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Updated: Jan 11, 2026

Kinematic History of a Salient-recess Junction Explored through a Combined Approach of Field Data and Analog Sandbox Modeling
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GCFR:图形对比的故障表示,用于在电力通信网络中的可靠诊断

Baohao Chen1, Guosheng Lu2, Yifeng Zhu2

  • 1China Southern Power Grid EHV Transmission Company, Guangzhou, 510620, China. uveu35@163.com.

Scientific reports
|November 13, 2025
PubMed
概括

本研究介绍了GCFR,这是一种用于电力通信网络 (PCN) 故障诊断的图形对比框架. 它有效地处理有限的数据,网络变化和噪音警报,以进行可靠的预测.

关键词:
相反的学习学习.错误诊断是一个错误的诊断.图形数据增强 图形数据增强图形神经网络是一个神经网络.电力通信网络是电力通信网络.

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