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Annual Meeting Industrial and Commercial Power Systems Technical Conference

作者： Feiyu Sun Dongsheng Cai Zhenyuan Zhang Qi Huang College of Nuclear Technology and Automation Engineering Chengdu University of Technology China Sichuan Provincial Key Lab of Power System Wide-Area Measurement and Control School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China School of Information Engineering Southwest University of Science and Technology Mianyang P.R. China

ISBN: (数字)9798350345308

ISBN: (纸本)9798350345315

Wind and solar energy inverter-based resources (IBRs) has been employed extensively with the aim of carbon neutrality. Power systems also make extensive use of power electronic converters, leading to interactions between IBRs and with the grid, causing high frequency oscillation (HFO) problems. It damages equipment and jeopardizes the security of the system. To identify HFO components above 100 Hz, this research use Phasor measurement Unit (PMU) instantaneous value data to determine the HFO associated characteristics. To reduce the influence of spectral leakage and the fence effect, as well as to improve the identification accuracy of the related parameters, this paper adopts the RV self-convolutional window based Discrete Fourier Transform (DFT) and the Hanning double-window based all-phase Fast Fourier Transform (apFFT) hybrid algorithm to identify the HFO modal parameters. We take into account the identification accuracy in complex cases, such as non-nominal frequencies, with noise effects and multiple HFO modes. The simulation results show that the proposed method has high identification bandwidth, accuracy and strong flexibility.

关键词： Accuracy Wind energy Simulation Discrete Fourier transforms Noise Bandwidth Hafnium oxide

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A microwave photonic prototype for concurrent radar detection and spectrum sensing over an 8 to 40 GHz bandwidth

arXiv

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arXiv 2024年

作者： Shi, Taixia Liang, Dingding Wang, Lu Li, Lin Guo, Shaogang Gao, Jiawei Li, Xiaowei Lin, Chulun Shi, Lei Ding, Baogang Liu, Shiyang Yang, Fangyi Jiang, Chi Chen, Yang Shanghai Key Laboratory of Multidimensional Information Processing School of Communication and Electronic Engineering East China Normal University Shanghai200241 China Space Optoelectronic Measurement and Perception Lab Beijing Institute of Control Engineering Beijing100190 China Shanghai Lujie Communication Technology Company Limited Shanghai201613 China

In this work, a microwave photonic prototype for concurrent radar detection and spectrum sensing is proposed, designed, built, and investigated. A direct digital synthesizer and an analog electronic circuit are integrated to generate an intermediate frequency (IF) linearly frequency-modulated (LFM) signal with a tunable center frequency from 2.5 to 9.5 GHz and an instantaneous bandwidth of 1 GHz. The IF LFM signal is converted to the optical domain via an intensity modulator and then filtered by a fiber Bragg grating (FBG) to generate only two 2nd-order optical LFM sidebands. In radar detection, the two optical LFM sidebands beat with each other to generate a frequency-and-bandwidth-quadrupled LFM signal, which is used for ranging, radial velocity measurement, and imaging. By changing the center frequency of the IF LFM signal, the radar function can be operated within 8 to 40 GHz. In spectrum sensing, one 2nd-order optical LFM sideband is selected by another FBG, which then works in conjunction with the stimulated Brillouin scattering gain spectrum to map the frequency of the signal under test to time with an instantaneous measurement bandwidth of 2 GHz. By using a frequency shift module to adjust the pump frequency, the frequency measurement range can be adjusted from 0 to 40 GHz. The prototype is comprehensively studied and tested, which is capable of achieving a range resolution of 3.75 cm, a range error of less than ±2 cm, a radial velocity error within ±1 cm/s, delivering clear imaging of multiple small targets, and maintaining a frequency measurement error of less than ±7 MHz and a frequency resolution of better than 20 MHz. Copyright © 2024, The Authors. All rights reserved.

