Artificial frame filling using adaptive neural fuzzy inference system for particle image velocimetry dataset

Bayram Akdemir; Sercan Doğan; Muharrem Hilmi Aksoy; Eyüp Canli; Muammer Özgören

doi:10.1117/12.2179689

4 March 2015 Artificial frame filling using adaptive neural fuzzy inference system for particle image velocimetry dataset

Bayram Akdemir, Sercan Doğan, Muharrem Hilmi Aksoy, Eyüp Canli, Muammer Özgören

Author Affiliations +

Proceedings Volume 9443, Sixth International Conference on Graphic and Image Processing (ICGIP 2014); 94431R (2015) https://doi.org/10.1117/12.2179689
Event: Sixth International Conference on Graphic and Image Processing (ICGIP 2014), 2014, Beijing, China

Abstract

Liquid behaviors are very important for many areas especially for Mechanical Engineering. Fast camera is a way to observe and search the liquid behaviors. Camera traces the dust or colored markers travelling in the liquid and takes many pictures in a second as possible as. Every image has large data structure due to resolution. For fast liquid velocity, there is not easy to evaluate or make a fluent frame after the taken images. Artificial intelligence has much popularity in science to solve the nonlinear problems. Adaptive neural fuzzy inference system is a common artificial intelligence in literature. Any particle velocity in a liquid has two dimension speed and its derivatives. Adaptive Neural Fuzzy Inference System has been used to create an artificial frame between previous and post frames as offline. Adaptive neural fuzzy inference system uses velocities and vorticities to create a crossing point vector between previous and post points. In this study, Adaptive Neural Fuzzy Inference System has been used to fill virtual frames among the real frames in order to improve image continuity. So this evaluation makes the images much understandable at chaotic or vorticity points. After executed adaptive neural fuzzy inference system, the image dataset increase two times and has a sequence as virtual and real, respectively. The obtained success is evaluated using R² testing and mean squared error. R² testing has a statistical importance about similarity and 0.82, 0.81, 0.85 and 0.8 were obtained for velocities and derivatives, respectively.

Citation Download Citation

Bayram Akdemir, Sercan Doğan, Muharrem Hilmi Aksoy, Eyüp Canli, and Muammer Özgören "Artificial frame filling using adaptive neural fuzzy inference system for particle image velocimetry dataset", Proc. SPIE 9443, Sixth International Conference on Graphic and Image Processing (ICGIP 2014), 94431R (4 March 2015); https://doi.org/10.1117/12.2179689

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