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controllability of neighborhood Corona product networks

Science China(information Sciences) 2024年第12期67卷 327-328页

作者： Bo LIU Xuan LI Qiang ZHANG Junjie HUANG Housheng SU Ministry of Education Key Laboratory for Intelligent Analysis and Security Governance of Ethnic Languages School of Information Engineering Minzu University of China School of Mathematical Sciences Inner Mongolia University Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology

controlling networks aims to study the models, structures,and related dynamics of complex networks. The primary problem of controlling networks is to determine whether they are controllable. Nowadays, controllability has been widely studied and applied to system engineering and control theory, power systems, aerospace, and quantum systems. Various classical criteria include the Gram matrix criterion, Kalman rank criterion, and PBH test.

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Aerospace Relay Health Status Assessment Method Based on SMOTE-Enhanced EBRB 11

Aerospace Relay Health Status Assessment Method Based on SMO...

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11th IEEE International Conference on Behavioural and Social Computing, BESC 2024

作者： Fang, Jiayue Mai, Jiahao Lv, Yan Miao, Shuo He, Wei Beijing institute of control and electronic technology Key laboratory of information system and technology Beijing China Harbin Normal University Harbin China

ISBN: (纸本)9798331531904

The aerospace relay, a complex and precise power transmission system integrating electromagnetic and mechanical elements, holds vital significance for spacecraft safety, rendering its operational condition crucial. Therefore, it is imperative to conduct assessments of the health status of aerospace relays. However, existing models for aerospace relays are beset with issues related to data uncertainty and lack of interpretability of evaluation outcomes. Existing models for aerospace relays grapple with issues surrounding data uncertainty and the uninterpretable nature of their evaluation results. The extended belief rule base (EBRB) is a modeling approach adept at addressing uncertainties, characterized by a lucid inference mechanism and inherently interpretable results. However, current research has overlooked the fact that, during actual operation of aerospace relays, external disturbances and internal influences can lead to an imbalance among health status categories, resulting in a tendency for minority samples to be disregarded during training and rendering accurate evaluations of all categories unattainable. Consequently, this paper proposes an EBRB-based method for assessing the health status of aerospace relays that incorporates Synthetic Minority Over-sampling Technique (SMOTE). Firstly, the sample dataset is balanced using SMOTE. Secondly, an EBRB model is constructed to process the data. Lastly, ensemble reasoning (ER) algorithm is employed for fusion inference. In the experiment, the data processed by this model demonstrated obvious advantages across all performance indicators, indicating the model's powerful efficacy in the health status assessment of aerospace relays. © 2024 IEEE.

关键词： aerospace relay EBRB ER health status assessment SMOTE

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Penetration-Free Collision Detection and Response Algorithm Based on Extended Position-Based Dynamics 6

Penetration-Free Collision Detection and Response Algorithm ...

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6th International Conference on Industrial Artificial Intelligence, IAI 2024

作者： Wang, Minghui Ding, Fan Liao, Chengye Pan, Yangyang Yang, Tao He, Jianping Pingtan Center of Quality Metrology and Testing Pingtan350400 China Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Department of Automation Shanghai200240 China Northeastern University Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China

ISBN: (纸本)9798350356618

In the virtual simulation platform, frequent physical interactions between the robot and the environment poses extremely high demands on the handling of collisions and contacts in the simulation. Current mainstream virtual simulation platforms, in pursuit of real-time performance, have sacrificed some accuracy in simulation algorithms, leading to control strategies obtained in training that are difficult to directly transfer to actual physical machines. This virtual-physical discrepancy has significantly impacted the efficiency and effectiveness of mobile robot simulation. Existing work has attempted to use more accurate mathematical modeling and design more realistic solvers, but at the cost of uncontrollable problem scale and extremely high computational complexity. Starting from this perspective, this paper designs a non-penetration simulation algorithm based on extended Position-Based Dynamics (XPBD), which improves the physical processing accuracy during the simulation while ensuring real-time performance, enhances the fidelity of the simulation, and reduces the virtual-physical discrepancy. Simulation experiments have been conducted to validate the reliability of our proposed algorithm. © 2024 IEEE.

