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10th Symposium on Novel Optoelectronic Detection Technology and Applications

作者： Zhang, Xin Li, Sining Sun, Jianfeng Jiang, Peng Jin, Haowei National Key Laboratory of Laser Spatial Information Institute of Opto-Electronic Harbin Institute of Technology Heilongjiang Harbin150001 China Zhengzhou Research Institute of Harbin Institute of Technology Henan Zhengzhou450000 China National Key Laboratory of Complex System Control and Intelligent Agent Cooperation Beijing100074 China

ISBN: (数字)9781510688155

ISBN: (纸本)9781510688148

This study proposes a target extraction algorithm for array GM-APD lidar based on hybrid domain features, designed to address the challenges of accurately extracting target region based on the image in complex scenes and high noise interference. We extracted the temporal features of the echo signals and transformed the echo signals into the image domain to enhance the information content and further extract image features. By integrating multi-view K-means clustering and AdaBoost classification methods, we introduced a dynamic weight adjustment mechanism and a region adjustment method based on a correlation coefficient to achieve pixel-type discrimination and target region extraction. Experimental results demonstrate that the proposed method exhibits good robustness and adaptability in complex detection environments. Under conditions of different occlusion, different signal-to-noise ratios, and different target reflectivity, the proposed algorithm significantly outperforms traditional region-growing segmentation methods in terms of accuracy of target region extraction. © 2025 SPIE.

关键词： K-means clustering

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Grid Strength Assessment Method for Evaluating Small-signal Synchronization Stability of Grid-following and Grid-forming Converters Integrated systems

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Journal of Modern Power systems and Clean Energy 2025年第1期13卷 55-65页

作者： Qianhong Shi Wei Dong Guanzhong Wang Junchao Ma Chenxu Wang Xianye Guo Vladimir Terzija Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education School of Electrical EngineeringShandong UniversityJinan 250061China Electric Power Research Institute State Grid Zhejiang Electric Power Co.Ltd.Hangzhou 310014China School of Engineering Newcastle UniversityNewcastle upon TyneUK

Oscillations caused by small-signal instability have been widely observed in AC grids with grid-following(GFL)and grid-forming(GFM)*** generalized short-circuit ratio is commonly used to assess the strength of GFL converters when integrated with weak AC systems at risk of *** paper provides the grid strength assessment method to evaluate the small-signal synchronization stability of GFL and GFM converters integrated ***,the admittance and impedance matrices of the GFL and GFM converters are analyzed to identify the frequency bands associated with negative damping in oscillation modes dominated by heterogeneous synchronization ***,based on the interaction rules between the short-circuit ratio and the different oscillation modes,an equivalent circuit is proposed to simplify the grid strength assessment through the topological transformation of the AC *** risk of sub-synchronization and low-frequency oscillations,influenced by GFL and GFM converters,is then reformulated as a semi-definite programming(SDP)model,incorporating the node admittance matrix and grid-connected device *** effectiveness of the proposed method is demonstrated through a case analysis.

关键词： Grid-following converter grid-forming converter grid strength assessment small-signal stability

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Mechanism and Methods for Fault Recovery of Grid-Forming Inverter by Graphical Illustration

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IEEE Transactions on Power systems 2025年第2期40卷 1973-1976页

作者： Yang, Jialun Ye, Hua Wang, Yantuan Liu, Yutian Li, Yujun Du, Zhengchun Shandong Research Institute of Industrial Technology Jinan250061 China Shandong University Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Jinan250061 China Xi'an Jiaotong University Department of Electrical Engineering Xi'an710049 China

The existing priority-based current limiting control (CLC) for grid-forming (GFM) inverters may lead to failures in fault recovery, including being locked in CLC and mode oscillation between CLC and constant voltage control (CVC). To resolve these problems, this letter first reveals the fault recovery mechanism of droop controlled GFM inverters by illustrating the conditions of three fault recovery states in the phasor diagram. Then, two methods including determination of the optimal range of current angle and upper limit of saturation block are presented to guarantee successful fault recovery of GFM inverters. The correctness of theoretical analyses is verified and validated by multiple experimental tests. © 1969-2012 IEEE.

