagreb Faculty of Electrical engineering and Computing. All rights reserved.

关键词： Neural network models

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Controllability of heterogeneous networked sampled-data systems

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Science China(information Sciences) 2025年第3期68卷 302-318页

作者： Zixuan YANG Lin WANG Xiaofan WANG Guanrong CHEN Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China School of Mechatronic Engineering and Automation Shanghai University School of Electrical and Electronic Engineering Shanghai Institute of Technology Department of Electrical Engineering City University of Hong Kong

This study investigates the controllability of a general heterogeneous networked sampled-data system(HNSS) consisting of nonidentical node systems, where the inner coupling between any pair of nodes can be described by a unique *** signals on control and transmission channels are sampled and held by zero-order holders, and the control sampling period of each node can be different. Necessary and sufficient controllability conditions are developed for the general HNSS, using the Smith normal form and matrix equations, respectively. The HNSS in specific topology or dynamic settings is discussed subsequently with easier-to-verify conditions derived. These heterogeneous factors have been determined to independently or jointly affect the controllability of networked sampled-data systems. Notably, heterogeneous sampling periods have the potential to enhance the overall controllability, but not for systems with some special dynamics. When the node dynamics are heterogeneous,the overall system can be controllable even if it is topologically uncontrollable. In addition, in several typical heterogeneous sampled-data multi-agent systems, pathological sampling of single-node systems will necessarily cause overall uncontrollability.

关键词： network controllability sampled-data system heterogeneous system inner coupling multi-agent system

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Inexact proximal gradient algorithm with random reshuffling for nonsmooth optimization

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Science China(information Sciences) 2025年第1期68卷 219-237页

作者： Xia JIANG Yanyan FANG Xianlin ZENG Jian SUN Jie CHEN National Key Lab of Autonomous Intelligent Unmanned Systems School of AutomationBeijing Institute of Technology Beijing Institute of Technology Chongqing Innovation Center School of Electronic and Information Engineering Tongji University

Proximal gradient algorithms are popularly implemented to achieve convex optimization with nonsmooth regularization. Obtaining the exact solution of the proximal operator for nonsmooth regularization is challenging because errors exist in the computation of the gradient; consequently, the design and application of inexact proximal gradient algorithms have attracted considerable attention from researchers. This paper proposes computationally efficient basic and inexact proximal gradient descent algorithms with random reshuffling. The proposed stochastic algorithms take randomly reshuffled data to perform successive gradient descents and implement only one proximal operator after all data pass through. We prove the convergence results of the proposed proximal gradient algorithms under the sampling-without-replacement reshuffling *** computational errors exist in gradients and proximal operations, the proposed inexact proximal gradient algorithms can converge to an optimal solution neighborhood. Finally, we apply the proposed algorithms to compressed sensing and compare their efficiency with some popular algorithms.

关键词： proximal operator random reshuffling inexact computation compressed sensing nonsmooth optimization

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Intelligent identification method of insulation pull rod defects based on intactness-aware Mosaic data augmentation and fusion of YOLOv5s

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High Voltage 2024年第5期9卷 1171-1182页

作者： Changyun Li Yuze Hua Yilin Liu Kai Liu Sanyi Zhang College of Electrical Engineering and Automation Shandong University of Science and TechnologyQingdaoChina College of Electrical Engineering Southwest Jiaotong UniversityChengduChina Institute of Information Engineering Chinese Academy of SciencesBeijingChina

The authors introduce the intactness-aware Mosaic data augmentation strategy,designed to tackle challenges such as low accuracy in detecting defects in insulation pull rods,limited timeliness in intelligent analysis,and the absence of a comprehensive database for information on insulation pull rod *** proposed strategy incorporates the YOLOv5s algorithm for detecting defects in insulation pull ***,the YOLOv5s network was constructed,and a dataset containing photos of insulation pull rods with white spots,fractures,impurities,and bubble flaws was compiled to capture images of *** research presented a data enhancement approach to improve the images and establish a dataset for insulation pull rod *** YOLOv5s algorithm was applied for both training and testing purposes.A comparative analysis was conducted to assess the detection performance of YOLOv5s against a conventional target detector for identifying defects in insulation pull ***,the utility of Mosaic's data augmentation technique,which incorporates intactness awareness,was evaluated to enhance the accuracy of identifying insulation pull rod *** research findings indicate that the YOLOv5s algorithm is employed for intelligent detection and precise localisation of *** intactnessaware Mosaic data augmentation strategy significantly improves the accuracy of detecting faults in insulation pull *** YOLOv5s model used achieves a performance index mAP@0.5:0.95 of 0.563 on the test set,distinct from the training set *** a threshold of 0.5,the mAP@0.5 score is 0.904,indicating a substantial improvement in both detection efficiency and accuracy compared to conventional target detection *** approaches for identifying defects in insulation pull rods are introduced.

