@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",
}