• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.6_1
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• pp.529-538
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• 2012

By using an algorithm derived by a multiple linear regression analysis, a technique for filtering was developed; and by using the developed technique, the results of conducting filtering of the raw data collected via scanning with a terrestrial LiDAR the actual sloped terrain was analyzed. As such, when filtering was applied by dividing the observation areas into two areas with the topographical line as a reference in order to improve the filtering accuracy, it was seen that the filtering accuracy improved by about 8.73% as compared to when filtering was applied without dividing the observation area. In addition, considering the fact that the accuracy improved by 5~7% when the sloped sides of a multicurvature topography were divided and a complex filtering applied as compared to when filtering was applied for the entire area or by regions, it can be asserted that the accuracy was higher when a complex filtering was conducted by dividing the sloped areas where the slope is not constant due to the multi-curvature of topography.

• Korean Journal of Clinical Laboratory Science
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• v.38 no.1
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• pp.16-21
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• 2006

Parallel testing means ordering a number of tests at the same time so abnormalities in any of the tests can be found quickly and used in making the diagnosis. This is a good medical strategy to eliminate diseases and it is relatively inexpensive if all the tests are potential sources of information and performed on the same analyzer. In regression, the equation for the straight line is recast as y = bx + a. This change in terminology leads to confusion. Here a is the y-intercept or constant and b is the coefficient or slope of the line. A few more words of caution about regression - as in all of statistics there are certain assumptions: the x value is a true measure, both X and Y distributions are normal, and homoscedasticity, i.e., the variance of y is the same for each value of x. In this study the linearity classification made by different scientists were always in agreement. Typical examples of curves that were considered linear are presented in Fig. 1-5. Because these automated procedures values were usually within five percent of each other the curvature could be easily detected. The plot of the WBC, RBC, hemoglobin, hematocrit and platelet concentrations from approximately 74.4 to $0{\times}10^3/{\mu}L$ and $80.4-0{\times}10^3/{\mu}L$, $5.6-0{\times}10^6/{\mu}L$ and $6.1-0{\times}1106/{\mu}L$, 18.3-0 g/dL and 19.0-0 g/dL, 54.1-0% and 56.8-0% and 642.0 to $0.03{\times}10^3{\mu}L$ and $754.0-0{\times}10^3/{\mu}L$ on the ADVIA 2120 C Versus and A and B typical of an acceptable linear study as shown in Fig. 1-5. The grand mean of R2, intercept and slope is 0.99898, 0.99459 and 1.54626.