关键词： Fault current limiters

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Bearing Fault Diagnosis Based on an Improved Morphological Filter

Bearing Fault Diagnosis Based on an Improved Morphological F...

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TEPEN International Workshop on Fault Diagnostics and Prognostics, TEPEN-IWFDP 2024

作者： Ren, Xueping Guo, Liangjian Qiu, Fuqiang Liu, Tongtong School of Mechanical Engineering Inner Monglia University of Science and Technology Baotou014010 China Inner Monglia Key Laboratory of Intelligent Diagnosis and Control of Mechatronic System Baotou014010 China

ISBN: (纸本)9783031734069

Extraction of repetitive shock signals from vibration signals is crucial in bearing fault detection. Morphological filtering can extract the signal components based on the geometric features of the signal. In this paper, we propose an improved morphological filter for extracting fault cycle pulses from vibration signals in the time domain. Morlet wavelets are used as the main waveforms of the structural elements, which guide the construction of the structural elements based on their correlation with the defect-induced pulse, the Teager energy operator (TEO) is used to reduce the modulated signal interference. The improved morphological filtering algorithm can obtain fault features from low signal-to-noise ratio signals. The processing results of one simulated signal and two sets of experimental signals verify the effectiveness and robustness of the method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Wiener filtering

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Fault Diagnosis of Rolling Bearing in Broad Learning system Based on Multi-domain Feature Selection

Fault Diagnosis of Rolling Bearing in Broad Learning System ...

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TEPEN International Workshop on Fault Diagnostics and Prognostics, TEPEN-IWFDP 2024

作者： Ouyang, Bing Zhang, Chao Wu, Le Liu, Guiyi Wu, Yangbiao Xu, Shuai School of Mechanical Engineering Inner Monglia University of Science and Technology Baotou014010 China Inner Monglia Key Laboratory of Intelligent Diagnosis and Control of Mechatronic System Baotou014010 China

ISBN: (纸本)9783031734069

This paper presents a novel solution for bearing fault diagnosis based on a Broad Learning system with multi-domain feature selection. Currently, the diagnosis of bearing faults is beset with numerous challenges, including operation under complex and fluctuating conditions, the subtlety of early fault indicators, noise interference in measurement signals, variations in operational conditions, and a broad spectrum of potential fault types. These factors collectively contribute to the complexity of accurately identifying and classifying bearing faults. The Broad Learning system introduced in this paper, by integrating multi-domain feature selection, enhances the model's capability to extract and utilize relevant features from noisy or incomplete data, potentially increasing diagnostic accuracy in suboptimal sensor environments. By leveraging features across multiple domains, the method based on the Broad Learning system can capture a wider array of fault characteristics, thereby broadening its applicability across various types of bearing faults and operational conditions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Roller bearings

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Fully Distributed Output Regulation of Linear Discrete-Time Multi-Agent systems With Markov Switching Topology

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IEEE Transactions on Circuits and systems I: Regular Papers 2025年 1-9页

作者： Liu, Jiamin Li, Zhao-Yan Zhou, Bin Jimei University School of Science Xiamen361021 China Harbin Institute of Technology School of Mathematics Harbin150001 China Zhengzhou Research Institute of HIT Zhengzhou450000 China Harbin Institute of Technology Center for Control Theory and Guidance Technology Harbin150001 China National Key Laboratory of Complex System Control and Intelligent Agent Cooperation Harbin150001 China

This paper studies the stochastic cooperative output regulation problem for linear discrete-time multi-agent systems (MASs) with Markov switching topologies and communication/input delays. First, a moment stability criterion for an interconnected time-homogeneous Markov jump linear systems is builded. Then, based on the moment stability criterion, a fully distributed output regulation protocol is designed to solve the stochastic cooperative output regulation problem successively. Finally, a numerical simulation is provided to validate the efficiency of the designed protocol. © 2004-2012 IEEE.

