Standard

Pattern recognition for bubbly flows with vapor or gas-liquid interfaces using U-Net architecture. / Seredkin, Alexander; Plokhikh, Ivan; Mullyadzhanov, Rustam et al.

Proceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 5-8 9303175 (Proceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

Harvard

Seredkin, A, Plokhikh, I, Mullyadzhanov, R, Malakhov, I, Serdyukov, V, Surtaev, A, Chinak, A, Lobanov, P & Tokarev, M 2020, Pattern recognition for bubbly flows with vapor or gas-liquid interfaces using U-Net architecture. in Proceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020., 9303175, Proceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020, Institute of Electrical and Electronics Engineers Inc., pp. 5-8, 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020, Virtual, Novosibirsk, Russian Federation, 14.11.2020. https://doi.org/10.1109/S.A.I.ence50533.2020.9303175

APA

Seredkin, A., Plokhikh, I., Mullyadzhanov, R., Malakhov, I., Serdyukov, V., Surtaev, A., Chinak, A., Lobanov, P., & Tokarev, M. (2020). Pattern recognition for bubbly flows with vapor or gas-liquid interfaces using U-Net architecture. In Proceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020 (pp. 5-8). [9303175] (Proceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/S.A.I.ence50533.2020.9303175

Vancouver

Seredkin A, Plokhikh I, Mullyadzhanov R, Malakhov I, Serdyukov V, Surtaev A et al. Pattern recognition for bubbly flows with vapor or gas-liquid interfaces using U-Net architecture. In Proceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 5-8. 9303175. (Proceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020). doi: 10.1109/S.A.I.ence50533.2020.9303175

Author

Seredkin, Alexander ; Plokhikh, Ivan ; Mullyadzhanov, Rustam et al. / Pattern recognition for bubbly flows with vapor or gas-liquid interfaces using U-Net architecture. Proceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 5-8 (Proceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020).

BibTeX

@inproceedings{58ece1a370b14157b51c842dc3ca5885,
title = "Pattern recognition for bubbly flows with vapor or gas-liquid interfaces using U-Net architecture",
abstract = "We apply deep learning algorithms to tackle the bubble recognition task relying on the experimental video recordings of the vapor cavities growing during the water pool boiling due to the heated bottom and an isothermal multiphase flow in a channel. As a basic network architecture we use U-Net with ResNet 34 and ResNet 50 encoders depending on the complexity of the image background. Three classes have been introduced, i.e. the background, bubble and its boundary allowing to post-process some geometric characteristics in a straightforward manner. We demonstrate the capabilities by tracking the growth of an ensemble of vapor bubbles attached to the heater and studying the size distribution of bubbles in a channel.",
keywords = "bubbles, image processing, neural networks",
author = "Alexander Seredkin and Ivan Plokhikh and Rustam Mullyadzhanov and Ivan Malakhov and Vladimir Serdyukov and Anton Surtaev and Alexander Chinak and Pavel Lobanov and Mikhail Tokarev",
note = "Funding Information: Funded by the RFBR grants No. 20-08-01093, 20-58-46008 and within the state contract with IT SB RAS Publisher Copyright: {\textcopyright} 2020 IEEE. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.; 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020 ; Conference date: 14-11-2020 Through 15-11-2020",
year = "2020",
month = nov,
day = "14",
doi = "10.1109/S.A.I.ence50533.2020.9303175",
language = "English",
series = "Proceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "5--8",
booktitle = "Proceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020",
address = "United States",

}

RIS

TY - GEN

T1 - Pattern recognition for bubbly flows with vapor or gas-liquid interfaces using U-Net architecture

AU - Seredkin, Alexander

AU - Plokhikh, Ivan

AU - Mullyadzhanov, Rustam

AU - Malakhov, Ivan

AU - Serdyukov, Vladimir

AU - Surtaev, Anton

AU - Chinak, Alexander

AU - Lobanov, Pavel

AU - Tokarev, Mikhail

N1 - Funding Information: Funded by the RFBR grants No. 20-08-01093, 20-58-46008 and within the state contract with IT SB RAS Publisher Copyright: © 2020 IEEE. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2020/11/14

Y1 - 2020/11/14

N2 - We apply deep learning algorithms to tackle the bubble recognition task relying on the experimental video recordings of the vapor cavities growing during the water pool boiling due to the heated bottom and an isothermal multiphase flow in a channel. As a basic network architecture we use U-Net with ResNet 34 and ResNet 50 encoders depending on the complexity of the image background. Three classes have been introduced, i.e. the background, bubble and its boundary allowing to post-process some geometric characteristics in a straightforward manner. We demonstrate the capabilities by tracking the growth of an ensemble of vapor bubbles attached to the heater and studying the size distribution of bubbles in a channel.

AB - We apply deep learning algorithms to tackle the bubble recognition task relying on the experimental video recordings of the vapor cavities growing during the water pool boiling due to the heated bottom and an isothermal multiphase flow in a channel. As a basic network architecture we use U-Net with ResNet 34 and ResNet 50 encoders depending on the complexity of the image background. Three classes have been introduced, i.e. the background, bubble and its boundary allowing to post-process some geometric characteristics in a straightforward manner. We demonstrate the capabilities by tracking the growth of an ensemble of vapor bubbles attached to the heater and studying the size distribution of bubbles in a channel.

KW - bubbles

KW - image processing

KW - neural networks

UR - http://www.scopus.com/inward/record.url?scp=85099552474&partnerID=8YFLogxK

U2 - 10.1109/S.A.I.ence50533.2020.9303175

DO - 10.1109/S.A.I.ence50533.2020.9303175

M3 - Conference contribution

AN - SCOPUS:85099552474

T3 - Proceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020

SP - 5

EP - 8

BT - Proceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020

Y2 - 14 November 2020 through 15 November 2020

ER -

ID: 27590117