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Cellular neural network to detect spurious vectors in PIV data

Cellular neural network to detect spurious vectors in PIV data This paper proposes an artificial neural network (ANN) method to effectively detect spurious velocity vectors in a velocity field measured by particle image velocimetry (PIV). The neural network is a recurrent network referred to as a cellular neural network (CNN). The method is compared with the local-median method to remove measurement outliers. Both artificially generated velocity fields containing known errors and actual experimental data were used to study the performance of these methods. The influences of the velocity gradient and the error percentage are discussed. The CNN model was shown to be more efficient for removal of erroneous vectors. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Experiments in Fluids Springer Journals

Cellular neural network to detect spurious vectors in PIV data

Liang,; Jiang,; Li,
Experiments in Fluids , Volume 34 (1) – Jan 28, 2003
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References (13)

Publisher
Springer Journals
Copyright
Copyright © 2003 by Springer-Verlag
Subject
Engineering; Engineering Fluid Dynamics; Fluid- and Aerodynamics; Engineering Thermodynamics, Heat and Mass Transfer
ISSN
0723-4864
eISSN
1432-1114
DOI
10.1007/s00348-002-0530-8
Publisher site
See Article on Publisher Site

Abstract

This paper proposes an artificial neural network (ANN) method to effectively detect spurious velocity vectors in a velocity field measured by particle image velocimetry (PIV). The neural network is a recurrent network referred to as a cellular neural network (CNN). The method is compared with the local-median method to remove measurement outliers. Both artificially generated velocity fields containing known errors and actual experimental data were used to study the performance of these methods. The influences of the velocity gradient and the error percentage are discussed. The CNN model was shown to be more efficient for removal of erroneous vectors.

Journal

Experiments in Fluids – Springer Journals

Published: Jan 28, 2003

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