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Портал Begell Электронная Бибилиотека e-Книги Журналы Справочники и Сборники статей Коллекции
Journal of Flow Visualization and Image Processing
SJR: 0.161 SNIP: 0.312 CiteScore™: 0.1

ISSN Печать: 1065-3090
ISSN Онлайн: 1940-4336

Выпуски:
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Journal of Flow Visualization and Image Processing

DOI: 10.1615/JFlowVisImageProc.v1.i4.10
pages 261-269

APPLICATION OF NEURAL NETWORKS TO QUANTITATIVE FLOW VISUALIZATION

Ichiro Kimura
Department of Electro-Mechanics, Osaka Electro-Communication University, Osaka; Department of Environmental and Information Sciences, Yokkaichi University 1200 Kayo-cho, Yokkaichi 512-8512, Japan
Yasuaki Kuroe
Department of Electronics and Information Science, Kyoto Institute of Technology, Kyoto, Japan
Mamoru Ozawa
Department of Safety Science, Kansai University, 7-1 Hakubai-cho, Takatsuki-shi, Osaka 569-1098, Japan

Краткое описание

It is an important challenge to analyze a highly complex flow field in engineering, science, agriculture, and medicine. For such an analysis, it is essential to measure instantaneous physical quantities over the entire flow field. Recently, some flow measurement systems have been developed using image processing to obtain useful information from visualized flow images. The technique, however, has some problems.
This report presents two new algorithms using neural networks for improving the measured values obtained by the image processing. One is a new algorithm for formularizing the relationship between color and temperature using the Back-propagation neural networks. The formularization is needed for the analysis of thermal flows using a thermo-sensitive liquid crystal method. The algorithm extends the measurement range. The other new algorithm uses the Hopfield neural networks for determining erroneous vectors, which appear in a velocity vector distribution estimated by a correlation method. The Hopfield network can distinguish erroneous from correct vectors even if the density of the erroneous vectors in a flow field is high.