基于生成对抗网络的太阳能电池缺陷增强方法
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刘坤,文熙,黄闽茗,杨欣欣,毛经坤
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Solar cell defect enhancement method based on generative adversarial network
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Kun LIU,Xi WEN,Min-ming HUANG,Xin-xin YANG,Jing-kun MAO
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表 1 各模型在太阳能电池EL缺陷数据上的生成质量 |
Tab.1 Generated quality of each model on solar cell EL dataset |
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方法 | s | p1-NN | MMD | WD | DCGAN | 20 | 0.81 | 0.35 | 4.3 | DCGAN | 50 | 0.79 | 0.33 | 4.0 | DCGAN | 100 | 0.76 | 0.31 | 3.8 | DCGAN | 200 | 0.75 | 0.30 | 3.6 | WGAN-GP | 20 | 0.73 | 0.32 | 4.2 | WGAN-GP | 50 | 0.73 | 0.31 | 3.9 | WGAN-GP | 100 | 0.72 | 0.28 | 3.7 | WGAN-GP | 200 | 0.69 | 0.27 | 3.5 | FOGAN | 20 | 0.68 | 0.29 | 3.8 | FOGAN | 50 | 0.65 | 0.25 | 3.4 | FOGAN | 100 | 0.61 | 0.20 | 3.1 | FOGAN | 200 | 0.58 | 0.18 | 2.9 | NSGGAN | 20 | 0.53 | 0.20 | 3.3 | NSGGAN | 50 | 0.53 | 0.18 | 3.2 | NSGGAN | 100 | 0.53 | 0.17 | 3.1 | NSGGAN | 200 | 0.52 | 0.15 | 2.8 |
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