Solar Cell Cracks and Finger Failure Detection Using Statistical Parameters of Electroluminescence Images and Machine Learning

Autor: Harsh Rajesh Parikh, Yoann Buratti, Sergiu Spataru, Frederik Villebro, Gisele Alves Dos Reis Benatto, Peter B. Poulsen, Stefan Wendlandt, Tamas Kerekes, Dezso Sera, Ziv Hameiri

Publikováno v: Applied Sciences, Vol 10, Iss 24, p 8834 (2020)

A wide range of defects, failures, and degradation can develop at different stages in the lifetime of photovoltaic modules. To accurately assess their effect on the module performance, these failures need to be quantified. Electroluminescence (EL) im

Externí odkaz: https://doaj.org/article/1e66b7841b4a4255b4a41f2709f52f2f

Solar Cell Cracks and Finger Failure Detection Using Statistical Parameters of Electroluminescence Images and Machine Learning

Autor: Frederik Villebro, Harsh Parikh, Dezso Sera, Gisele Alves dos Reis Benatto, Tamas Kerekes, Ziv Hameiri, Sergiu Spataru, Yoann Buratti, Stefan Wendlandt, Peter Behrensdorff Poulsen

Publikováno v: Parikh, H R, Buratti, Y, Spataru, S V, Villebro, F, A. dos Reis Benatto, G, Poulsen, P, Wendlandt, S, Kerekes, T, Séra, D & Hameiri, Z 2020, ' Solar Cell Cracks and Finger Failure Detection Using Statistical Parameters of Electroluminescence Images and Machine Learning ', Applied Sciences, vol. 10, no. 24, pp. 1-15 . https://doi.org/10.3390/app10248834
Parikh, H R, Buratti, Y, Spataru, S, Villebro, F, Benatto, G A D R, Poulsen, P B, Wendlandt, S, Kerekes, T, Sera, D & Hameiri, Z 2020, ' Solar Cell Cracks and Finger Failure Detection Using Statistical Parameters of Electroluminescence Images and Machine Learning ', Applied Sciences, vol. 10, no. 24, 8834 . https://doi.org/10.3390/app10248834
Applied Sciences, Vol 10, Iss 8834, p 8834 (2020)
Applied Sciences
Volume 10
Issue 24

A wide range of defects, failures, and degradation can develop at different stages in the lifetime of photovoltaic modules. To accurately assess their effect on the module performance, these failures need to be quantified. Electroluminescence (EL) im

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::12e706d11a24d0ad3c60de7a632f0329
https://vbn.aau.dk/ws/files/393464089/applsci_10_08834.pdf

Leakage Detection in District Heating Systems Using UAV IR Images: Comparing Convolutional Neural Network and ML Classifiers

Autor: Kabir Hossain, Frederik Villebro, Søren Forchhammer

Publikováno v: Hossain, K, Villebro, F & Forchhammer, S 2019, Leakage Detection in District Heating Systems Using UAV IR Images: Comparing Convolutional Neural Network and ML Classifiers . in Proceedings of 27th European Signal Processing Conference . European Association for Signal Processing (EURASIP), 2019 27th European Signal Processing Conference, A Coruña, Spain, 02/09/2019 .
Technical University of Denmark Orbit

In this paper, we proposed a method to detect leakages automatically in underground pipes of district heating networks based on images, which are captured by an Unmanned Aerial Vehicle (UAV). The original datasets are captured in a 16 bits format and

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::b71a746a61e2d1edb33ba9a8ae6563e6
https://orbit.dtu.dk/en/publications/3f97b567-8fe2-4d4f-936c-d0076de1347c