关键词： Topology

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NO_(2) gas sensor with high selectivity and fast response based on Pt-loaded nanoporous GaN

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International Journal of Minerals,Metallurgy and Materials 2025年第4期32卷 964-972页

作者： Dan Han Xiaoru Liu Donghui Li Jiexu Shi Yu Wang Yuxuan Wang Hongtao Wang Shengbo Sang Shanxi Key Laboratory of Micro/Nano Sensors&Artificial Intelligence Perception College of Integrated CircuitsTaiyuan University of TechnologyTaiyuan030024China Key Lab of Advanced Transducers and Intelligent Control System of the Ministry of Education Taiyuan University of TechnologyTaiyuan 030024China Shanxi Research Institute of 6D Artificial Intelligence Biomedical Science Taiyuan 030032China

In this work,we realized a room-temperature nitrogen dioxide(NO_(2))gas sensor based on a platinum(Pt)-loaded nanoporous gallium nitride(NP-GaN)sensing material using the thermal reduction method and coreduction with the catalysis of *** gas sensor gained excellent sensitivity to NO_(2) at a concentration range of 200 ppm to 100 ppb,benefiting from the loading of Pt nanoparticles,and exhibited a short response time(22 s)and recovery time(170 s)to 100 ppm of NO_(2) at room temperature with excellent selectivity to NO_(2) compared with other *** phenomenon was attributed to the spillover effect and the synergic electronic interaction with semiconductor materials of Pt,which not only provided more electrons for the adsorption of NO_(2) molecules but also occupied effective sites,causing poor sites for other *** low detection limit of Pt/NP-GaN was 100 ppb,and the gas sensor still had a fast response 70 d after ***,the gas-sensing mechanism of the gas sensor was further elaborated to determine the reason leading to its improved *** significant spillover impact and oxygen dissociation of Pt provided advantages to its synergic electronic interaction with semiconductor materials,leading to the improvement of the gas properties of gas sensors.

关键词： nanoporous gallium nitride platinum nitrogen dioxide gas sensor

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Effect of Free-End Torsion on the Corrosion and Mechanical Properties for Mg-3Zn-0.2Ca Alloy

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Acta Metallurgica Sinica(English Letters) 2025年第1期38卷 59-70页

作者： Li-Lan Gao Jiang Ma Yan-Song Tan Xiao-Hao Sun Qi-Jun Gao De-Bao Liu Chun-Qiu Zhang Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical EngineeringTianjin University of TechnologyTianjin300384China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of TechnologyTianjin300384China School of Materials Science and Engineering Tianjin University of TechnologyTianjin300384China

Magnesium alloys with excellent degradability and biocompatibility are promising materials for biomedical implants,saving patients the burden of second ***,their mechanical properties and corrosion resistance are significantly below the requirements for implant *** study aims to improve the mechanical and corrosion resistance properties of Mg-3Zn-0.2Ca alloy by pre-torsion treatment,and find out the optimal shear *** rod-shaped Mg-3Zn-0.2Ca alloy specimens were pre-torsion treated at different torsion *** effect of free-end torsion on Mg-3Zn-0.2Ca alloy was characterized through microstructure analysis,mechanical testing,and corrosion ***-torsion treatment can refine grain and induce twins and dislocations in Mg-3Zn-0.2Ca *** surface hardness increases with the increase in torsion *** yield strength and ultimate strength initially increase and then decrease with increasing torsion angle,while ductility decreases with increasing torsion *** with a shear strain of 30%exhibit the highest tensile strength,reaching 284.78±10.62 MPa,with an elongation of 19.37±1.66%.Furthermore,they show a significant improvement in fatigue lives both before and after *** the stress amplitude is 120 MPa,the fatigue lives for specimens without pre-torsion treatment are 54,275 cycles and 4324 cycles before and after pre-corrosion,while they increased significantly to 92,015 cycles and 5050 cycles with the 30%shear strain,***,the 30%shear strain specimens show a significant reduction in corrosion *** conclusion,pre-torsion treatment can effectively modify the microstructure of Mg-3Zn-0.2Ca alloy,enhancing both mechanical properties and corrosion *** optimal shear strain for this improvement is 30%.This study provides a practical method to enhance the mechanical properties and corrosion resistance of Mg-3Zn-0.2Ca alloy,making it more suitable for biomedical implan

