Deep-Learning-Based Lithium Battery Defect Detection via Cross-Domain Generalization-Reference-Cited by-同舟云学术

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Deep-Learning-Based Lithium Battery Defect Detection via Cross-Domain Generalization

Published:2024 Issue: Volume:12 Page:78505-78514
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Chen Xuhesheng¹^ORCID,Liu Mingyue²^ORCID,Niu Yongjie³^ORCID,Wang Xukang⁴^ORCID,Cheng Wu Ying⁵^ORCID

Affiliation:

1. School of Information and Library Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

2. Department of Computer Science, Cornell University, Ithaca, NY, USA

3. CEEC Jiangsu Power Design Institute Company Ltd., Nanjing, China

4. Sage IT Consulting Group, Shanghai, China

5. School of Law, University of Washington, Seattle, WA, USA

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/6287639/10380310/10545557.pdf?arnumber=10545557

Reference24 articles.

1. Surface defect classification of steels with a new semi-supervised learning method

2. Steel Surface Defect Classification Using Deep Residual Neural Network

3. A new graph-based semi-supervised method for surface defect classification

4. Surface Defects Detection and Identification of Lithium Battery Pole Piece Based on Multi-Feature Fusion and PSO-SVM

5. An Automatic Defects Detection Scheme for Lithium-ion Battery Electrode Surface

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