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Details for article 6 of 25 found articles
| Title: |
Gradient-based PIV Using Neural Networks |
| Author: |
I. Kimura Y. Susaki R. Kiyohara A. Kaga Y. Kuroe |
| Appeared in: |
Journal of visualization |
| Paging: | Volume 5 (2003) nr. 4 pages 363-370 |
| Year: |
2003-02-03 |
| Contents: |
This paper proposes a new gradient-based PIV using an artificial neural network for acquiring the characteristics of a two-dimensional flow field. The neural network can effectively realize an accurate approximation of the vector field by introducing some knowledge on the characteristic property. The neural network is trained by using spatial and temporal image gradients so that the basic equation of the gradient-based method is satisfied. Since the neural network itself learns the stream function, the continuity equation of flow is consequently satisfied in the measured velocity vector field. The new gradient-based PIV can be applied to even partly lacking visualized images. |
| Publisher: |
IOS Press |
| Source file: |
Elektronische Wetenschappelijke Tijdschriften |
Details for article 6 of 25 found articles
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