关键词： Magnesium alloy Pre-torsion Microstructure Mechanical property Corrosion resistance

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An online self-organizing radial basis function neural network based on Gaussian Membership

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Applied Intelligence 2025年第6期55卷 1-17页

作者： Jia, Lijie Li, Wenjing Qiao, Junfei Zhang, Xinliang School of Electrical Engineering and Automation Henan International Joint Laboratory of Direct Drive and Control of Intelligent Equipment Henan Polytechnic University Jiaozuo454003 China Faculty of Information Technology Beijing University of Technology No. 100 Pingleyuan Chaoyang District Beijing100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System No. 100 Pingleyuan Chaoyang District Beijing100124 China

Radial basis function neural network (RBFNN) is one of the most popular neural networks, and an appropriate selection of its structure and learning algorithms is crucial for its performance. Aiming to alleviate the sensitivity of the RBFNN to its parameters and improve the overall performance of the network, this study proposes a Gaussian Membership-based online self-organizing RBF neural network (GM-OSRBFNN). First, the Gaussian Membership is introduced to enhance network insensitivity to network parameters and used as a similarity metric to indicate the similarity between the sample to a hidden neuron and that between hidden neurons. Second, the similarity metric is used to design the neuron addition and merging rules to achieve a self-organizing network structure, and error constraints are introduced to the neuron addition rule;also, the noisy neuron deletion rule is defined to make the network structure more compact. In addition, an online fixed mini-batch gradient algorithm is used for online learning of network parameters, which can guarantee fast and stable convergence of the network. Finally, the proposed GM-OSRBFNN is tested on common nonlinear system modeling problems to verify its effectiveness. The experimental results show that compared to the existing models, the GM-OSRBFNN can achieve competitive prediction performance with a more compact network structure, faster convergence speed, and, more importantly, better insensitivity to network parameters. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Radial basis function networks

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Deep Learning and Projection Neural Network With Finite-Time Convergence for Energy Management of Multi-Energy system

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IEEE Transactions on Smart Grid 2025年第3期16卷 2156-2168页

作者： Liu, Xueying He, Xing Li, Chaojie Huang, Tingwen Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Chongqing400715 China North China Electric Power University State Key Laboratory of Alternate Electrical Power System With Renewable Energy Sources Beijing102206 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

In this paper, an approach based on projection neural network (PNN), sliding mode control technique, and deep learning is proposed to solve the energy management problem of multi-energy systems (MES) containing dynamic parameters. First, the sliding mode technique is introduced in the PNN to design a finite-time PNN (FTPNN). The stability and finite-time convergence of the proposed FTPNN are proved by the Lyapunov method and the setting time bound is given. Then, the deep FTPNN (DFTPNN) is designed by combining deep learning with the proposed FTPNN. The dynamic parameters in the MES that change over time are used as input variables for the DFTPNN, allowing the trained DFTPNN to respond immediately to changes in the dynamic parameters and to predict the solutions of the FTPNN with different parameters directly. Simulation experiments show that FTPNN has faster convergence compared to PNN. DFTPNN significantly reduces the computation time compared to FTPNN. DFTPNN provides predicted solutions to FTPNN. Since DFTPNN can respond immediately to changes in dynamic parameters and directly provide energy management strategies under different parameters, it can adapt to changing environments and promote the economic and stable operation of MES. © 2010-2012 IEEE.

关键词： Cogeneration Heuristic algorithms Convergence Deep learning Prediction algorithms Optimization Heating systems Energy management Approximation algorithms Power system stability

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