The Hydrophobicity Class Identification of Silicone-Rubber Samples using Deep Learning Algorithms

Nesibe Demiroglu; Idris Ozdemir; Halil Ibrahim Uckol; Suat Ilhan

doi:10.1109/UPEC55022.2022.9917706

The Hydrophobicity Class Identification of Silicone-Rubber Samples using Deep Learning Algorithms

Nesibe Demiroglu
, Idris Ozdemir
, Halil Ibrahim Uckol
, Suat Ilhan

Elektrik Mühendisliği Bölümü

Istanbul Technical University

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

1 Atıf (Scopus)

Özet

This paper presents an approach to classify the hydrophobicity characteristic of silicone rubber (SiR) samples using deep learning algorithms. By deforming the hydrophobicity property of SiR samples using corona discharges, images of water droplets placed on the sample surface were acquired. From the images, the contact angles of the droplets were determined to find the hydrophobicity classes. The generated water droplet image dataset was trained, validated, and tested utilizing AlexNet, VGGNet, and ResNet. The result shows that the modified AlexNet model with an accuracy of 99.36% is a reliable diagnostic method to identify the hydrophobicity qualification of the SiR samples.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	2022 57th International Universities Power Engineering Conference
Ana bilgisayar yayını alt yazısı	Big Data and Smart Grids, UPEC 2022 - Proceedings
Yayınlayan	Institute of Electrical and Electronics Engineers Inc.
ISBN (Elektronik)	9781665455053
DOI'lar	https://doi.org/10.1109/UPEC55022.2022.9917706
Yayın durumu	Yayınlandı - 2022
Etkinlik	57th International Universities Power Engineering Conference: Big Data and Smart Grids, UPEC 2022 - Istanbul, Turkey Süre: 30 Ağu 2022 → 2 Eyl 2022

Yayın serisi

Adı	2022 57th International Universities Power Engineering Conference: Big Data and Smart Grids, UPEC 2022 - Proceedings

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	57th International Universities Power Engineering Conference: Big Data and Smart Grids, UPEC 2022
Ülke/Bölge	Turkey
Şehir	Istanbul
Periyot	30/08/22 → 2/09/22

Bibliyografik not

Publisher Copyright:
© 2022 IEEE.

Belgeye Erişim

10.1109/UPEC55022.2022.9917706

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

Demiroglu, N., Ozdemir, I., Uckol, H. I., & Ilhan, S. (2022). The Hydrophobicity Class Identification of Silicone-Rubber Samples using Deep Learning Algorithms. In 2022 57th International Universities Power Engineering Conference: Big Data and Smart Grids, UPEC 2022 - Proceedings (2022 57th International Universities Power Engineering Conference: Big Data and Smart Grids, UPEC 2022 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/UPEC55022.2022.9917706

Demiroglu, Nesibe ; Ozdemir, Idris ; Uckol, Halil Ibrahim et al. / The Hydrophobicity Class Identification of Silicone-Rubber Samples using Deep Learning Algorithms. 2022 57th International Universities Power Engineering Conference: Big Data and Smart Grids, UPEC 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. (2022 57th International Universities Power Engineering Conference: Big Data and Smart Grids, UPEC 2022 - Proceedings).

@inproceedings{5936c407319c40a7a32a757ea8b651e4,

title = "The Hydrophobicity Class Identification of Silicone-Rubber Samples using Deep Learning Algorithms",

abstract = "This paper presents an approach to classify the hydrophobicity characteristic of silicone rubber (SiR) samples using deep learning algorithms. By deforming the hydrophobicity property of SiR samples using corona discharges, images of water droplets placed on the sample surface were acquired. From the images, the contact angles of the droplets were determined to find the hydrophobicity classes. The generated water droplet image dataset was trained, validated, and tested utilizing AlexNet, VGGNet, and ResNet. The result shows that the modified AlexNet model with an accuracy of 99.36\% is a reliable diagnostic method to identify the hydrophobicity qualification of the SiR samples.",

keywords = "contact angle, deep learning, hydrophobicity characteristic, image classification, silicone-rubber",

author = "Nesibe Demiroglu and Idris Ozdemir and Uckol, \{Halil Ibrahim\} and Suat Ilhan",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 57th International Universities Power Engineering Conference: Big Data and Smart Grids, UPEC 2022 ; Conference date: 30-08-2022 Through 02-09-2022",

