목차
1. Introduction
2. Hopfield and Cellular Neural Networks
3. Particle pairing algorithm
4. Result
5. Conclusion
6. References
본문내용
The 3-D particle tracking velocimetry is basically composed of two successive processes of particle pairing. The first one is the spatial particle pairing, in which the particles viewed by two (or more than two) stereoscopic cameras with a parallax
(different viewing angles) have to be correctly paired at every synchronized time step. This is quite important because the 3-D coordinates of individual particles cannot be computed without being assured of identification of two particle images.
참고 자료
Chua, L.O., Yang, L., 1988, “Cellular Neural Networks: Theory and Applications”, IEEE
Trans. on Circuits and Systems, Vol.35, No.10, pp.1257-1290
Hopfield, J.J., 1982, “Neural networks and physical systems with emergent collective
computational abilities”, Proc. Nat’l Acad. Sci. USA, Vol.97, pp.2554-2558.
Nishino K., Kagasi N., Hirata M.,1989, “ThreeDimensional particle tracking velocimetry
based on automated digital image processing”, Trans. ASME, J. Fluids Eng. Vol 111, pp.384-391
Ohmi, K., Sapkota, A.,2004, “Improvement in Hopfield Neural Network PTV”, Proc.
International Conference on Advanced Optical Diagnostics in Fluids, Solids and Combustion, Tokyo, Japan, #V0010
Okamoto, K., Nishio, S., Kobayashi, T., Saga, T., 1997, “Standard images for particle
imaging velocimetry”, Proc. PIV-Fukui '97, pp.229-236.