文献
J-GLOBAL ID:202102289952833769
整理番号:21A3171705
シールド機械トンネル地質編隊認識のための新しい制約高密度畳込みオートエンコーダとDNNベース半教師つき法【JST・京大機械翻訳】
A novel constrained dense convolutional autoencoder and DNN-based semi-supervised method for shield machine tunnel geological formation recognition
著者 (7件):
Yu Honggan
(State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)
,
Tao Jianfeng
(State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)
,
Qin Chengjin
(State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)
,
Liu Mingyang
(State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)
,
Xiao Dengyu
(State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)
,
Sun Hao
(State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)
,
Liu Chengliang
(State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)
資料名:
Mechanical Systems and Signal Processing
(Mechanical Systems and Signal Processing)
巻:
165
ページ:
Null
発行年:
2022年
JST資料番号:
T0514A
ISSN:
0888-3270
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)