文献
J-GLOBAL ID:201902276236520603
整理番号:19A2340277
コーヒー産業における高密度豆間の深い学習欠陥検査のための労働効率の良いGaNベースのモデル生成スキーム【JST・京大機械翻訳】
A Labor-Efficient GAN-based Model Generation Scheme for Deep-Learning Defect Inspection among Dense Beans in Coffee Industry
著者 (10件):
Kuo Cheng-Ju
(Department of Computer Science and Information Engineering, Institute of Manufacturing Information and Systems, National Cheng Kung University, Taiwan)
,
Chen Chao-Chun
(Department of Computer Science and Information Engineering, Institute of Manufacturing Information and Systems, National Cheng Kung University, Taiwan)
,
Chen Tzu-Ting
(Department of Computer Science and Information Engineering, Institute of Manufacturing Information and Systems, National Cheng Kung University, Taiwan)
,
Tsai ZhiJing
(Dept. of Mgmt. Info. Sys, Southern Taiwan University of Science and Technology (STUST), Taiwan)
,
Hung Min-Hsiung
(Department of Computer Science and Information Engineering, Chinese Culture University, Taiwan)
,
Lin Yu-Chuan
(Department of Computer Science and Information Engineering, Institute of Manufacturing Information and Systems, National Cheng Kung University, Taiwan)
,
Chen Yi-Chung
(Department of Industrial Engineering and Management, National Yunlin University of Science and Technology, Taiwan)
,
Wang Ding-Chau
(Dept. of Mgmt. Info. Sys, Southern Taiwan University of Science and Technology (STUST), Taiwan)
,
Homg Gwo-Jiun
(Dept. of Comp. Sci. & Info. Engr, STUST)
,
Su Wei-Tsung
(Department of Computer Science and Information Engineering, Aletheia University, Taiwan)
資料名:
IEEE Conference Proceedings
(IEEE Conference Proceedings)
巻:
2019
号:
CASE
ページ:
263-270
発行年:
2019年
JST資料番号:
W2441A
資料種別:
会議録 (C)
記事区分:
原著論文
発行国:
アメリカ合衆国 (USA)
言語:
英語 (EN)