关键词： Virtual environments

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STUDY ON COUPLING FAULT DIAGNOSIS OF THE PLANETARY TRANSMISSION GEARBOX BY USING WAVELET TRANSFORM AND 1DCNN 10

STUDY ON COUPLING FAULT DIAGNOSIS OF THE PLANETARY TRANSMISS...

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10th International Symposium on Test Automation and Instrumentation, ISTAI 2024

作者： Qiu, Baolin Ma, Chao Wang, Shaohong Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing Information Science and Technology Beijing China Beijing Key Lab of Measurement and Control of Mechanical and Electrical System Beijing Information Science and Technology Beijing China

ISBN: (纸本)9781837242672

Considering the susceptibility of planetary transmission to strong noise interference, a fault diagnosis method is proposed based on Morlet wavelet transform and 1D Convolutional Neural Network (1DCNN). Firstly, the acceleration captured from the transmission is dealt with angle resampling to reduce the dependence of speed on signal characteristics, and band-pass filtering is performed, which parameter is determined by using the fast spectral kurtosis algorithm. Subsequently, the filtered data is decomposed into several multiple intrinsic mode function (IMF) components with the EEMD algorithm and reconstructed according to the correlation coefficient parameter. Then reconstructed data is subjected to further noise reduction processing using wavelet transform and the amplitude in the scale parameter with the highest kurtosis as the input to 1DCNN for training and fault diagnosis classification. The experimental results show that the proposed method can effectively reduce noise interference, the fault diagnosis accuracy of the processed data is relatively high. The fault diagnosis accuracy of the processed data is significantly higher than that of the raw data. © The Institution of Engineering & Technology 2024.

关键词： Noise abatement

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Prediction Method of Axial Compression Capacity of CCFST Columns Based on Deep Learning

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2024年第3期51卷 243-251页

作者： Cheng, Yong Tao, Huawei Lu, Xiangying Xu, Zhijun Fu, Hongliang Zhu, Chunhua Key Laboratory of Food Information Processing and Control Ministry of Education Henan University of Technology Zhengzhou450001 China Henan Engineering Laboratory of Grain IOT Technology Henan University of Technology Zhengzhou450001 China Key Laboratory of Food Information Processing and Control Ministry of Education Henan University of Technology Zhengzhou450001 China School of Civil Engineering Henan University of Technology Zhengzhou450001 China Henan Engineering Laboratory of Grain IOT Technology Henan University of Technology Zhengzhou450001 China Henan Engineering Laboratory of Grain IOT Technology Henan University of Technology Zhengzhou450001 China

Accurate prediction of axial compression capacity (ACC) is crucial for evaluating the performance of circular concrete-filled steel tubes (CCFST). Traditional methods face challenges in dealing with the intricate nonlinear relationship inherent in the structure. This paper presents a deep learning (DL) method to predict ACC. The proposed method standardizes parameters, including steel yield strength, column length, and other relevant characteristics of CCFST. Subsequently, a three-layer Long Short-Term Memory (LSTM) network is constructed to capture the complex relationships among these parameters. To enhance the model’s capability for nonlinear fitting, the Rectified Linear Unit (ReLU) activation function is applied between network layers. Additionally, an attention mechanism is introduced to improve feature extraction capabilities. Comprehensive simulations demonstrate that the proposed method surpasses state-of-the-art techniques, achieving optimal values for Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R2). The simulation results validate the efficacy of the proposed method. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Long short-term memory

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Day-ahead Chance-constrained Energy Management of Energy Hubs:A Distributionally Robust Approach

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CSEE Journal of Power and Energy systems 2022年第3期8卷 812-825页

作者： Jiaxin Cao Bo Yang Shanying Zhu Chao Ning Xinping Guan Department of Automation Shanghai Jiao Tong UniversityShanghai200240China Key Laboratory of System Control and Information Processing Ministry of Education of China Robert Frederick Smith School of Chemical and Biomolecular Engineering Cornell UniversityIthacaNY 14853 USA

