DEFECT GENERATION OF SOLAR CELLS BY FUSION OF META-LEARNING AND DUAL-PATH ATTENTION

Zhou Ying; Yuan Zitong; Chen Haiyong; Wang Shijie

doi:10.19912/j.0254-0096.tynxb.2022-0806

PDF(2005 KB)

Acta Energiae Solaris Sinica ›› 2023, Vol. 44 ›› Issue (9) : 85-93. DOI: 10.19912/j.0254-0096.tynxb.2022-0806

DEFECT GENERATION OF SOLAR CELLS BY FUSION OF META-LEARNING AND DUAL-PATH ATTENTION

Zhou Ying^1,2, Yuan Zitong¹, Chen Haiyong^1,2, Wang Shijie¹

Author information +

History +

Abstract

Due to over-fitting of deep learning models brought on by the absence of picture data containing solar cell defects, few-shot defect identification has become challenging. As a method of enhancing the data, this study proposes employing meta-attention generative adversarial networks （MAGAN）, which incorporate meta-learning and dual-path attention. The dual-path attention module （DPAT） is designed to pay more attention to the small and faint defect features in the image during the feature extraction process. A clustering constrained loss function is proposed to solve the gradient disappearance problem during the training process while improving the network architecture. The meta-learning tuning module （MTM） is designed to optimize the weight parameters in the generator. The study and experiment discoveries demonstrate that the suggested strategy outperforms previous generative adversarial networks and may produce useful target datasets for modest sample faults.

Key words

solar cells / defect detection / generative adversarial networks / meta-learning / dual-path attention / data augmentation

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Zhou Ying, Yuan Zitong, Chen Haiyong, Wang Shijie. DEFECT GENERATION OF SOLAR CELLS BY FUSION OF META-LEARNING AND DUAL-PATH ATTENTION[J]. Acta Energiae Solaris Sinica. 2023, 44(9): 85-93 https://doi.org/10.19912/j.0254-0096.tynxb.2022-0806

References

[1] 张治, 邹鹏辉, 刘志锋, 等. 发射极掺杂工艺对产业化IBC太阳电池性能的影响[J]. 太阳能学报, 2022, 43(3): 158-162.
ZHANG Z, ZOU P H, LIU Z F, et al.Influence of emitter doping process on performance of industrial IBC solar cell[J]. Acta energiae solaris sinica, 2022, 43(3): 158-162.
[2] 仝勖峰, 金嘉祺, 袁晓军, 等. 基于机器视觉的区域太阳直接辐射动态预测方法研究[J]. 太阳能学报, 2021, 42(6): 247-255.
TONG X F, JIN J Q, YUAN X J, et al.Research on regional solar direct normal irradiance dynamic forecasting based on machine vision[J]. Acta energiae solaris sinica, 2021, 42(6): 247-255.
[3] EL HAJ Y, EL-HAG A H, GHUNEM R A. Application of deep-learning via transfer learning to evaluate silicone rubber material surface erosion[J]. IEEE transactions on dielectrics and electrical insulation, 2021, 28(4): 1465-1467.
[4] CUBUK E D, ZOPH B, MANÉ D, et al.AutoAugment: learning augmentation strategies from data[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 2020: 113-123.
[5] REIPSCHLAGER P, FLEMISCH T, DACHSELT R.Personal augmented reality for information visualization on large interactive displays[J]. IEEE transactions on visualization and computer graphics, 2021, 27(2): 1182-1192.
[6] GOODFELLOW I J, POUGRT A J, MIRZA M, et al.Generative adversarial nets[C]//Proceedings of the 27th International Conference on Neural Information Processing Systems, Montreal, Cambridge, UK, 2014: 2672-2680.
[7] TAO X, ZHANG D P, WANG Z H, et al.Detection of power line insulator defects using aerial images analyzed with convolutional neural networks[J]. IEEE transactions on systems, man, and cybernetics: systems, 2020, 50(4): 1486-1498.
[8] HANG R L, ZHOU F, LIU Q S, et al.Classification of hyperspectral images via multitask generative adversarial networks[J]. IEEE transactions on geoscience and remote sensing, 2021, 59(2): 1424-1436.
[9] RADFORD A, METZ L, CHINTALA S.Unsupervised representation learning with deep convolutional generative adversarial networks[J]. Computer ence, 2015. DOI:10.48550/arXiv.1511.06434.
[10] ARJOVSKY M, CHINTALA S, BOTTOU L. Wasserstein GAN[EB/OL].2017: arXiv: 1701.07875. https://arxiv.org/abs/1701.07875.
[11] ZHANG H, GOODFELLOW I, METAXAS D, et al. Self-attention generative adversarial networks[EB/OL].2018: arXiv: 1805.08318. https://arxiv.org/abs/1805.08318.
[12] CHEN Z T, FU Y W, ZHANG Y D, et al.Multi-level semantic feature augmentation for one-shot learning[J]. IEEE transactions on image processing, 2019, 28(9): 4594-4605.
[13] PENG S G.Overview of meta-reinforcement learning research[C]//2020 2nd International Conference on Information Technology and Computer Application (ITCA), Guangzhou, China, 2021: 54-57.
[14] PARK S, JANG H, SIMEONE O, et al.Learning to demodulate from few pilots via offline and online meta-learning[J]. IEEE transactions on signal processing, 2021, 69: 226-239.
[15] 叶星余, 何元烈, 汝少楠. 基于生成式对抗网络及自注意力机制的无监督单目深度估计和视觉里程计[J]. 机器人, 2021, 43(2): 203-213.
YE X Y, HE Y L, RU S N.Unsupervised monocular depth estimation and visual odometry based on generative adversarial network and self-attention mechanism[J]. Robot, 2021, 43(2): 203-213.
[16] WOO S, PARK J, LEE J Y, et al. CBAM: convolutional block attention module[EB/OL].2018: arXiv: 1807.06521. https://arxiv.org/abs/1807.06521.