文献
J-GLOBAL ID:202102272462343500
整理番号:21A0989779
故障検査報告によるRNN-LSTMに基づく非構造テキストデータマイニングと故障分類に関する研究【JST・京大機械翻訳】
Research on Unstructured Text Data Mining and Fault Classification Based on RNN-LSTM with Malfunction Inspection Report
著者 (7件):
Wei Daqian
(School of Electrical Engineering, Wuhan University, Wuhan 430072, China)
,
Wang Bo
(School of Electrical Engineering, Wuhan University, Wuhan 430072, China)
,
Lin Gang
(School of Electrical Engineering, Wuhan University, Wuhan 430072, China)
,
Liu Dichen
(School of Electrical Engineering, Wuhan University, Wuhan 430072, China)
,
Dong Zhaoyang
(School of Electrical Engineering and Telecommunications, University of NSW, Sydney 2052, Australia)
,
Liu Hesen
(Department of Electrical Engineering and Computer Science, The University of Tennessee, Knoxville, TN 37996, USA)
,
Liu Yilu
(Department of Electrical Engineering and Computer Science, The University of Tennessee, Knoxville, TN 37996, USA)
資料名:
Energies (Web)
(Energies (Web))
巻:
10
号:
3
ページ:
406
発行年:
2017年03月
JST資料番号:
U7016A
ISSN:
1996-1073
CODEN:
ENERGA
資料種別:
逐次刊行物 (A)
記事区分:
原著論文
発行国:
スイス (CHE)
言語:
英語 (EN)