文献
J-GLOBAL ID:202202283994289756
整理番号:22A0943014
残差学習による新しいエンコーダ-復号器モデルを用いた土壌水分予測の改善【JST・京大機械翻訳】
Improving soil moisture prediction using a novel encoder-decoder model with residual learning
著者 (6件):
Li Qingliang
(College of Computer Science and Technology, Changchun Normal University, Changchun 130032, China)
,
Li Zhongyan
(College of Computer Science and Technology, Changchun Normal University, Changchun 130032, China)
,
Shangguan Wei
(Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-Sen University, Guangzhou, Guangdong, China)
,
Wang Xuezhi
(College of Computer Science and Technology, Jilin University, Changchun 130012, China)
,
Li Lu
(Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-Sen University, Guangzhou, Guangdong, China)
,
Yu Fanhua
(College of Computer Science and Technology, Changchun Normal University, Changchun 130032, China)
資料名:
Computers and Electronics in Agriculture
(Computers and Electronics in Agriculture)
巻:
195
ページ:
Null
発行年:
2022年
JST資料番号:
T0337A
ISSN:
0168-1699
資料種別:
逐次刊行物 (A)
記事区分:
原著論文
発行国:
オランダ (NLD)
言語:
英語 (EN)