Skip to main content
SpringerLink
Log in

Gradient-based PIV using neural networks

  • Published:
Journal of Visualization Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Grant, I., Particle Image Velocimetry: a review, Proc. Instn. Mech. Engrs., 211-C, (1997), 55–76.

    Google Scholar 

  • Horn, B. K. P. and Schunck, B. G., Determining Optical Flow, Artificial Intelligence, 17 (1981), 185–203.

    Article  Google Scholar 

  • Ido, T., Murai, Y., Ohta, J. and Yamamoto, F., High Resolution Measurement of Flow Based on High-Order Image Reconstruction, CD-ROM Proc. 3rd ASME/JSME Joint Fluids Engineering Conference, FEDSM99-7071, (1999).

  • Kuroe, Y., Mitsui, M., Kawakami, H. and Mori, T., A Learning Method for Vector Field Approximation by Neural Networks, Proc. of 1998 IEEE International Joint Conference on Neural Networks (IJCNN’98) (Anchorage), (1998), 2300–2305.

  • Okuno, T., Image Measurement by means of Spatio-Temporal Derivative Method, Proc. The International Workshop on PIV-Fukui ’95 (Fukui, Japan), (1995), 167–173.

Download references

Author information

Authors and Affiliations

  1. Osaka Electro-Communication University, Hatsu-cho, Neyagawa, 572-8530, Osaka, Japan

    Kimura I., Susaki Y. & Kiyohara R.

  2. Department of Environmental Engineering, Osaka University, Yamadaoka, 565-0871, Suita, Osaka, Japan

    Kaga A.

  3. Department of Electronics and Information Science, Kyoto Institute of Technology, Matsugasaki, Sakyo-ku, 606-8585, Kyoto, Japan

    Kuroe Y.

Authors
  1. Kimura I.
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Susaki Y.
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kiyohara R.
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kaga A.
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kuroe Y.
    View author publications

    You can also search for this author in PubMed Google Scholar

Additional information

Ichiro Kimura: He received his M.S. degree in Instrumentation Engineering from Kobe University, Kobe, Japan in 1972 and his Dr. E. degree in Mechanical Engineering for industrial machinery from Osaka University, Osaka, Japan, in 1983. He was a research associate from 1972 to 1984 and an associate professor from 1984 to 1993 of Instrumentation Engineering at the Faculty of Engineering, Kobe University. He is currently a professor of Electromechanical Engineering at Osaka Electro-Communication University, Osaka, Japan. He has been engaged in research on quantitative flow visualization using image processing and information processing on human sensibility. He is a member of the American Society of Mechanical Engineers, the Society of Instrument and Control Engineers, the Robotics Society of Japan, the Visualization Society of Japan, etc.

Yasunori Susaki: He received his B.S. degree in Electromechanical Engineering in 1998 and his M.S. degree in Mechanical and Control Engineering in 2000 from Osaka Electro-Communication University, Osaka, Japan. He currently works for Konami Corporation.

Ryuji Kiyohara: He received his B.S. degree in Electromechanical Engineering from Osaka Electro-Communication University, Osaka, Japan in 2001. He currently works for Kawamura Seni Corporation.

Akikazu Kaga: He received his B.E. degree in 1969, M.E. in 1971 in Mechanical Engineering, and his D.E. degree in 1985 in Environmental Engineering from Osaka University. He has been working in Osaka University from 1971 as a research associate, from 1988 as an associate professor and from 2000 as a professor. His current research interests are airflow measurement using flow visualization and image processing, and the analysis and management of air pollution problems in Asian cities.

Yasuaki Kuroe: He received his Ph.D. in Industrial Science from Kobe University, Kobe, Japan in 1982. In the same year he joined the faculty of Department of Electrical Engineering, Kobe University as a research associate. He is currently an associate professor at the Department of Electronics and Information Science, Kyoto Institute of Technology, Kyoto, Japan. His research interests are in the areas of control theory and its application, computer-aided analysis and design and neuro-computing and its applications. He is a member of IEEE, IEE Japan, the Society of Instrument and Control Engineers, the Institute of Systems, Control and Information Engineers, the Robotics Society of Japan, etc.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kimura, I., Susaki, Y., Kiyohara, R. et al. Gradient-based PIV using neural networks. J Vis 5, 363–370 (2002). https://doi.org/10.1007/BF03182351

Download citation

  • Received:

  • Revised:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF03182351

Keywords

Search

Navigation