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• W2984077352 abstract "Abstract Materials and devices age with time. Material aging and degradation has important implications for lifetime performance of materials and systems. While consensus exists that materials should be studied and designed for degradation, materials inspection during operation is typically performed manually by technicians. The manual inspection makes studies prone to errors and uncertainties due to human subjectivity. In this work, we focus on automating the process of degradation mechanism detection through the use of a fully convolutional deep neural network architecture (F-CNN). We demonstrate that F-CNN architecture allows for automated inspection of cracks in polymer backsheets from photovoltaic (PV) modules. The developed F-CNN architecture enabled an end-to-end semantic inspection of the PV module backsheets by applying a contracting path of convolutional blocks (encoders) followed by an expansive path of decoding blocks (decoders). First, the hierarchy of contextual features is learned from the input images by encoders. Next, these features are reconstructed to the pixel-level prediction of the input by decoders. The structure of the encoder and the decoder networks are thoroughly investigated for the multi-class pixel-level degradation type prediction for PV module backsheets. The developed F-CNN framework is validated by reporting degradation type prediction accuracy for the pixel level prediction at the level of 92.8%." @default.
• W2984077352 created "2019-11-22" @default.
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• W2984077352 date "2019-11-06" @default.
• W2984077352 modified "2023-09-25" @default.
• W2984077352 title "Degradation Mechanism Detection in Photovoltaic Backsheets by Fully Convolutional Neural Network" @default.
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• W2984077352 doi "https://doi.org/10.1038/s41598-019-52550-6" @default.
• W2984077352 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6834571" @default.
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