Artificial neural network for bubbles pattern recognition on the images

All-Russian Conference with the School for Young Scientists “Thermophysics and Physical Hydrodynamics –2016”

Poletaev, I.E.^1,2 ; Pervunin, K.S.^1,2 ; Tokarev, M.P.^1

Kutateladze Institute of Thermophysics, Russian Academy of Sciences, Siberian Branch, 1, Lavrentyev Ave., Novosibirsk

630090, Russia^1

Novosibirsk National Research State University, Pirogova St. 2, Novosibirsk

630090, Russia^2

关键词: Convolutional Neural Networks (CNN);    Experimental and numerical studies;    High spatial resolution;    Interphase boundaries;    Modern technologies;    Optical diagnostics;    Recognition methods;    Sub-gradient optimization;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/754/7/072002/pdf
DOI  :  10.1088/1742-6596/754/7/072002

来源: IOP

【 摘 要 】

Two-phase bubble flows have been used in many technological and energy processes as processing oil, chemical and nuclear reactors. This explains large interest to experimental and numerical studies of such flows last several decades. Exploiting of optical diagnostics for analysis of the bubble flows allows researchers obtaining of instantaneous velocity fields and gaseous phase distribution with the high spatial resolution non-intrusively. Behavior of light rays exhibits an intricate manner when they cross interphase boundaries of gaseous bubbles hence the identification of the bubbles images is a complicated problem. This work presents a method of bubbles images identification based on a modern technology of deep learning called convolutional neural networks (CNN). Neural networks are able to determine overlapping, blurred, and non-spherical bubble images. They can increase accuracy of the bubble image recognition, reduce the number of outliers, lower data processing time, and significantly decrease the number of settings for the identification in comparison with standard recognition methods developed before. In addition, usage of GPUs speeds up the learning process of CNN owning to the modern adaptive subgradient optimization techniques.