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Journal of Visualization

, Volume 5, Issue 4, pp 363–370 | Cite as

Gradient-based PIV using neural networks

  • Kimura I. 
  • Susaki Y. 
  • Kiyohara R. 
  • Kaga A. 
  • Kuroe Y. 
Article

Abstract

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.

Keywords

PIV neural networks gradient-based method stream function 

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References

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Copyright information

© The Visualization Society of Japan 2002

Authors and Affiliations

  • Kimura I. 
    • 1
  • Susaki Y. 
    • 1
  • Kiyohara R. 
    • 1
  • Kaga A. 
    • 2
  • Kuroe Y. 
    • 3
  1. 1.Osaka Electro-Communication UniversityOsakaJapan
  2. 2.Department of Environmental EngineeringOsaka UniversitySuita, OsakaJapan
  3. 3.Department of Electronics and Information ScienceKyoto Institute of TechnologyKyotoJapan

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