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Seo, Hyeon-Kyo;Um, Jeong-Gi
- Tunnel and Underground Space
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- v.22 no.3
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- pp.196-204
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- 2012
Quantification of roughness plays an important role in modeling strength deformability and fluid flow behaviors of rock joints. A procedure was suggested to simulate joint roughness, and characteristics of the roughness was investigated in this study. Stationary fractional Brownian profiles with known input values of the fractal parameter and other profile properties were generated based on random midpoint displacement method. Also, a procedure to simulate three dimensional roughness surface was suggested using the random midpoint displacement method. Selected statistical roughness parameters were calculated for the generated self-affine profiles to investigate the attribute of roughness. Obtained results show that statistical parameters applied in this study were able to consider correlation structure and amplitude of the profiles. However, effect of data density should be tackled to use statistical parameters for roughness quantification.
https://doi.org/10.7474/TUS.2012.22.3.196 인용 PDF KSCI

Goo, Bong-Hoe;Lee, Seung-Ho
- Journal of IKEEE
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- v.18 no.4
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- pp.478-484
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- 2014
In this paper, we propose new algorithm for positioning of mouse cursor using fingertip direction on kinect depth camera. The proposed algorithm uses center of parm points from distance transform when fingertip point toward screen. Otherwise, algorithm use fingertip points. After image preprocessing, the center of parm points is calculated from distance transform results. If the direction of the finger towards the camera becomes close to the distance between the fingertip point and center of parm point, it is possible to improve the accuracy of positioning by using the center of parm point. After remove arm on image, the fingertip points is obtained by using a pixel on the long distance from the center of the image. To calculate accuracy of mouse positioning, we selected any 5 points. Also, we calculated error rate between reference points and mouse points by performed 500 times. The error rate results could be confirmed the accuracy of our algorithm indicated an average error rate of less than 11%.
https://doi.org/10.7471/ikeee.2014.18.4.478 인용 PDF KSCI