year = "2022",

doi = "10.1109/UPEC55022.2022.9917706",

language = "English",

series = "2022 57th International Universities Power Engineering Conference: Big Data and Smart Grids, UPEC 2022 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2022 57th International Universities Power Engineering Conference",

address = "United States",

}

Demiroglu, N, Ozdemir, I, Uckol, HI & Ilhan, S 2022, The Hydrophobicity Class Identification of Silicone-Rubber Samples using Deep Learning Algorithms. in 2022 57th International Universities Power Engineering Conference: Big Data and Smart Grids, UPEC 2022 - Proceedings. 2022 57th International Universities Power Engineering Conference: Big Data and Smart Grids, UPEC 2022 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 57th International Universities Power Engineering Conference: Big Data and Smart Grids, UPEC 2022, Istanbul, Turkey, 30/08/22. https://doi.org/10.1109/UPEC55022.2022.9917706

The Hydrophobicity Class Identification of Silicone-Rubber Samples using Deep Learning Algorithms. / Demiroglu, Nesibe; Ozdemir, Idris; Uckol, Halil Ibrahim et al.
2022 57th International Universities Power Engineering Conference: Big Data and Smart Grids, UPEC 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. (2022 57th International Universities Power Engineering Conference: Big Data and Smart Grids, UPEC 2022 - Proceedings).

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

TY - GEN

T1 - The Hydrophobicity Class Identification of Silicone-Rubber Samples using Deep Learning Algorithms

AU - Demiroglu, Nesibe

AU - Ozdemir, Idris

AU - Uckol, Halil Ibrahim

AU - Ilhan, Suat

PY - 2022

Y1 - 2022

N2 - This paper presents an approach to classify the hydrophobicity characteristic of silicone rubber (SiR) samples using deep learning algorithms. By deforming the hydrophobicity property of SiR samples using corona discharges, images of water droplets placed on the sample surface were acquired. From the images, the contact angles of the droplets were determined to find the hydrophobicity classes. The generated water droplet image dataset was trained, validated, and tested utilizing AlexNet, VGGNet, and ResNet. The result shows that the modified AlexNet model with an accuracy of 99.36% is a reliable diagnostic method to identify the hydrophobicity qualification of the SiR samples.

AB - This paper presents an approach to classify the hydrophobicity characteristic of silicone rubber (SiR) samples using deep learning algorithms. By deforming the hydrophobicity property of SiR samples using corona discharges, images of water droplets placed on the sample surface were acquired. From the images, the contact angles of the droplets were determined to find the hydrophobicity classes. The generated water droplet image dataset was trained, validated, and tested utilizing AlexNet, VGGNet, and ResNet. The result shows that the modified AlexNet model with an accuracy of 99.36% is a reliable diagnostic method to identify the hydrophobicity qualification of the SiR samples.

KW - contact angle

KW - deep learning

KW - hydrophobicity characteristic

KW - image classification

KW - silicone-rubber

UR - https://www.scopus.com/pages/publications/85141485123

U2 - 10.1109/UPEC55022.2022.9917706

DO - 10.1109/UPEC55022.2022.9917706

M3 - Conference contribution

AN - SCOPUS:85141485123

T3 - 2022 57th International Universities Power Engineering Conference: Big Data and Smart Grids, UPEC 2022 - Proceedings

BT - 2022 57th International Universities Power Engineering Conference

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 57th International Universities Power Engineering Conference: Big Data and Smart Grids, UPEC 2022

Y2 - 30 August 2022 through 2 September 2022

ER -

Demiroglu N, Ozdemir I, Uckol HI, Ilhan S. The Hydrophobicity Class Identification of Silicone-Rubber Samples using Deep Learning Algorithms. In 2022 57th International Universities Power Engineering Conference: Big Data and Smart Grids, UPEC 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2022. (2022 57th International Universities Power Engineering Conference: Big Data and Smart Grids, UPEC 2022 - Proceedings). doi: 10.1109/UPEC55022.2022.9917706

The Hydrophobicity Class Identification of Silicone-Rubber Samples using Deep Learning Algorithms

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