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Computer communication and Artificial Intelligence (CCAI), International Conference on

作者： Qiankun Cheng Rongen Dong Wenlong Cai Ruiqi Liu Feng Shu Jiangzhou Wang Hainan University School of Information and Communication Engineering Haikou China National Key Laboratory of Science and Technology on Aerospace Intelligence Control Beijing Aerospace Automatic Control Institute Beijing China Wireless and Computing Research Institute ZTE Corporation Beijing China University of Kent School of Engineering Canterbury U.K.

ISBN: (数字)9798350362763

ISBN: (纸本)9798350362770

Due to its ability of overcoming the impact of double-fading effect, active intelligent reflecting surface (IRS) has attracted a lot of attention. Unlike passive IRS, active IRS should be supplied by power, thus adjusting power between base station (BS) and IRS having a direct impact on the system rate performance. In this paper, the active IRS-aided network under a total power constraint is modeled with an ability of adjusting power between BS and IRS. Given the transmit beamforming at BS and reflecting beamforming at IRS, the SNR expression is derived to be a function of power allocation (PA) factor, and the optimization of maximizing the SNR is given. Subsequently, two high-performance PA strategies, enhanced multiple random initialization Newton's (EMRIN) and Taylor polynomial approximation (TPA), are proposed. The former is to improve the rate performance of classic Newton's method to avoid involving a local optimal point by using multiple random initializations. To reduce its high computational complexity, the latter provides a closed-form solution by making use of the first-order Taylor polynomial approximation to the original SNR function. Actually, using TPA, the original optimization problem is transformed into a problem of finding a root for a third-order polynomial. Simulation results are as follows: the first-order TPA of SNR fit its exact expression well, the proposed two PA methods performs much better than fixed PA in accordance with rate, and approaches exhaustive search as the number of IRS reflecting elements goes to large-scale.

关键词： Array signal processing Wireless networks Simulation Polynomials Vectors Resource management Newton method

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Detection and Classification of Transmission Line Transient Faults Based on Graph Convolutional Neural Network

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CSEE Journal of Power and Energy Systems 2021年第3期7卷 456-471页

作者： Houjie Tong Robert C.Qiu Dongxia Zhang Haosen Yang Qi Ding Xin Shi Department of Electrical Engineering Center for Big Data and Artificial IntelligenceShanghai Jiao Tong UniversityShanghai 200240China School of Electronic Information and Communication Huazhong University of Science and TechnologyWuhan 430000China Electric Power Research Institute Haidian DistrictBeijing 100192China School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China

We present a novel transient fault detection and classification approach in power transmission lines based on graph convolutional neural *** with the existing techniques,the proposed approach considers explicit spatial information in sampling sequences as prior knowledge and it has stronger feature extraction *** this basis,a framework for transient fault detection and classification is *** structure is generated to provide topology information to the *** approach takes the adjacency matrix of topology graph and the bus voltage signals during a sampling period after transient faults as inputs,and outputs the predicted classification results ***,the proposed approach is tested in various situations and its generalization ability is verified by experimental *** results show that the proposed approach can detect and classify transient faults more effectively than the existing techniques,and it is practical for online transmission line protection for its rapidness,high robustness and generalization ability.

关键词： Graph convolutional network(GCN) power transmission line fault detection and classification spatio-temporal data topology information

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A Signal Demodulation Algorithm Based on Generative Adversarial Networks 3

A Signal Demodulation Algorithm Based on Generative Adversar...

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3rd IEEE International Conference on Civil Aviation Safety and Information technology, ICCASIT 2021

作者： Lijun, Wang Minghong, Zhang Yu, Han Lei, Shen Science and Technology on Communication Information Security Control Laboratory The 36th Research Institute of China Electronics Technology Group Corporation Jiaxing China School of Communication Engineering Hangzhou Dianzi University Hangzhou China

ISBN: (纸本)9781665425186

In recent years, extensive research has been done to obtain better demodulation performance by combining signal demodulation with deep learning algorithms, such as convolutional neural networks (CNN). However, the demodulation performance of CNN is not much improved compared with traditional coherent demodulation algorithms. This paper proposes a signal demodulation algorithm based on generative adversarial networks (GAN). The proposed GAN-based demodulation network uses the confrontation characteristics between the generator and the discriminator in GAN to greatly improve the demodulation ability of the signal. Numerical results show that the demodulation performance is better than the coherent demodulation algorithm and the CNN-based demodulation algorithm. © 2021 IEEE

关键词： Demodulation

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Intelligent Clustering Based Active Power Allocation Strategy for Wind Turbines

Intelligent Clustering Based Active Power Allocation Strateg...