关键词： Fiber Bragg gratings

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Fault Detection Method for Insulators Using Improved YOLOv4

Journal of Network Intelligence

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Journal of Network Intelligence 2022年第4期7卷 818-834页

作者： Zhao, Li-Quan Jiang, Zi-Cong Teng, Zi-Ming Jia, Yan-Fei Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University 168 Changchun Road Jilin132012 China Graduate school of Information Science and Electrical Engineering Kyushu University Fukuoka819-0395 Japan College of information and communication Jilin University Changchun130012 China School of Electrical and Information Engineering Beihua University Jilin132013 China

A defective insulator detection method based on improved You Only Look Once version 4 (YOLOv4) is proposed to improve the precision of defective insulator detection. This method designs a dual-branch attention block by simulating the attention process of the human eye, and introduces it into the backbone of YOLOv4 to extract richer information on defective insulator features. To make the output feature map of shallow layer network fuse more different scale features, a path aggregation network has been designed. To improve detection accuracy, the spatial pyramid pooling has been replaced by atrous spatial pyramid pooling. A dynamic non-maximum suppression method is proposed to improve defective insulator detection accuracy. It employs an adaptive threshold control strategy and weighted operation to full use of the predicted box. We compare our method with defective insulator detection methods based on YOLO and R-CNN. Our simulation results show that the proposed method is more accurate than defective insulator detection methods based on YOLO, and has a faster detection speed than methods based on R-CNN. Compared with the defective insulator detection method based on R-CNN that has the highest mean average precision, the mean average precision of the proposed method is lower, but its detection speed is faster. Compared with YOLOv4, its detection speed is slower, but the mean average precision is higher. The proposed method strikes a satisfactory tradeoff between detection speed and mean average precision. © 2022, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Object detection

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Adaptive Extensive Cancellation Algorithm and Harmonic Enhanced Heart Rate Estimation based on MMWave Radar

arXiv

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arXiv 2025年

作者： Tang, Hui Yang, Zhan Rong, Yu Chai, Li Engineering Research Center of Metallurgical Automation and Measurement Technology Wuhan University of Science and Technology Wuhan430081 China The School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85281 United States College of Control Science and Engineering Zhejiang University Hangzhou310027 China

Heart rate (HR) monitoring is crucial for assessing physical fitness, cardiovascular health, and stress management. Millimeter-wave radar offers a promising non-contact solution for long-term monitoring. However, accurate HR estimation remains challenging in low signal-to-noise ratio (SNR) conditions. To deal with both respiration harmonics and intermodulation interference, this paper proposes a cancellation-before-estimation strategy. Firstly, we present the adaptive extensive cancellation algorithm (ECA) to suppress respiratory and its low-order harmonics. Then, we propose an adaptive harmonic enhanced trace (AHET) method to avoid intermodulation interference by refining the HR search region. Various experimental results validate the effectiveness of the proposed methods, demonstrating improvements in accuracy, robustness, and computational efficiency compared to conventional approaches based on the FMCW (Frequency Modulated Continuous Wave) system. © 2025, CC BY-NC-ND.

关键词： Intermodulation

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Interpretable DRL-based Maneuver Decision of UCAV Dogfight

arXiv

引用

arXiv 2024年

作者： Han, Haoran Cheng, Jian Lv, Maolong School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China Air Traffic Control and Navigation College Air Force Engineering University Xi’an710051 China College of Aeronautics Engineering Air Force Engineering University Xi’an710038 China

This paper proposes a three-layer unmanned combat aerial vehicle (UCAV) dogfight frame where Deep reinforcement learning (DRL) is responsible for high-level maneuver decision. A four-channel low-level control law is firstly constructed, followed by a library containing eight basic flight maneuvers (BFMs). Double deep Q network (DDQN) is applied for BFM selection in UCAV dogfight, where the opponent strategy during the training process is constructed with DT. Our simulation result shows that, the agent can achieve a win rate of 85.75% against the DT strategy, and positive results when facing various unseen opponents. Based on the proposed frame, interpretability of the DRL-based dogfight is significantly improved. The agent performs yo-yo to adjust its turn rate and gain higher maneuverability. Emergence of"Dive and Chase" behavior also indicates the agent can generate a novel tactic that utilizes the drawback of its opponent. © 2024, CC0.

