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  1. 721

    Using Clustering Methods for the Identification of Acoustic Emission Signals Generated by the Selected Form of Partial Discharge in Oil-Paper Insulation by Sebastian BORUCKI, Jacek ŁUCZAK, Dariusz ZMARZŁY

    Published 2018-01-01
    “…The article presents the results concerning the use of clustering methods to identify signals of acoustic emission (AE) generated by partial discharge (PD) in oil-paper insulation. …”
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    Article
  2. 722

    River Channel Microgeomorphic Feature Extraction and Potential Sandstorm Source Identification Method Based on a Convolutional Autoencoder Model by Kecong Wu, Lirong Chen, Yalige Bai, Xinhang Wang, Danzeng Pingcuo, Zhongpeng Han, Chengshan Wang

    Published 2025-01-01
    “…Current deep learning methods are predominantly applied to visible objects, rendering them unsuitable for latent objects with unstable spatiotemporal distributions, such as potential sandstorm sources. …”
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    Minima-YOLO: A Lightweight Identification Method for Lithium Mineral Components Under a Microscope Based on YOLOv8 by Zeyang Qiu, Xueyu Huang, Xiangyu Xu

    Published 2025-03-01
    “…To enable effective deployment and implementation of rapid mineral sorting for valuable ores on edge computing devices, we propose a lightweight identification method for lithium minerals under visible light microscopy based on YOLOv8, named Minima-YOLO. …”
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    Research on Cross-Circuitry Fault Identification Method for AC/DC Transmission System Based on Blind Signal Separation Algorithm by Yan Tao, Xiangping Kong, Chenqing Wang, Junchao Zheng, Zijun Bin, Jinjiao Lin, Sudi Xu

    Published 2025-03-01
    “…The traditional AC/DC transmission detection methods have the characteristics of complex detection processes and low fault line identification rates. …”
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    Power Operation Violation Identification Method Based on Point Cloud Data Preprocessing and Deep Learning under the Architecture of IoT by Shibo Yang, Weiping Fu, Lishuo Zhang, Zhaolei Wang

    Published 2023-01-01
    “…Therefore, the proposed method can effectively improve the accuracy of power operation violation identification.…”
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    Identification of Abnormal Electricity Consumption Behavior of Low-Voltage Users in New Power Systems Based on a Combined Method by Jiaolong Gou, Xudong Niu, Xi Chen, Shuxin Dong, Jing Xin

    Published 2025-05-01
    “…To further validate the proposed method, a comparative analysis was conducted against the Long Short-Term Memory Autoencoder (LSTM-AE) model. …”
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  20. 740

    An Improved Time-Frequency Identification Method for Pipeline Weld EMAE Aliasing Modes With a Focused Electromagnetic Excitation Device by Houshi Ding, Pan Hu, Tongyu Zhao, Yuchao Jin, Xu Gui

    Published 2025-01-01
    “…However, the complex nature of EMAE signals, which consist of both guided wave and acoustic emission (AE) components, presents challenges in the design of excitation devices and mode identification. This paper presents an improved time-frequency identification method for pipeline weld EMAE aliasing modes with a focused electromagnetic excitation device. …”
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    Article