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Smart Grids and Energy Systems (SGES), International Conference on

作者： Zhihong Jiang Kangnan Song Guiyuan Liu Han Wu Han Li Hao Yang School of Electrical Engineering and Information Technology Changchun Institute of Technology Changchun China National Local Joint Engineering Research Center for Smart Distribution Grid Measurement and Control With Safety Operation Technology Changchun China International Joint Research Center for New Energy Power Generation and Network Control Technology of Jilin Province Changchun China

Aiming at the problem of uneven distribution of active power of each unit in a wind farm, an artificial intelligence clustering method based active power distribution strategy of wind turbine is proposed. Firstly, according to the actual operation data of the wind farm, the Pearson correlation coefficient is used to select the clustering data. Then, the Gaussian mixture model is used for clustering experiments. Finally, according to the clustering results, the distribution method of weight proportional coefficient is adopted to evenly distribute power to all units. The simulation results show that the proposed clustering method has a good evaluation score, and the power allocation strategy based on this can fully meet the scheduling criterias. All units can realize the smooth and orderly power rise and fall in the safe operation state, and avoid the problem of frequent start-stop and overload operation of some units caused by uneven power distribution.

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Unsupervised Feature Enrichment and Fidelity Preservation Learning Framework for Skeleton-based Action Recognition

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Li, Chuankun Li, Shuai Gao, Yanbo Gao, Xingyu Chen, Ping Li, Jian Li, Wanqing North University of China State Key Laboratory of Dynamic Testing Technology School of Information and Communication Engineering Taiyuan030051 China Shandong University School of Control Science and Engineering School of Software Jinan250100 China Chinese Academy of Sciences Institute of Microelectronics Beijing100029 China University of Wollongong Advanced Multimedia Research Lab WollongongNSW2522 Australia

Unsupervised skeleton-based action recognition has achieved remarkable progress recently. Existing unsupervised learning methods suffer from severe overfitting problem, and thus small networks are used, significantly reducing the representation capability. To address this problem, the overfitting mechanism behind the unsupervised learning for skeleton-based action recognition is first investigated. It is observed that skeleton is already a relatively high-level and low-dimension feature, but not in the same manifold as the features for action recognition. Simply applying the existing unsupervised learning method tends to produce features that discriminate the different samples rather than action classes, resulting in the overfitting problem. To address this problem, this paper proposes an Unsupervised spatial-temporal Feature Enrichment and Fidelity Preservation (U-FEFP) learning framework to generate rich distributed features that contain all the information of a skeleton sample. A spatial-temporal feature transformation subnetwork is developed using channel-wise topology refinement graph convolutional block and graph convolutional gated recurrent unit block as the basic feature extraction network. The unsupervised Bootstrap Your Own Latent-based learning is utilized to generate rich distributed features, and the unsupervised pretext task-based learning is employed to preserve the information contained in the skeleton. The two unsupervised learning ways are collaborated as U-FEFP to produce robust and discriminative representations. Experimental results on four widely used benchmarks, namely NTU-RGB+D-60, PKU-MMD, NTU-RGB+D-120 and UAV-Human dataset, demonstrate that the proposed U-FEFP obtains the best result compared with the state-of-the-art unsupervised learning methods. © 1991-2012 IEEE.

关键词： Convolution

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Adaptive Sliding Mode control for Force Tracking Using Nonlinear Observer

Adaptive Sliding Mode Control for Force Tracking Using Nonli...

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第34届中国控制与决策会议

作者： Linyan Han Haibo Du Shihua Li School of Automation Southeast UniversityKey Laboratory of Measurement and Control of Complex Systems of EngineeringMinistry of Education School of Electrical Engineering and Automation Hefei University of Technology

This paper focuses on the development of a control scheme capable of tracking contact forces ***,the nonlinear observer,admittance control and adaptive sliding mode control are integrated into a single *** the force sensor is absent,a nonlinear observer,which serves as a reaction force observer,can be used to estimate the contact ***,in order to track the force profile explicitly,admittance control is introduced to transform the desired contact force into the desired position *** adaptive sliding mode controller is designed to track the desired position and meanwhile compensate the estimation errors of contact forces caused by nonlinear *** stability of the whole system under the proposed controller can be guaranteed by the Lyapunov ***,several simulations are reported to demonstrate the effectiveness of the proposed approach.

关键词： Lyapunov Adaptive Sliding Mode control for Force Tracking Using Nonlinear Observer

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Design of Half-bridge Bidirectional DC-DC Converter control Loop

Design of Half-bridge Bidirectional DC-DC Converter Control ...

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2020 International Conference on Intelligent control, measurement and Signal Processing and Intelligent Oil Field, ICMSP 2020

作者： Cheng, Huan Guo, Yingna Ma, Zhao Bai, Sisi School of Electronic Engineering Xi'An Shiyou University Xi'an China Shaanxi Key Laboratory of Measurement and Control Technology for Oil and Gas Wells Xi'an China School of Electronic Engineering Xi'An Aeronautical University Xi'an China

In recent years, the direct current (DC) microgrid system has received extensive attention from industrial and academic circles worldwide. As an essential part of the DC microgrid, the bidirectional DC-DC converter can realize the bidirectional flow of energy according to the needs of the DC microgrid. In this paper, the half-bridge bidirectional DC-DC converter is taken as the research object, and the double closed-loop feedback control loop of the converter in the charging and discharging mode is designed based on the intermittent and fluctuating characteristics of distributed generation. Besides, a simulation model is built using MATLAB to analyze the operation of the converter in different modes and the control effect of the double closed-loop feedback control loop. The results verify the effectiveness and feasibility of the proposed control method. © Published under licence by IOP Publishing Ltd.

