• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.4B
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• pp.363-374
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• 2008

Intensity-Duration-Frequency (IDF) curve that is essential to calculate rainfall quantiles for designing hydraulic structures in Korea is generally formulated by regression analysis. In this study, IDF curve derived by the cumulative distribution function ("IDF by CDF") of the proper probability distribution function (PDF) of each site is suggested, and the corresponding parameters of IDF curve are computed using genetic algorithm (GA). For this purpose, IDF by CDF and the conventional IDF derived by regression analysis ("IDF by REG") were computed for 22 Korea Meteorological Administration (KMA) rainfall recording sites. Comparisons of RMSE (root mean squared error) and RRMSE (Relative RMSE) of rainfall intensities computed from IDF by CDF and IDF by REG show that IDF by CDF is more accurate than IDF by REG. In order to accommodate the effect of the recent intensive rainfall of Korea, the rainfall intensities computed by the two IDF curves are compared with that by at-site frequency analysis using the rainfall data recorded by 2006, and the result from IDF by CDF show the better performance than that from IDF by REG. As a result, it can be said that the suggested IDF by CDF curve would be the more efficient IDF curve than that computed by regression analysis and could be applied for Korean rainfall data.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.1
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• pp.29-41
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• 2017

Design rainfalls are one of the most important hydrologic data for river management, hydraulic structure design and risk analysis. The design rainfalls are first estimated by a point frequency analysis and the IDF (intensity-duration-frequency) curve is then constructed by a nonlinear regression to either interpolate or extrapolate the design rainfalls for other durations which are not used in the frequency analysis. It has been widely recognised that the more reliable approaches are required to better account for uncertainties associated with the model parameters under circumstances where limited hydrologic data are available for the watershed of interest. For these reasons, this study developed a hierarchical Bayesian based GLM (generalized linear model) for a regional frequency analysis in conjunction with a scaling function of the parameters in probability distribution. The proposed model provided a reliable estimation of a set of parameters for each individual station, as well as offered a regional estimate of the parameters, which allow us to have a regional IDF curve. Overall, we expected the proposed model can be used for different aspects of water resources planning at various stages and in addition for the ungaged basin.

• International Journal of Automotive Technology
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• v.7 no.2
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• pp.227-235
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• 2006

In this paper a robust image processing method with modified Otsu algorithm to recognize the road lane for a real-time controlled autonomous vehicle is presented. The main objective of a proposed method is to drive an autonomous vehicle safely irrespective of road image qualities. For the steering of real-time controlled autonomous vehicle, a detection area is predefined by lane segment, with previously obtained frame data, and the edges are detected on the basis of a lane width. For stable as well as psudo-robust autonomous driving with "good", "shady" or even "bad" road profiles, the variable threshold with modified Otsu algorithm in the image histogram, is utilized to obtain a binary image from each frame. Also Hough transform is utilized to extract the lane segment. Whether the image is "good", "shady" or "bad", always robust and reliable edges are obtained from the algorithms applied in this paper in a real-time basis. For verifying the adaptability of the proposed algorithm, a miniature vehicle with a camera is constructed and tested with various road conditions. Also, various highway road images are analyzed with proposed algorithm to prove its usefulness.