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IEEE Transactions on Artificial Intelligence 2024年第8期5卷 4013-4025页

作者： Wang, Haoyu Liu, Xiaomin the Engineering Research Center of Intelligent Control for Underground Space Ministry of Education China University of Mining and Technology Xuzhou 221116 China and also with the School of Information and Control Engineering China University of Mining and Technology Xuzhou 221116 China

Affected by the sensor, shooting environment, and other aspects, hyperspectral images (HSIs) in the source and target domains exhibit phenomenon of difficult feature extraction and domain shift. The above phenomena pose challenges to the cross-scene HSI classification task. Therefore, a focal transfer graph network (FTGN) for cross-scene HSI classification is proposed. First, FTGN leverages graph sample and aggregate (GraphSAGE) to capture spatial-spectral features by aggregating partial adjacency nodes, ensuring the acquisition of contextual information. The neighbor weighting strategy based on spatial-spectral information is proposed to solve the information interference caused by excessive node aggregation. Second, a pseudolabel trimming strategy based on class metrics is proposed to reduce the adverse effects of pseudolabel noise in the transfer process. Then, a specification subdomain adaptation (SSA) module is proposed, which helps to achieve effective distribution alignment by reducing the feature distance of intraclass samples and widening the feature distance of interclass samples during the subdomain adaptation process. Finally, the focal loss is utilized to help FTGN focus on hard-to-classify samples. The experimental results on eight data pairs show that the proposed method outperforms several state-of-the-art methods. © 2020 IEEE.

关键词： Data mining

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Key Technologies for Machine Vision for Picking Robots:Review and Benchmarking

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Machine Intelligence Research 2025年第1期22卷 2-16页

作者： Xu Xiao Yiming Jiang Yaonan Wang College of Electrical and Information Engineering Hunan UniversityChangsha 410082China National Engineering Research Center for Robot Vision Perception and Control Technology Hunan UniversityChangsha 410082China

The increase in precision agriculture has promoted the development of picking robot technology,and the visual recognition system at its core is crucial for improving the level of agricultural *** paper reviews the progress of visual recognition tech-nology for picking robots,including image capture technology,target detection algorithms,spatial positioning strategies and scene *** article begins with a description of the basic structure and function of the vision system of the picking robot and em-phasizes the importance of achieving high-efficiency and high-accuracy recognition in the natural agricultural ***-sequently,various image processing techniques and vision algorithms,including color image analysis,three-dimensional depth percep-tion,and automatic object recognition technology that integrates machine learning and deep learning algorithms,were *** the same time,the paper also highlights the challenges of existing technologies in dynamic lighting,occlusion problems,fruit maturity di-versity,and real-time processing *** paper further discusses multisensor information fusion technology and discusses methods for combining visual recognition with a robot control system to improve the accuracy and working rate of *** the same time,this paper also introduces innovative research,such as the application of convolutional neural networks(CNNs)for accurate fruit detection and the development of event-based vision systems to improve the response speed of the *** the end of this paper,the future development of visual recognition technology for picking robots is predicted,and new research trends are proposed,including the refinement of algorithms,hardware innovation,and the adaptability of technology to different agricultural *** purpose of this paper is to provide a comprehensive analysis of visual recognition technology for researchers and practitioners in the field of agricul-tural rob

关键词： Picking robot visual system perception technology image processing machine learning deep learning.

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Model-Based Cross-Scale Reinforcement Learning Optimal control 6

Model-Based Cross-Scale Reinforcement Learning Optimal Contr...

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6th International Conference on Electronic engineering and Informatics, EEI 2024

作者： Li, Gonghe Zhou, Linna Liu, Xiaomin Yang, Chunyu School of Information and Control Engineering China University of Mining and Technology Xuzhou China China University of Mining and Technology Faculty of School of Information and Control Engineering Xuzhou China

ISBN: (纸本)9798350353594

A model-based cross-scale reinforcement learning optimal control method is proposed for a class of nonlinear singularly perturbed systems. Initially, according to the singular perturbation theory, the original singularly perturbed system is decomposed into fast and slow subsystems, thereby transforming the original optimal control problem into optimal control problems for the fast and slow subsystems. A cross-scale information interaction mechanism is proposed, where full-order system state data is sampled to update the initial values of the fast and slow subsystems. This approach helps overcome the limitations associated with the online solving of rigid equations and reduce the cumulative errors. To approximate the solution of the Hamilton-Jacobi-Bellman (HJB) equations for the fast and slow subsystems, neural networks (NNs) are employed to approximate the cost functions within iterative processes. Simulation resultst validated the effectiveness of the proposed method. © 2024 IEEE.

