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Journal of Flow Visualization and Image Processing
Главный редактор: Krishnamurthy Muralidhar (open in a new tab)

Выходит 4 номеров в год

ISSN Печать: 1065-3090

ISSN Онлайн: 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

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EDGE DETECTION AND MACHINE LEARNING FOR AUTOMATIC FLOW STRUCTURES DETECTION AND TRACKING ON SCHLIEREN AND SHADOWGRAPH IMAGES

Том 28, Выпуск 4, 2021, pp. 1-26
DOI: 10.1615/JFlowVisImageProc.2021037690
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Краткое описание

Visual information in experimental fluid dynamics is enlarging now to the level of big scientific data due to usage of digital cameras. Flow images and animations are the objects of digital image processing where different algorithms can be applied. Schlieren, shadow, and other refraction-based techniques have been often used to study gas flow. They can capture strong density gradients, such as shock waves. Shock detection is an important task in analyzing unsteady supersonic gas flows. High-speed cameras are widely used to record large arrays of shadow images. In this paper, two computer software systems based on the edge detection and machine learning with CNN were developed to process datasets of the shadow images and automatically detect shock waves, plumes, and other gas flow structures. The edge-detection software uses image filtering, noise removing, background image subtraction, and edge detection based on the Canny algorithm. The machine learning software is based on CNN. A full object detection network was trained to identify two different types of objects on the schlieren or shadow images: plumes and shock waves. We applied transfer learning to decrease learning time and number of images for training. Shock detection was tested on a flat shock wave moving in a shock tube. Plume detection was tested on the shadow flow images, initiated by the pulsed surface gas discharge. It was shown that both edge detection and machine learning may be successfully applied for gas flow structures tracking and measurements. The dynamics of the original and the reflected shock waves and the position and size of the plume were automatically measured by the developed software.

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ЦИТИРОВАНО В
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