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Journal of Flow Visualization and Image Processing

Erscheint 4 Ausgaben pro Jahr

ISSN Druckformat: 1065-3090

ISSN Online: 1940-4336

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 0.6 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 0.6 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.00013 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.14 SJR: 0.201 SNIP: 0.313 CiteScore™:: 1.2 H-Index: 13

Indexed in

APPLICATION OF NEURAL NETWORKS TO QUANTITATIVE FLOW VISUALIZATION

Volumen 1, Ausgabe 4, 1993, pp. 261-269
DOI: 10.1615/JFlowVisImageProc.v1.i4.10
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ABSTRAKT

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.

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