• KSCE Journal of Civil and Environmental Engineering Research
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• v.13 no.5
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• pp.191-199
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• 1993

Investigation is given to the detailed procedure of a computer assisted automatic technique for ortho-image generation from digital stereo image data of close-range photographs scanned by the CCD camera scanner. After rectification of geometric scanning errors, the bundle adjustment technique was used to determine the exterior orientation parameters of terrestrial camera. An automatic correlation matching technique was applied to search for the conjugate pixels in digital stereo pairs. And the 3-dimensional coordinates of the corresponding pixels were calculated by the space intersection method. For the generation of ortho-image from the calculated coordinates and right image data values, inverse-weighted-distance average method was used. And the accuracy of the resulting ortho-image was checked by comparing its image coordinates with there corresponding ground coordinates for the check points.

• The Korean Journal of Applied Statistics
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• v.5 no.2
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• pp.243-254
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• 1992

In the present study two sets of unbalanced two-way cross-classification data with and without empty cell(s) were used to evaluate empirically the various sums of squares in the analysis of variance table. Searle(1977) and Searle et.al.(1981) developed a method of computing R($\alpha$\mid$\mu, \beta$) and R($\beta$\mid$\mu, \alpha$) by the use of partitioned matrix of X'X for the model of no interaction, interchanging the columns of X in order of $\alpha, \mu, \beta$ and accordingly the elements in b. An alternative way of computing R($\alpha$\mid$\mu, \beta$), R($\beta$\mid$\mu, \alpha$) and R($\gamma$\mid$\mu, \alpha, \beta$) without interchanging the columns of X has been found by means of,$(X'X)^-$ derived, using $W_2 = Z_2Z_2-Z_2Z_1(Z_1Z_1)^-Z_1Z_2$. It is true that $R(\alpha$\mid$\mu,\beta,\gamma)\Sigma = SSA_W and R(\beta$\mid$\mu,\alpha,\gamma)\Sigma = SSB_W$ where $SSA_W$ and means analysis and $R(\gamma$\mid$\mu,\alpha,\beta) = R(\gamma$\mid$\mu,\alpha,\beta)\Sigma$ for the data without empty cell, but not for the data with empty cell(s). It is also noticed that for the datd with empty cells under W - restrictions $R(\alpha$\mid$\mu,\beta,\gamma)_W = R(\mu,\alpha,\beta,\gamma)_W - R(\mu,\alpha,\beta,\gamma)_W = R(\alpha$\mid$\mu) and R(\beta$\mid$\mu,\alpha,\gamma)_W = R(\mu,\alpha,\beta,\gamma)_W - R(\mu,\alpha,\beta,\gamma)_W = R(\beta$\mid$\mu) but R(\gamma$\mid$\mu,\alpha,\beta)_W = R(\mu,\alpha,\beta,\gamma)_W - R(\mu,\alpha,\beta,\gamma)_W \neq R(\gamma$\mid$\mu,\alpha,\beta)$. The hypotheses $H_o : K' b = 0$ commonly tested were examined in the relation with the corresponding sums of squares for $R(\alpha$\mid$\mu), R(\beta$\mid$\mu), R(\alpha$\mid$\mu,\beta), R(\beta$\mid$\mu,\alpha), R(\alpha$\mid$\mu,\beta,\gamma), R(\beta$\mid$\mu,\alpha,\gamma), and R(\gamma$\mid$\mu,\alpha,\beta)$ under the restrictions.

• KDI Journal of Economic Policy
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• v.35 no.4
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• pp.31-61
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• 2013

The purpose of this study is to calculate the economic value of transport demand forecasting risks in the road PPP project. Under the assumption that volatility of the road PPP project value occurs only in regard with uncertainty of traffic volume forecasting, this study calculates the economic value of the traffic forecasting risks in the case of the road PPP project. To that end, forecasted traffic volume is assumed to be a stochastic variable and to follow the Geometric Brownian motion as time passes. In particular, this study attempts to differentiate itself from existing studies that simply use an arbitrary assumption by presenting the application of different traffic volume growth volatility and the rates before and after the ramp-up period. Analysis of the case projects reveals that the risk premium related to traffic volume forecast of the project turns out as 7.39~8.30%, without considering option value-such as minimum revenue guarantee-while the project value volatility caused by transport demand forecasting risks is 17.11%. As the discount rate grows higher, the project value volatility tends to decrease and volatility in project value is always suggested to be larger than that in transport volume influenced by leverage effect due to fixed expenditure. The market value of transport demand forecasting risk-calculated using the project value volatility and risk premium-is analyzed to be between 0.42~0.50, implying that a 1% increase or decrease in the transport amount volatility would lead to a 0.42~0.50% increase or decrease in risk premium of the project.