Hydrophobicity Classes Photos

Hydrophobicity Classes Photos

Citation Author(s):
Christos-Christodoulos
Kokalis
Thanos
Tasakos
Vassiliki
Kontargyri
Giorgos
Siolas
Ioannis
Gonos
Submitted by:
Christos Kokalis
Last updated:
Thu, 05/16/2019 - 11:17
DOI:
10.21227/arq0-qm58
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Abstract: 

This paper discusses the classification of composite insulators in hydrophobicity classes, according to the spray method of IEC Standard 62073, using convolutional neural networks. By applying the spray method, about 4500 photos were collected and are available online, from all hydrophobicity classes using distilled water-ethyl alcohol as spraying sollution. Convolutional neural networks based on Keras and Tensorflow libraries of Python programming language were trained, validated and tested in order to determine the hydrophobicity class of composite insulators. Various configuration setups of convolutional neural networks are applied and compared for their appropriateness in accurately classifying the composite insulators. The proposed methodology is a useful tool for the classification of composite insulators in hydrophobicity classes restricting the subjectivity of human judgment. The experiments showed that this method gives almost 98% accuracy in this classification task.

Instructions: 

The pictures of the seven different hydrophobicity classes were split into three separate sets for each hydrophobicity class. The first one consisting of 400instances of each class (400 × 7 = 2800 photos) was used for the training of the networks. The second one consisting of 100 instances of each class (100 × 7 = 700 photos) was used for the evaluation-validation of the learning course and the comparison of the different models. The last one with 122-165 different instances of each class (980 photos) was used for the final assessment of our chosen model. In this work the dimensions of photographs are (height, width) = (300, 450) pixels.

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[1] Christos-Christodoulos Kokalis, Thanos Tasakos, Vassiliki Kontargyri, Giorgos Siolas, Ioannis Gonos, "Hydrophobicity Classes Photos", IEEE Dataport, 2019. [Online]. Available: http://dx.doi.org/10.21227/arq0-qm58. Accessed: May. 20, 2019.
@data{arq0-qm58-19,
doi = {10.21227/arq0-qm58},
url = {http://dx.doi.org/10.21227/arq0-qm58},
author = {Christos-Christodoulos Kokalis; Thanos Tasakos; Vassiliki Kontargyri; Giorgos Siolas; Ioannis Gonos },
publisher = {IEEE Dataport},
title = {Hydrophobicity Classes Photos},
year = {2019} }
TY - DATA
T1 - Hydrophobicity Classes Photos
AU - Christos-Christodoulos Kokalis; Thanos Tasakos; Vassiliki Kontargyri; Giorgos Siolas; Ioannis Gonos
PY - 2019
PB - IEEE Dataport
UR - 10.21227/arq0-qm58
ER -
Christos-Christodoulos Kokalis, Thanos Tasakos, Vassiliki Kontargyri, Giorgos Siolas, Ioannis Gonos. (2019). Hydrophobicity Classes Photos. IEEE Dataport. http://dx.doi.org/10.21227/arq0-qm58
Christos-Christodoulos Kokalis, Thanos Tasakos, Vassiliki Kontargyri, Giorgos Siolas, Ioannis Gonos, 2019. Hydrophobicity Classes Photos. Available at: http://dx.doi.org/10.21227/arq0-qm58.
Christos-Christodoulos Kokalis, Thanos Tasakos, Vassiliki Kontargyri, Giorgos Siolas, Ioannis Gonos. (2019). "Hydrophobicity Classes Photos." Web.
1. Christos-Christodoulos Kokalis, Thanos Tasakos, Vassiliki Kontargyri, Giorgos Siolas, Ioannis Gonos. Hydrophobicity Classes Photos [Internet]. IEEE Dataport; 2019. Available from : http://dx.doi.org/10.21227/arq0-qm58
Christos-Christodoulos Kokalis, Thanos Tasakos, Vassiliki Kontargyri, Giorgos Siolas, Ioannis Gonos. "Hydrophobicity Classes Photos." doi: 10.21227/arq0-qm58