关键词： Reinforcement learning

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Actively tunable dual-broadband graphene-based terahertz metamaterial absorber

引用

Chinese Physics B 2021年第12期30卷 495-502页

作者： Dan Hu Tian-Hua Meng Hong-Yan Wang Mai-Xia Fu School of Physics and Electrical Engineering Anyang Normal UniversityAnyang 455000China Department of Physics and Electronics Science Shanxi Datong UniversityDatong 037009China School of Education Information Technology and Communication Anyang Normal UniversityAnyang 455000China College of Information Science and Engineering Henan University of TechnologyKey Laboratory of Grain Information Processing and ControlMinistry of EducationZhengzhou 450001China

A tunable metamaterial absorber(MA)with dual-broadband and high absorption properties at terahertz(THz)frequencies is designed in this *** MA consists of a periodic array of flower-like monolayer graphene patterns at top,a SiO_(2)dielectric spacer in middle,and a gold ground plane at the *** simulation results demonstrate that the designed MA has two wide absorption bands with an absorption of over 90%in frequency ranges of 0.68 THz-1.63 THz and 3.34 THz-4.08 THz,and the corresponding relative bandwidths reach 82.3%and 20%,*** peak absorptivity of the absorber can be dynamically controlled from less than 10%to nearly 100%by adjusting the graphene chemical potential from 0 eV to 0.9 ***,the designed absorber is polarization-insensitive and has good robustness to incident *** a high-performance MA has broad application prospects in THz imaging,modulating,filtering,etc.

关键词： metamaterial graphene absorber terahertz

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Attention Mechanism-Based Multilayer Pruned Graph Convolutional Network for Industrial Fault Diagnosis

SSRN

引用

SSRN 2024年

作者： Zhao, Qiang Chen, Libin Chen, Qiyue Han, Yinghua Sun, Jie College of Information Science and Engineering Northeastern University Shenyang China School of Control Engineering Northeastern University at Qinhuangdao Qinhuangdao China School of Computer and Communication Engineering Northeastern University at Qinhuangdao Qinhuangdao China The State Key Laboratory of Rolling and Automation Northeastern University United States Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao066004 China

Fault diagnosis in complex industrial processes has become a vital issue drawing the attention in the industrial field. With the increasing deployment of sensors in industrial process equipment, deep learning (DL) based approaches have become more feasible. However, most existing DL-based approaches have two main drawbacks. Firstly, there is complex coupling between variables in industrial processes. Most existing approaches have only one metric to build the network, which cannot fully explore the potential relationships between variables. Secondly, there are few studies on aggregating critical features of different variables and reducing the interference of irrelevant and redundant information. To overcome these challenges, this paper presents a new approach for industrial process troubleshooting, called the variational modal decomposition-multi-head graph pruning network (VMD-MHGPN). This approach draws inspiration from the attention mechanism and combines it with graph construction and pruning. Specifically, we use multiple attention mechanisms to learn the interactions between nodes. Previously, variational modal decomposition (VMD) is applied to extract fault features at different time scales and use them as subsequent graph nodes. To reduce the impact of irrelevant and redundant nodes on the fault feature representation, we introduce the graph pruning operation, which uses an attention mechanism to score nodes and discard relatively unimportant ones. The final representation of fault features is generated by computing the weights of different interaction graphs based on the attention mechanism and aggregating multiple interaction graphs according to the weights. The proposed approach for diagnosing faults in industrial processes proved efficient and superior based on experimental findings from a three-phase flow apparatus and a Tennessee Eastman process. © 2024, The Authors. All rights reserved.

关键词： Failure analysis

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Obstacle Avoidance Based on Deep Reinforcement Learning and Artificial Potential Field

Obstacle Avoidance Based on Deep Reinforcement Learning and ...

引用

International Conference on control, Automation and Robotics (ICCAR)

作者： Haoran Han Zhilong Xi Jian Cheng Maolong Lv School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China Air Traffic Control and Navigation College Air Force Engineering University Xian China

Obstacle avoidance is an essential part of mobile robot path planning, since it ensures the safety of automatic control. This paper proposes an obstacle avoidance algorithm that combines artificial potential field with deep reinforcement learning (DRL). State regulation is presented so that the pre-defined velocity constraint could be satisfied. To guarantee the isotropy of the robot controller as well as reduce training complexity, coordinate transformation into normal direction and tangent direction is introduced, making it possible to use one-dimension controllers to work in a two-dimension task. Artificial potential field (APF) is modified such that the obstacle directly affects the intermediate target positions instead of the control commands, which can well be used to guide the previously trained one-dimension DRL controller. Experiment results show that the proposed algorithm successfully achieved obstacle avoidance tasks in single-agent and multi-agent scenarios.