The day-ahead management schedules of hybrid energy hubs are intricate and usually exposed to various uncertainties with the penetration of renewable sources and different ***,it is difficult to access to precise probability distribution functions and exact moment information of uncertain *** cope with these issues,an energy management scheme based on the distributionally robust optimization approach is developed for the energy *** makes no assumptions of certain probability distributions and can be implemented with limited empirical data and partial information of underlying *** operational strategy can provide decision makers with a preliminary and robust optimal solution in the day-ahead *** results illustrate the economical benefit of the energy model,and the effectiveness of the proposed approach in chance-constrained energy management is demonstrated by comparing with other *** Terms-Chance constraint,distributionally robust optimization,energy hub,energy management.

关键词： Chance constraint distributionally robust optimization energy hub energy management

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HPHR-SAR-Net: Hyperpixel High-Resolution SAR Imaging Network Based on Nonlocal Total Variation

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2023年 16卷 8595-8608页

作者： Zhou, Guoru Xu, Zhongqiu Fan, Yizhe Zhang, Zhe Qiu, Xiaolan Zhang, Bingchen Fu, Kun Wu, Yirong Chinese Academy of Sciences Key Laboratory of Technology in Geo-spatial Information Processing and Application System Beijing100045 China University of Chinese Academy of Sciences Aerospace Information Research Institute Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering Beijing100045 China Suzhou Aerospace Information Research Institute Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology Suzhou215009 China

High resolution is a key trend in the development of synthetic aperture radar (SAR), which enables the capture of fine details and accurate representation of backscattering properties. However, traditional high-resolution SAR imaging algorithms face several challenges. First, these algorithms tend to focus on local information, neglecting nonlocal information between different pixel patches. Second, speckle is more pronounced and difficult to filter out in high-resolution SAR images. Third, the process of high-resolution SAR imaging generally involves high time and computational complexity, making real-time imaging difficult to achieve. To address these issues, we propose a hyperpixel high-resolution SAR imaging network (HPHR-SAR-Net) for rapid despeckling in high-resolution modes. Based on the concept of hyperpixel techniques, we initially combine nonconvex and nonlocal total variation as compound regularization. This approach more effectively despeckles and manages the relationship between pixels while reducing bias effects caused by convex constraints. Subsequently, we solve the compound regularization model using the alternating direction method of multipliers (ADMM) algorithm and unfold it into a deep unfolding network (DUN). The network's parameters are adaptively learned in a data-driven manner, and the learned network significantly increases imaging speed. Additionally, the proposed HPHR-SAR-Net is compatible with high-resolution imaging modes, such as spotlight, staring spotlight, and sliding spotlight. In this article, we demonstrate the superiority of HPHR-SAR-Net through experiments in both simulated and real SAR scenarios. The results indicate that HPHR-SAR-Net can rapidly perform high-resolution SAR imaging from raw echo data, producing accurate imaging results. © 2008-2012 IEEE.

关键词： Radar imaging

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An Enhanced Particle Swarm Algorithm Based on Fitness Landscape information 13

An Enhanced Particle Swarm Algorithm Based on Fitness Landsc...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Diao, Zhenya Yu, Fei Guan, Jian Liu, Meitong School of Physics and Information Engineering Minnan Normal University Key Lab of Intelligent Optimization and Information Processing Zhangzhou36300 China School of Physics and Information Engineering Minnan Normal University Key Laboratory of Light Field Manipulation and System Integration Applications in Fujian Province Key Lab of Intelligent Optimization and Information Processing Zhangzhou36300 China