关键词： Mosaic insulation defects

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Multispectral point cloud superpoint segmentation

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Science China(Technological Sciences) 2024年第4期67卷 1270-1281页

作者： WANG QingWang WANG MingYe ZHANG ZiFeng SONG Jian ZENG Kai SHEN Tao GU YanFeng Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming 650500China Yunnan Key Laboratory of Computer Technologies Application Kunming 650500China School of Electronics and Information Engineering Harbin Institute of TechnologyHarbin 150001China

The multitude of airborne point clouds limits the point cloud processing *** are grouped based on similar points,which can effectively alleviate the demand for computing resources and improve processing ***,existing superpoint segmentation methods focus only on local geometric structures,resulting in inconsistent spectral features of points within a *** feature inconsistencies degrade the performance of subsequent ***,this study proposes a novel Superpoint Segmentation method that jointly utilizes spatial Geometric and Spectral information for multispectral point cloud superpoint segmentation(GSI-SS).Specifically,a similarity metric that combines spatial geometry and spectral information is proposed to facilitate the consistency of geometric structures and object attributes within segmented *** the formation of the primary superpoints,an intersuperpoint pointexchange mechanism that maximizes feature consistency within the final superpoints is *** are conducted on two real multispectral point cloud datasets,and the proposed method achieved higher recall,precision,F score,and lower global consistency and feature classification *** experimental results demonstrate the superiority of the proposed GSI-SS over several state-of-the-art methods.

关键词： multispectral point cloud superpoint segmentation over-segmentation spatial-spectral joint metric

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Cooperative output regulation of heterogeneous directed multi-agent systems:a fully distributed model-free reinforcement learning framework

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Science China(information Sciences) 2025年第2期68卷 170-185页

作者： Xiongtao SHI Yanjie LI Chenglong DU Huiping LI Chaoyang CHEN Weihua GUI Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics School of Mechanical Engineering and AutomationHarbin Institute of Technology (Shenzhen) School of Automation Central South University School of Marine Science and Technology Northwestern Polytechnical University School of Information and Electrical Engineering Hunan University of Science and Technology

In this paper, the cooperative output regulation(COR) problem of a class of unknown heterogeneous multi-agent systems(MASs) with directed graphs is studied via a model-free reinforcement learning(RL) based fully distributed eventtriggered control(ETC) strategy. First, we consider the scenario that the exosystem is accessible globally to all agents, an internal model-based augmented algebraic Riccati equation(AARE) is constructed, and its solution is learned by the proposed model-free RL algorithm via online input-output data. Further, for the scenario that the exosystem is accessible only to its adjacent followers, the distributed observers are designed for each agent to get the state of the exosystem, and an internal modelbased fully distributed adaptive ETC protocol is then synthesized to construct the corresponding AARE, and the feedback gain matrix is learned in a model-free fashion. The model-free RL-based control protocol proposed in this paper can not only remove the prior knowledge of agents' dynamics, but also release the dependence on global information by the adaptive event-triggered mechanism(ETM) and the new graph-based Lyapunov function. Finally, simulation results are illustrated to show the feasibility and effectiveness of the proposed control scheme.

关键词： model-free reinforcement learning unknown heterogeneous multi-agent systems fully distributed event-triggered control directed graph

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Self-Triggered Impulsive Control for Nonlinear Stochastic Systems

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 264-266页

作者： Tao Zhan Yi Ji Yabin Gao Hongyi Li Yuanqing Xia the School of Mathematics and Statistics Southwest University the School of Astronautics Harbin Institute of Technology IEEE the College of Electronic and Information Engineering Southwest University Zhongyuan University of Technology the School of Automation Beijing Institute of Technology

Dear Editor,This letter deals with the stabilization problem of nonlinear stochastic systems via self-triggered impulsive control(STIC), where the timing of impulsive control actions is not dependent on continuous state monitoring. In contrast to the existing self-triggered control method, novel self-triggered mechanism(STM) is proposed by incorporating a waiting time for stabilizing impulses. This allows for direct prediction of the next impulsive instant.

关键词： Stabilization

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