关键词： MATLAB

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Research on frequency control strategy of combined wind- energy storage system

Research on frequency control strategy of combined wind- ene...

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Smart Grids and Energy Systems (SGES), International Conference on

作者： Zhihong Jiang Guiyuan Liu Kangnan Song Han Li Hao Yang Han Wu School of Electrical Engineering and Information Technology Changchun Institute of Technology Changchun China National Local Joint Engineering Research Center for Smart Distribution Grid Measurement and Control With Safety Operation Technology Changchun China International Joint Research Center for New Energy Power Generation and Network Control Technology of Jilin Province Changchun China

Under the high penetration rate of wind power, the power system puts forward technical requirements for the frequency regulation capability of wind farms. Considering that the frequency response capability of the double-fed induction generator (DFIG) is insufficient, it is proposed to combine energy storage (ES) with wind power for system frequency regulation. Based on the frequency characteristic models of the wind farm and the ES system, this paper establishes a prediction model for the over speed control of the wind farm and a prediction model for the electrochemical energy storage system, and optimizes the active power output of the wind turbine and the energy storage system. The strategy is based on the optimal control of the wind turbine output power and ES power variation, and on this basis, an optimal control model considering the ES state of charge (SOC) and power is designed. The simulation results show that the proposed control strategy can not only effectively suppress grid frequency deviation can also coordinate the ES state of charge and frequency control.

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HSE: A plug-and-play module for unified fault diagnosis foundation models

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Information Fusion 2025年 123卷

作者： Li, Qi Chen, Bojian Chen, Qitong Li, Xuan Qin, Zhaoye Chu, Fulei Department of Mechanical Engineering Tsinghua University Beijing100084 China State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310058 China School of Mechanical and Electric Engineering Soochow University Suzhou215000 China Belt and Road Joint Laboratory on Measurement and Control Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

Intelligent Fault Diagnosis (IFD) plays a crucial role in industrial applications, where developing foundation models analogous to ChatGPT for comprehensive fault diagnosis remains a significant challenge. Current IFD methodologies are constrained by their inability to construct unified models capable of processing heterogeneous signal types, varying sampling rates, and diverse signal lengths across different equipment. To address these limitations, we propose a novel Heterogeneous Signal Embedding (HSE) module that projects heterogeneous signals into a unified signal space, offering seamless integration with existing IFD architectures as a plug-and-play solution. The HSE framework comprises two primary components: the Temporal-Aware Patching (TAP) module for embedding heterogeneous signals into a unified space, and the Cross-Dimensional Patch Fusion (CDPF) module for fusing embedded signals with temporal information into unified representations. We validate the efficacy of HSE through two comprehensive case studies: a simulation signal dataset and three distinct bearing datasets with heterogeneous features. Our experimental results demonstrate that HSE significantly enhances traditional fault diagnosis models, improving both diagnostic accuracy and generalization capability. While conventional approaches necessitate separate models for specific signal types, sampling frequencies, and signal lengths, HSE-enabled architectures successfully learn unified representations across diverse signal. The results from bearing fault diagnosis applications confirm substantial improvements in both diagnostic precision and cross-dataset generalization. As a pioneering contribution toward IFD foundation models, the proposed HSE framework establishes a fundamental architecture for advancing unified fault diagnosis systems. © 2025 Elsevier B.V.

关键词： Heterogeneous signal embedding Intelligent fault diagnosis Plug-and-play module Signal foundation model Signal fusion

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Research of Active Disturbance Rejection controller Design for PMSM Servo System

Research of Active Disturbance Rejection Controller Design f...

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2020 International Conference on Intelligent control, measurement and Signal Processing and Intelligent Oil Field, ICMSP 2020

作者： Hongliang, Yan Jiapeng, Li Leiming, Li Juju, Ma Shaanxi Key Laboratory of Oil and Gas Well Measurement and Control Technology Xi'An Shiyou University China School of Electronic Engineering Xi'An Shiyou University Xi'an China

In the permanent magnet synchronous motor (PMSM) speed control system, an improved active disturbance rejection control strategy with higher tracking accuracy for time-varying input is proposed. The traditional active disturbance rejection controller is mainly used for fast and static-free tracking of step signals, but there is a large tracking error for time-varying signals, which limits the application of active disturbance rejection controller. In this paper, the theoretical analysis of the existence of steady-state error is carried out, and then an improved speed active disturbance rejection controller (ADRC) with derivative feedforward and parallel linear extended state observer(P-LESO) is designed to reduce the tracking error of the system. In order to observe and compensate the reverse electromotive force in real time and reduce the current following error, a current loop linear active disturbance rejection controller is designed. By constructing Simulink simulation model for verification, the control system not only improves the tracking accuracy of PMSM for time-varying input, but also has a good dynamic performance for step input. © Published under licence by IOP Publishing Ltd.

关键词： controllers

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