This paper presents a new methodology based on elliptical basis function (EBF) networks and an innovative feature extraction technique which makes use of the co-occurrence matrices and the SVD decomposition in order to recognize organic solar cells defects. The experimental results show that our algorithm achieves an high accuracy of recognition of 96% and that the feature extraction technique proposed is very effective in the pattern recognition problems that involving the texture’s analysis. The proposed methodology can be used as a tool to optimize the fabrication process of the organic solar cells. All the tests carried out for this work were made by using the organic solar cells realized in the Optoelectronic Organic Semiconductor Devices Laboratory at Ben Gurion University of the Negev.
|Titolo:||Combining SVD and co-occurrence matrix information to recognize organic solar cells defects with a elliptical basis function network classifier|
|Data di pubblicazione:||2017|
|Appare nelle tipologie:||2.1 Contributo in volume (Capitolo o Saggio)|
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