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pro vyhledávání: '"Huang, Xueyou"'
Akademický článek
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Publikováno v:
In Composites Part B 15 April 2016 91:392-398
Publikováno v:
2020 IEEE International Conference on High Voltage Engineering and Application (ICHVE).
The textual data of insulation status contains the original records of the on-site partial discharge detection. In order to improve the data utilization rate and mine the relevant information that affects the insulation status of the equipment, it ne
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::05dba8f5a57d127240c656bdf0cbd132
https://doi.org/10.1109/ichve49031.2020.9279643
https://doi.org/10.1109/ichve49031.2020.9279643
Publikováno v:
DSA
Power cable is an important carrier of power in medium voltage distribution network. It plays an important role in power transportation and power distribution. Whether the insulation performance of the cable is intact in actual production is an impor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9c5a3ce36cc9d5baea8db5cec9823d58
https://doi.org/10.1109/dsa.2019.00036
https://doi.org/10.1109/dsa.2019.00036
Publikováno v:
Journal of Physics: Conference Series. 1659:012057
The traditional switchgear partial discharge pattern recognition method lacks a certain generalization performance and the recognition accuracy is low, which is difficult to apply in practical engineering. In view of this, this paper proposes a switc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8520c3e68dc3f947eba52fa06914b95e
https://doi.org/10.1088/1742-6596/1659/1/012057
https://doi.org/10.1088/1742-6596/1659/1/012057
Publikováno v:
Journal of Physics: Conference Series. 1659:012056
The problem of sample imbalance will lead to poor generalization ability of the deep learning model algorithm, and the phenomenon of overfitting during network training, which limits the accuracy of intelligent fault diagnosis of switchgear equipment
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c3c93be6a5f32b9c2e3e3b48f13fd1ac
https://doi.org/10.1088/1742-6596/1659/1/012056
https://doi.org/10.1088/1742-6596/1659/1/012056
Publikováno v:
IOP Conference Series: Earth and Environmental Science. 431:012028
An infrared temperature prediction method for power equipment is proposed based on the radial basis function (RBF) network optimized by quantum genetic algorithm (QGA) and orthogonal least squares algorithm (OLSA). The modified compound algorithm was
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::962f5c0486eef642484e7d7652d27837
https://doi.org/10.1088/1755-1315/431/1/012028
https://doi.org/10.1088/1755-1315/431/1/012028
Akademický článek
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