研究者業績

能勢 和夫

ノセ カズオ  (Kazuo Nose)

基本情報

所属
大阪産業大学 デザイン工学部 情報システム学科 教授
学位
工学博士(京都大学)

J-GLOBAL ID
200901022109545726
researchmap会員ID
1000183200

委員歴

 1

MISC

 137
  • Joshi, B, Ohmi, K, Nose, K
    Int. J. of Innovative Computing, Information and Control 9(5) 1971-1986 2013年  
  • Basante Joshi, Kazuo Ohmi, Kazuo Nose
    Proceedings of the IASTED International Conference on Signal Processing, Pattern Recognition and Applications, SPPRA 2013 431-438 2013年  
    Novel 3D image analysis and particle matching techniques for the use in the volumetric particle tracking velocimetry have been developed and tested by using synthetic images and experimental images of unsteady 3D flows. A tomography based particle reconstruction scheme along with the subsequent process of individual particle detection and validation was used. The detected particles in the two time differential samples are matched by using Self Organising Map (SOM) neural network scheme. SOM neural network tracking algorithm is highly adaptive to time differential tracking even with loss-of-pair particles. The particle location and velocity results of the present new approach turned out accurate, reliable and robust in comparison to the conventional 3D PTV approaches.
  • Joshi, B, Ohmi, K, Nose, K
    Int. J. of Innovative Computing, Information and Control 9(5) 1971-1986 2013年  
  • Basante Joshi, Kazuo Ohmi, Kazuo Nose
    Proceedings of the IASTED International Conference on Signal Processing, Pattern Recognition and Applications, SPPRA 2013 431-438 2013年  
    Novel 3D image analysis and particle matching techniques for the use in the volumetric particle tracking velocimetry have been developed and tested by using synthetic images and experimental images of unsteady 3D flows. A tomography based particle reconstruction scheme along with the subsequent process of individual particle detection and validation was used. The detected particles in the two time differential samples are matched by using Self Organising Map (SOM) neural network scheme. SOM neural network tracking algorithm is highly adaptive to time differential tracking even with loss-of-pair particles. The particle location and velocity results of the present new approach turned out accurate, reliable and robust in comparison to the conventional 3D PTV approaches.
  • Joshi, B, Ohmi, K, Nose,K
    J. of Fluid Science and Technology 7(3) 242-258 2012年  
    New algorithms of 3D particle tracking velocimetry (3D PTV) based on a tomographic reconstruction approach have been developed and tested by using synthetic images of unsteady 3D flows. The new algorithms are considered not only in the tomographic reconstruction process of the fluid volume with particles but also in the subsequent process of individual particle detection and validation. In particular, the tomographic reconstruction accuracy is boosted up by using a new recursive validation scheme through which many of ghost particles can be removed effectively. The particle detection process includes the particle mask correlation operator and the dynamic threshold scheme to extract individual particle centroids from the reconstructed intensity clusters of the fluid volume. The overall reconstruction accuracy is checked by the synthetic image data sets with different particle density and different volume thickness.

Works(作品等)

 9