Art

J-GLOBAL ID：202402242525577255   Reference number：24A0339220

Automated Battery Making Fault Classification Using Over-Sampled Image Data CNN Features

オーバサンプリング画像データCNN特徴を用いた自動電池製造故障分類【JST・京大機械翻訳】

Author (3)： Din Nasir Ud ,  Zhang Li ,  Yang Yatao
Material： Sensors (Web)
Volume： 23  Issue： 4  Page： 1927  Publication year： 2023 
JST Material Number： U7015A  ISSN： 1424-8220  CODEN： SENSC9  Document type： Article
Article type： 原著論文  Country of issue： Switzerland (CHE)  Language： ENGLISH (EN)

Abstract/Point： Due to the tremendous expectat...

Thesaurus term： *battery ,...
Semi thesaurus term： *オーバサンプリング ,...

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Author keywords (5)： fault detection ,  deep learning ,  machine learning ,  SMOTE ,  image classification

JST classification (1)： Graphic and image processing in general  (JE04010I)

Reference (58)：

• Chiang, Y.M. Building a better battery. Science 2010, 330, 1485-1486.
• Borah, R.; Hughson, F.; Johnston, J.; Nann, T. On battery materials and methods. Mater. Today Adv. 2020, 6, 100046.
• Lee, S.S.; Kim, T.H.; Hu, S.J.; Cai, W.W.; Abell, J.A. Joining technologies for automotive lithium-ion battery manufacturing: A review. In Proceedings of the International Manufacturing Science and Engineering Conference, Erie, PA, USA, 12-15 October 2010; Volume 49460, pp. 541-549.
• Brand, M.J.; Schmidt, P.A.; Zaeh, M.F.; Jossen, A. Welding techniques for battery cells and resulting electrical contact resistances. J. Energy Storage 2015, 1, 7-14.
• Fonseka, C.; Jayasinghe, J. Implementation of an automatic optical inspection system for solder quality classification of THT solder joints. IEEE Trans. Compon. Packag. Manuf. Technol. 2018, 9, 353-366.

Terms in the title (4)： オーバサンプリング ,  CNN ,  電池 ,  故障分類