关键词： cross-scale information interaction Nonlinear singularly perturbed systems rein-forcement learning

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Survey and Prospect for Applying Knowledge Graph in Enterprise Risk Management

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Computers, Materials & Continua 2024年第3期78卷 3825-3865页

作者： Pengjun Li Qixin Zhao Yingmin Liu Chao Zhong Jinlong Wang Zhihan Lyu School of Information and Control Engineering Qingdao University of TechnologyQingdao266520China Department of Game Design Uppsala UniversityVisby62167Sweden

Enterprise risk management holds significant importance in fostering sustainable growth of businesses and in serving as a critical element for regulatory bodies to uphold market *** the challenges posed by intricate and unpredictable risk factors,knowledge graph technology is effectively driving risk management,leveraging its ability to associate and infer knowledge from diverse *** review aims to comprehensively summarize the construction techniques of enterprise risk knowledge graphs and their prominent applications across various business ***,employing bibliometric methods,the aim is to uncover the developmental trends and current research hotspots within the domain of enterprise risk knowledge *** the succeeding section,systematically delineate the technical methods for knowledge extraction and fusion in the standardized construction process of enterprise risk knowledge *** comparing and summarizing the strengths and weaknesses of each method,we provide recommendations for addressing the existing challenges in the construction ***,categorizing the applied research of enterprise risk knowledge graphs based on research hotspots and risk category standards,and furnishing a detailed exposition on the applicability of technical routes and ***,the future research directions that still need to be explored in enterprise risk knowledge graphs were discussed,and relevant improvement suggestions were *** and researchers can gain insights into the construction of technical theories and practical guidance of enterprise risk knowledge graphs based on this foundation.

关键词： Knowledge graph enterprise risk risk identification risk management review

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Developer Cooperation Relationship and Attribute Similarity Based Community Detection in Software Ecosystem

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Chinese Journal of Electronics 2023年第1期32卷 39-50页

作者： SHEN Xin DU Junwei GONG Dunwei YAO Xiangjuan School of Information and Control Engineering China University of Mining and Technology School of Information Science and Technology Qingdao University of Science and Technology School of Mathematics China University of Mining and Technology

A software ecosystem(SECO) can be described as a special complex network. Previous complex networks in an SECO have limitations in accurately reflecting the similarity between each pair of nodes. The community structure is critical towards understanding the network topology and function. Many scholars tend to adopt evolutionary optimization methods for community detection. The information adopted in previous optimization models for community detection is incomprehensive and cannot be directly applied to the problem of community detection in an SECO. Based on this, a complex network in SECOs is first built. In the network, the cooperation intensity between developers is accurately calculated, and the attribute contained by each developer is considered. A multi-objective optimization model is formulated. A community detection algorithm based on NSGA-II is employed to solve the above model. Experimental results demonstrate that the proposed method of calculating the developer cooperation intensity and our model are advantageous.

关键词： Biological system modeling Ecosystems Optimization methods Complex networks Software Detection algorithms

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Research on Performance Optimization of Spark Distributed Computing Platform

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Computers, Materials & Continua 2024年第5期79卷 2833-2850页

作者： Qinlu He Fan Zhang Genqing Bian Weiqi Zhang Zhen Li School of Information and Control Engineering Xi’an University of Architecture and TechnologyXi’an710054China Shaanxi Institute of Metrology Science Xi’an710043China

Spark,a distributed computing platform,has rapidly developed in the field of big *** in-memory computing feature reduces disk read overhead and shortens data processing time,making it have broad application prospects in large-scale computing applications such as machine learning and image ***,the performance of the Spark platform still needs to be *** a large number of tasks are processed simultaneously,Spark’s cache replacementmechanismcannot identify high-value data partitions,resulting inmemory resources not being fully utilized and affecting the performance of the Spark *** address the problem that Spark’s default cache replacement algorithm cannot accurately evaluate high-value data partitions,firstly the weight influence factors of data partitions are modeled and ***,based on this weighted model,a cache replacement algorithm based on dynamic weighted data value is proposed,which takes into account hit rate and data *** integration and usage strategies are implemented based on LRU(LeastRecentlyUsed).Theweight update algorithm updates the weight value when the data partition information changes,accurately measuring the importance of the partition in the current job;the cache removal algorithm clears partitions without useful values in the cache to releasememory resources;the weight replacement algorithm combines partition weights and partition information to replace RDD partitions when memory remaining space is ***,by setting up a Spark cluster environment,the algorithm proposed in this paper is experimentally *** have shown that this algorithmcan effectively improve cache hit rate,enhance the performance of the platform,and reduce job execution time by 7.61%compared to existing improved algorithms.

