文献
J-GLOBAL ID:201902270307334864
整理番号:19A2164097
深層学習と転送学習技術を用いた大時間分解能における大気質予測精度の改善【JST・京大機械翻訳】
Improving air quality prediction accuracy at larger temporal resolutions using deep learning and transfer learning techniques
著者 (6件):
Ma Jun
(Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, China)
,
Cheng Jack C.P.
(Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, China)
,
Lin Changqing
(Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, China)
,
Lin Changqing
(Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong, China)
,
Tan Yi
(Sino-Australia Joint Research Center in BIM and Smart Construction, Shenzhen University, Shenzhen, China)
,
Zhang Jingcheng
(School of Engineering, The Hong Kong University of Science and Technology, Hong Kong, China)
資料名:
Atmospheric Environment
(Atmospheric Environment)
巻:
214
ページ:
Null
発行年:
2019年
JST資料番号:
C0382D
ISSN:
1352-2310
資料種別:
逐次刊行物 (A)
記事区分:
原著論文
発行国:
イギリス (GBR)
言語:
英語 (EN)