ISBN: (纸本)9798350308365

In the original Particle Swarm Optimization (PSO), the selection of parameters significantly impacts the search capability of PSO. The paper introduces an innovative algorithm called Fitness Landscape-based PSO (FLPSO) to address the impact of parameter selection on the search capability of PSO. The algorithm focuses on acquiring fitness landscape information and analyzing the population state to intelligently select appropriate parameters in each generation. FLPSO defines four primary states: exploration, exploitation, convergence, and escaping state. In the convergence state, an elite learning strategy is incorporated to help the algorithm escape potential local optima by targeting the global best particle. To validate the efficacy of FLPSO, the paper conducted experiments on 30 different characteristic test functions from CEC2017. FLPSO was compared with six existing algorithms: PSO, PSO-TVAC, APSO, AWPSO, RPSO and ASRPPSO. The experimental results showed significant improvements in FLPSO, providing promising evidence for its effectiveness. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Data secure transmission intelligent prediction algorithm for mobile industrial IoT networks

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Digital Communications and Networks 2023年第2期9卷 400-410页

作者： Lingwei Xu Hao Yin Hong Jia Wenzhong Lin Xinpeng Zhou Yong Fu Xu Yu Department of Information Science and Technology Qingdao University of Science and TechnologyQingdao266061China Fujian Key Laboratory of Sensing and Computing for Smart Cities Xiamen UniversityXiamen361005China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control(Minjiang University) Fuzhou350121China Faculty of Information Science and Engineering Ocean University of ChinaQingdao266100China Shandong Provincial Key Laboratory of Computer Networks Shandong Computer Science Center(National Supercomputer Center in Jinan)JinanChina

Mobile Industrial Internet of Things(IIoT)applications have achieved the explosive growth in recent *** mobile IIoT has flourished and become the backbone of the industry,laying a solid foundation for the interconnection of all *** variety of application scenarios has brought serious challenges to mobile IIoT networks,which face complex and changeable communication *** data secure transmission is critical for mobile IIoT *** paper investigates the data secure transmission performance prediction of mobile IIoT *** cut down computational complexity,we propose a data secure transmission scheme employing Transmit Antenna Selection(TAS).The novel secrecy performance expressions are first ***,to realize real-time secrecy analysis,we design an improved Convolutional Neural Network(CNN)model,and propose an intelligent data secure transmission performance prediction *** mobile signals,the important features may be removed by the pooling *** will lead to negative effects on the secrecy performance prediction.A novel nine-layer improved CNN model is *** of the input and output layers,it removes the pooling layer and contains six convolution ***,Back-Propagation(BP)and LeNet methods are employed to compare with the proposed *** simulation analysis,good prediction accuracy is achieved by the CNN *** prediction accuracy obtains a 59%increase.

关键词： Mobile IIoT networks Data secure transmission Performance analysis Intelligent prediction Improved CNN

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A Cross-Multi-Domain Trust Assessment Authority Delegation Method Based on Automotive Industry Chain

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Computers, Materials & Continua 2025年第1期82卷 407-426页

作者： Binyong Li Liangming Deng Jie Zhang Xianhui Deng School of Cybersecurity(Xin Gu Industrial College) Chengdu University of Information TechnologyChengdu610225China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610225China SUGON Industrial Control and Security Center Chengdu610225China

To solve the challenges of connecting and coordinating multiple platforms in the automotive industry and to enhance collaboration among different participants,this research focuses on addressing the complex supply relationships in the automotive market,improving data sharing and interactions across various platforms,and achieving more detailed integration of data and *** propose a trust evaluation permission delegation method based on the automotive industry *** proposed method combines smart contracts with trust evaluation mechanisms,dynamically calculating the trust value of users based on the historical behavior of the delegated entity,network environment,and other factors to avoid malicious node attacks during the permission delegation *** also introduce strict control over the cross-domain permission granting and revocation mechanisms to manage the delegation path,prevent information leakage caused by malicious node interception,and effectively protect data integrity and *** analysis shows that this method meets the realtime requirements of collaborative interaction in the automotive industry chain and provides a feasible solution to permission delegation issues in the automotive industry chain,offering dynamic flexibility in authorization and scalability compared to most existing solutions.

关键词： Automotive industry chain cross-domain access block chain delegate authority trust assessment

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