关键词： Spark memory optimization memory replacement strategy

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Fractional core-based collapse mechanism and structural optimization in complex systems

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Science China(information Sciences) 2023年第9期66卷 168-183页

作者： Shubin SI Changchun LV Zhiqiang CAI Dongli DUAN Jurgen KURTHS Zhen WANG School of Mechanical Engineering Northwestern Polytechnical University Key Laboratory of Industrial Engineering and Intelligent Manufacturing(Ministry of Industry and Information Technology) School of Information and Control Engineering Xi'an University of Architecture and Technology Instiute of Physics Humboldt-University Potsdam Institute for Climate Impact Research

Catastrophic and major disasters in real-world systems ranging from financial markets and ecosystems, often show generic early-warning signals that may indicate a collapse. Hence, understanding the collapse mechanism of a complex network and predicting its process are of uttermost importance. However, these challenges are often hindered by the extremely high dimensionality of the underlying *** present here the concept of the fractional core(F-core) that considers the contribution of the network topology and dynamics to systematically analyze the collapse process in such networks, and encompass a broad range of dynamical systems, from mutualistic ecosystems to regulatory dynamics. We offer testable predictions on the tipping point, and, in particular, prove that the extinction of the maximum F-core of a network is an efficient indicator of whether a system completely collapses. The results show that the death of species or cells in a low-order F-core may improve the average density and have little influence on the tipping point. Generally, the principle of the F-core demonstrates how complex systems collapse and opens an innovative optimization strategy to uncover the optimal structure of systems.

关键词： collapse mechanism tipping point structural optimization complex systems phase transition

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Policy Gradient Adaptive Dynamic Programming for Model-Free Multi-Objective Optimal control

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 1060-1062页

作者： Hao Zhang Yan Li Zhuping Wang Yi Ding Huaicheng Yan the Department of Control Science and Engineering Tongji UniversityShanghai 200092China the Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education School of Information Science and EngineeringEast China University of Science and TechnologyShanghai 200237China

Dear Editor,In this letter,the multi-objective optimal control problem of nonlinear discrete-time systems is investigated.A data-driven policy gradient algorithm is proposed in which the action-state value function is... 详细信息

关键词： policy gradient Optimal

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Displacement Self-sensing control of Permanent Magnet Assisted Bearingless Synchronous Reluctance Motor Based on BP Neural Network Optimized by Improved PSO

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Progress In Electromagnetics Research C 2024年 143卷 189-198页

作者： Wang, Jing Liu, Gai Zhu, Huangqiu School of Electrical and Information Engineering Jiangsu University Zhenjiang212013 China School of Electrical and Control Engineering Xuzhou University of Technology Xuzhou221018 China

In order to solve the problems of low reliability, low integration, and high cost brought by mechanical sensors in the control system of permanent magnet-assisted bearingless synchronous reluctance motor (PMa-BSynRM), a displacement self-sensing method of the back propagation (BP) neural network left-inverse system under the optimization of an improved particle swarm algorithm is proposed. Firstly, the working principle of PMa-BSynRM is introduced, and the mathematical model of PMa-BSynRM is derived. Secondly, the suspension force model is established to prove the left reversibility of the PMa-BSynRM displacement subsystem on the basis of the observation principle of the left reversible system. Thirdly, the weights of BP neural network are optimized by using the improved particle swarm algorithm to avoid local optimum, and the final weights are obtained to complete the construction of the displacement self-detection control system. On this basis, velocity change and anti-interference simulations are conducted to prove the tracking performance of the displacement system. Finally, static suspension, velocity change and anti-interference experiments are executed which verify the accuracy and feasibility of the proposed displacement self-detection system. © 2024, Electromagnetics Academy. All rights reserved.

关键词： Permanent magnets

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Multiobjective Differential Evolution for Higher-Dimensional Multimodal Multiobjective Optimization

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1458-1475页

作者： Jing Liang Hongyu Lin Caitong Yue Ponnuthurai Nagaratnam Suganthan Yaonan Wang School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou 450001 School of Electrical Engineering and Automation Henan Institute of TechnologyXinxiang 453003China School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou 450001China KINDI Center for Computing Research Qatar UniversityDohaQatarand also with the Nanyang Technology UniversitySingapore College of Electrical and Information Engineering and also with the National Engineering Research Center for Robot Visual Perception and Control TechnologyHunan UniversityChangsha 410082China IEEE

In multimodal multiobjective optimization problems(MMOPs),there are several Pareto optimal solutions corre-sponding to the identical objective *** paper proposes a new differential evolution algorithm to solve MMOPs with higher-dimensional decision *** to the increase in the dimensions of decision variables in real-world MMOPs,it is diffi-cult for current multimodal multiobjective optimization evolu-tionary algorithms(MMOEAs)to find multiple Pareto optimal *** proposed algorithm adopts a dual-population framework and an improved environmental selection *** utilizes a convergence archive to help the first population improve the quality of *** improved environmental selection method enables the other population to search the remaining decision space and reserve more Pareto optimal solutions through the information of the first *** combination of these two strategies helps to effectively balance and enhance conver-gence and diversity *** addition,to study the per-formance of the proposed algorithm,a novel set of multimodal multiobjective optimization test functions with extensible decision variables is *** proposed MMOEA is certified to be effective through comparison with six state-of-the-art MMOEAs on the test functions.

关键词： Benchmark functions diversity measure evolution-ary algorithms multimodal multiobjective optimization.

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