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Strip Weld Inspection Based on Improved VGG16

Strip Weld Inspection Based on Improved VGG16

引用

IEEE International Conference on Signal and Image Processing (ICSIP)

作者： Sisi Fu Yuan Chen Dexin Xian Shiyuan Kong Hao Wang Jinsong Wang School of Optoelectronic Engineering Changchun University of Science and Technology Zhongshan Guangdong China School of Measurement and Control Technology and Communication Engineering Harbin University of Science and Technology Harbin Heilongjiang China School of Optoelectronic Engineering Zhongshan Institute of Changchun University of Science and Technology Zhongshan Guangdong China School of Optoelectronic Engineering Changchun University of Science and Technology Zhongshan Institute of Changchun University of Science and Technology Zhongshan Guangdong China

As one of the indispensable materials in modern industry, high precision and high efficiency defect detection of strip weld has become an important part of production and quality control. In response to the shortcomings of the existing strip weld detection technology with low detection accuracy, this paper proposes a deep learning based strip weld defect detection and classification technique. First, the CBAM attention mechanism is introduced into the network structure to improve the attention to important features; second, the traditional convolution layer is replaced by using the dilated convolution to reduce the computation while increasing the perceptual field; finally, the original ReLU activation function is replaced by the PReLU activation function to effectively overcome the problem of neuron death in the Relu activation function. The improved model finally achieves a recognition rate of 98.1% in the test set, which is 22.1% higher than that of the unimproved model, greatly improving the accuracy and robustness of the algorithm.

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Joint Vehicle Pairing, Spectrum Assignment and Power control for Sum-Rate Maximization in NOMA-Based V2X Underlaid Cellular Networks

引用

IEEE Internet of Things Journal 2025年第12期12卷 22337-22349页

作者： Xue, Tong Zhang, Haixia Ding, Hui Yuan, Dongfeng Shandong University of Science and Technology College of Electronic and Information Engineering Shandong Qingdao266590 China Shandong Key Laboratory of Intelligent Communication and Sensing-Computing Integration Shandong Jinan250061 China Shandong University School of Control Science and Engineering Shandong Jinan250061 China

Vehicle-to-everything (V2X) underlaid cellular networks in underlaid mode suffers catastrophic co-channel interference caused by spectrum sharing, results in a reduced system sum-rate. To cope with this, this work studies a social-mobility-aware non-orthogonal multiple access (NOMA)-enabled V2X underlaid cellular network to mitigate the co-channel interference and improve the sum-rate. By jointly optimizing vehicle pairing and resources, a sum-rate maximization problem is formulated under the diverse quality of service requirements of both cellular and vehicular users. The formulated problem is proved to be a non-deterministic polynomial-time (NP)-hard problem, and is difficult to solve. As an alternative, we propose a NOMA-based joint vehicle pairing, spectrum assignment and power control algorithm (NOMA-JVP-SA-PCA), with which the original problem is decomposed into two disjoint subproblems, i. e., 1) joint vehicle pairing and spectrum assignment subproblem, and 2) power control subproblem. Dealing the first subproblem, we propose a heuristic social-mobility-aware vehicle pairing algorithm (HSMA-VPA) and a revised Kuhn-munkres-based spectrum assignment algorithm (KM-SAA) to acquire the vehicle pairing and spectrum assignment solutions. Then, solving the second subproblem, a closed-form power solution is obtained utilizing a three-dimensional geometric power control approach (3D-PCA). Finally, we solve the original problem through an iterative method. Simulation results show that the proposed NOMA-JVP-SA-PCA effectively enhances the sum-rate and outperforms the baseline algorithms around 24%-53% within a specific range. © 2014 IEEE.

关键词： Polynomial approximation

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