Defect Detection with Generative Adversarial Networks for Electroluminescence Images of Solar Cells-Reference-Cited by-同舟云学术

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Defect Detection with Generative Adversarial Networks for Electroluminescence Images of Solar Cells

Published:2020-10-16 Issue: Volume: Page:
ISSN:
Container-title:2020 35th Youth Academic Annual Conference of Chinese Association of Automation (YAC)
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Author:

Shou Chunhui¹,Hong Ling¹,Ding Waner¹,Shen Qu¹,Zhou Wenhao²,Jiang Yu²,Zhao Chunhui²

Affiliation:

1. Zhejiang Provincial Key Laboratory of Solar Energy, Utilization & Energy Saving Technology, Zhejiang Energy Group R&D,Hangzhou,China

2. State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University,Hangzhou,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9337559/9337493/09337676.pdf?arnumber=9337676

Cited by 13 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Towards more reliable photovoltaic energy conversion systems: A weakly-supervised learning perspective on anomaly detection;Energy Conversion and Management;2024-09

2. Photovoltaic Cell Anomaly Detection Enabled by Scale Distribution Alignment Learning and Multiscale Linear Attention Framework;IEEE Internet of Things Journal;2024-08-15

3. A Novel Fuzzy Neural Network Architecture Search Framework for Defect Recognition With Uncertainties;IEEE Transactions on Fuzzy Systems;2024-05

4. A lightweight network for photovoltaic cell defect detection in electroluminescence images based on neural architecture search and knowledge distillation;Applied Energy;2024-02

5. Photovoltaic Cell Defect Detection Based on Weakly Supervised Learning With Module-Level Annotations;IEEE Access;2024

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