Deep Learning-Based Fault Classification and Location for Underground Power Cable of Nuclear Facilities-Reference-Cited by-同舟云学术

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Deep Learning-Based Fault Classification and Location for Underground Power Cable of Nuclear Facilities

Published:2022 Issue: Volume:10 Page:70126-70142
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Said Abdelrahman¹^ORCID,Hashima Sherief²^ORCID,Fouda Mostafa M.¹^ORCID,Saad Mohamed H.³^ORCID

Affiliation:

1. Department of Electrical Engineering, Faculty of Engineering at Shoubra, Benha University, Cairo, Egypt

2. Engineering Department, Nuclear Research Center (NRC), Egyptian Atomic Energy Authority, Inshas, Cairo, Egypt

3. Radiation Engineering Department, National Center for Radiation Research and Technology (NCRRT), Egyptian Atomic Energy Authority, Cairo, Egypt

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09810250.pdf?arnumber=9810250

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1. A stacking-based fault forecasting study for power transmission lines under different weather conditions;Energy;2024-10

2. An Empirical Study on Fault Detection and Root Cause Analysis of Indium Tin Oxide Electrodes by Processing S-Parameter Patterns;IEEE Transactions on Device and Materials Reliability;2024-09

3. Leaky Cable Perimeter Intrusion Detection Based on Deep Reinforcement Learning;IEEE Internet of Things Journal;2024-06-15

4. Human State Recognition Using Ultra Wideband Radar Based on CvT;IEEE Internet of Things Journal;2024-06-15

5. Intelligent Radiation: A review of Machine learning applications in nuclear and radiological sciences;Annals of Nuclear Energy;2024-06

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