The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
La vélocimétrie par image de particules (PIV) est un outil largement utilisé pour mesurer les différentes propriétés cinématiques de l’écoulement d’un fluide. Dans cette technique de mesure, une feuille de lumière laser pulsée est utilisée pour éclairer un champ d'écoulement ensemencé de particules traceuses et à chaque éclairement, les positions des particules sont enregistrées sur des caméras numériques CCD. Les deux images de caméra résultantes peuvent ensuite être traitées par diverses techniques pour obtenir les vecteurs vitesse. L'une de ces techniques implique le suivi des particules individuelles afin d'identifier le déplacement de chaque particule présente dans le champ d'écoulement. Le déplacement des particules individuelles ainsi déterminé donne l'information sur la vitesse s'il est divisé par un intervalle de temps connu. La précision ainsi que l’efficacité de ces systèmes de mesure dépendent de la fiabilité des algorithmes permettant de suivre ces particules. Dans le présent travail, un algorithme basé sur un réseau neuronal cellulaire a été proposé. Le test de performance a été effectué à l’aide des images de flux standard. Il fonctionne bien par rapport aux algorithmes existants en termes de fiabilité, de précision et de temps de traitement.
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Achyut SAPKOTA, Kazuo OHMI, "A Neural Network Based Algorithm for Particle Pairing Problem of PIV Measurements" in IEICE TRANSACTIONS on Information,
vol. E92-D, no. 2, pp. 319-326, February 2009, doi: 10.1587/transinf.E92.D.319.
Abstract: Particle Image Velocimetry (PIV) is a widely used tool for the measurement of the different kinematic properties of the fluid flow. In this measurement technique, a pulsed laser light sheet is used to illuminate a flow field seeded with tracer particles and at each instance of illumination, the positions of the particles are recorded on digital CCD cameras. The resulting two camera frames can then be processed by various techniques to obtain the velocity vectors. One such techniques involve the tracking of the individual particles so as to identify the displacement of the every particles present in the flow field. The displacement of individual particles thus determined gives the velocity information if divided by known time interval. The accuracy as well as efficiency of such measurement systems depend upon the reliability of the algorithms to track those particles. In the present work, a cellular neural network based algorithm has been proposed. Performance test has been carried out using the standard flow images. It performs well in comparison to the existing algorithms in terms of reliability, accuracy and processing time.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E92.D.319/_p
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@ARTICLE{e92-d_2_319,
author={Achyut SAPKOTA, Kazuo OHMI, },
journal={IEICE TRANSACTIONS on Information},
title={A Neural Network Based Algorithm for Particle Pairing Problem of PIV Measurements},
year={2009},
volume={E92-D},
number={2},
pages={319-326},
abstract={Particle Image Velocimetry (PIV) is a widely used tool for the measurement of the different kinematic properties of the fluid flow. In this measurement technique, a pulsed laser light sheet is used to illuminate a flow field seeded with tracer particles and at each instance of illumination, the positions of the particles are recorded on digital CCD cameras. The resulting two camera frames can then be processed by various techniques to obtain the velocity vectors. One such techniques involve the tracking of the individual particles so as to identify the displacement of the every particles present in the flow field. The displacement of individual particles thus determined gives the velocity information if divided by known time interval. The accuracy as well as efficiency of such measurement systems depend upon the reliability of the algorithms to track those particles. In the present work, a cellular neural network based algorithm has been proposed. Performance test has been carried out using the standard flow images. It performs well in comparison to the existing algorithms in terms of reliability, accuracy and processing time.},
keywords={},
doi={10.1587/transinf.E92.D.319},
ISSN={1745-1361},
month={February},}
Copier
TY - JOUR
TI - A Neural Network Based Algorithm for Particle Pairing Problem of PIV Measurements
T2 - IEICE TRANSACTIONS on Information
SP - 319
EP - 326
AU - Achyut SAPKOTA
AU - Kazuo OHMI
PY - 2009
DO - 10.1587/transinf.E92.D.319
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E92-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2009
AB - Particle Image Velocimetry (PIV) is a widely used tool for the measurement of the different kinematic properties of the fluid flow. In this measurement technique, a pulsed laser light sheet is used to illuminate a flow field seeded with tracer particles and at each instance of illumination, the positions of the particles are recorded on digital CCD cameras. The resulting two camera frames can then be processed by various techniques to obtain the velocity vectors. One such techniques involve the tracking of the individual particles so as to identify the displacement of the every particles present in the flow field. The displacement of individual particles thus determined gives the velocity information if divided by known time interval. The accuracy as well as efficiency of such measurement systems depend upon the reliability of the algorithms to track those particles. In the present work, a cellular neural network based algorithm has been proposed. Performance test has been carried out using the standard flow images. It performs well in comparison to the existing algorithms in terms of reliability, accuracy and processing time.
ER -