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The Transactions of the Korean Institute of Electrical Engineers

The Transactions of the Korean Institute of Electrical Engineers

ISO Journal TitleTrans. Korean. Inst. Elect. Eng.
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  4. 10.5370/KIEE.2023.72.3.428
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References

1 
Korean Statistics Information Service, Electricity consumption per capitaGoogle Search
2 
Korean Statistics Information Service, Power Generation Status by Energy SourceGoogle Search
3 
Ministry of Environment, National Greenhouse Gas StatisticsGoogle Search
4 
Korean Statistics Information Service, New and Renewable Energy Supply StatusGoogle Search
5 
KEPCO, Solar power and solar heatGoogle Search
6 
Dhimish Mahmoud, 2019, Solar cells micro crack detection technique using state-of-the-art electroluminescence imaging, Journal of Science: Advanced Materials and Devices, Vol. 4, No. 4, pp. 499-508DOI
7 
M. Kontges, 2011, The risk of power loss in crystalline silicon based photovoltaic modules due to micro-cracks, Solar energy materials and solar cells, Vol. 95, No. 4, pp. 1131-1137DOI
8 
Yang Zhao, 2021, Deep learning‐based automatic detection of multitype defects in photovoltaic modules and application in real production line, Progress in Photovoltaics: Research and Applications, Vol. 29, No. 4, pp. 471-484DOI
9 
Wuqin Tang, 2020, Deep learning based automatic defect identification of photovoltaic module using electroluminescence images, Solar Energy, Vol. 201, pp. 453-460DOI
10 
Sergiu Deitsch, 2019, Automatic classification of defective photovoltaic module cells in electroluminescence images, Solar Energy, Vol. 185, pp. 455-468DOI
11 
Alexey Dosovitskiy, 2020, An image is worth 16x16 words: Transformers for image recognition at scale, arXiv preprint arXiv:2010.11929Google Search
12 
Tero Karras, 2020, Training generative adversarial networks with limited data, Advances in Neural Information Processing Systems, Vol. 33, pp. 12104-12114Google Search
13 
Ekin D. Cubuk, 2019, Autoaugment: Learning augmentation strategies from data, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern RecognitionGoogle Search
14 
Buerhop-Lutz Claudia, 2018, A benchmark for visual identification of defective solar cells in electroluminescence imagery, 35th European PV Solar Energy Conference and Exhibition, Vol. 1287-1289Google Search
15 
, 2018, A Benchmark for Visual Identification of Defective Solar Cells in Electroluminescence Imagery, https://github.com/ zae-bayern/elpv-dataset, Vol. 1287-1289Google Search
16 
Sergiu Deitsch, 2021, Segmentation of photovoltaic module cells in uncalibrated electroluminescence images, Machine Vision and Applications, Vol. 32, No. 4, pp. 1-23DOI
17 
Alex Krizhevsky, 2009, Learning multiple layers of features from tiny images, 7Google Search
18 
Jia Deng, 2009, Imagenet: A large-scale hierarchical image database, 2009 IEEE conference on computer vision and pattern recognition IeeeGoogle Search
19 
Dosovitskiy Alexey, 2020, An image is worth 16x16 words: Transformers for image recognition at scale, arXiv preprint arXiv:2010.11929Google Search