• Proceedings of the KSRS Conference
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• 2006.03a
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• pp.165-168
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• 2006

이 논문에서는 한국항공우주연구원 위성영상 검 보정 test site 중 하나인 충남 당진 연구지역에 대하여 AMS 시스템을 이용한 항공촬영과 분광반사도 측정, GCP target 실험, GPS 측량, 토지피복조사 등 ground truth를 통하여 구축된 자료를 소개하고, 구축된 자료를 이용하여 AMS 영상의 분광특성을 분석하였다. AMS 영상 DN값과 야외 분광반사도 측정값과의 선형회귀분석결과, AMS 영상의 모든 spectral band에서 DN값과 분광반사도 측정값이 매우 밀접한 관계가 있음을 알 수 있었다. 선형회귀식을 이용하여 AMS 영상을 reflectance 영상으로 변환하였으며, reflectance 영상과 야외 분광반사도 그래프가 거의 일치하였다.

• The Korean Journal of Applied Statistics
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• v.5 no.2
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• pp.181-192
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• 1992

We consider the estimation of the regression surface in generalized linear models based on tensor-product B-splines in a data-dependent way. Our approach is to use maximum likelihood method to estimate the regression function by a function from a space of tensor-product B-splines that have a finite number of knots and are linear in the tails. The knots are placed at selected order statistics of each coordinate of the sample data. The number of knots is determined by minimizing a variant of AIC. A numerical example is used to illustrate the performance of the tensor spline estimates.

• Journal of the Society of Naval Architects of Korea
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• v.31 no.1
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• pp.32-41
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• 1994

This paper presents the model-ship correlations based on model test results of 36 ships. All of model tests were conducted at KRISO towing tank The correlation factors $C_P,\;C_N,\;and\;C_{NP}$ are estimated by the ITTC Standard Method and compared with the results of another towing tank. In the 36 ships, the block coefficients of thirty ships are greater than 0.72. Nevertheless the comparison of factors is in good agreement. The corrections to the scale effect on wake fraction ${\Delta}{\omega}_c$ and roughness allowance $C_{Ac}$ are subject matter in practice. The correction formulae are proposed by functions of ship length and form factor. And the correction formula of resistance coefficient ${\Delta}C_{Fc}$ based on Townsis's hull roughness formula is presented.

• Journal of Korea Water Resources Association
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• v.47 no.9
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• pp.743-752
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• 2014

Delay time for groundwater recharge means the travel time from the bottom of soil layer to groundwater through vadose zone after infiltration from rainfall. As it is difficult to measure delay time, we suggested an empirical formula which is derived by using linear regression between altitude and delay time. For the regression analysis, 4 major gauging watersheds were chosen (Hancheon, Kangjeongcheon, Oedocheon, Cheonmicheon) with 18 measured groundwater level stations. To verify this empirical formula, derived equation from linear reservoir theory was applied to compute delay time and to compare estimated amounts of groundwater recharge using both methods. The result showed good agreement. Furthermore, if derived empirical formula would be linked with SWAT model, the spatial time delay effect in the watershed could be reflected properly.

• The Korean Journal of Applied Statistics
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• v.10 no.2
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• pp.293-308
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• 1997

Sliced inverse regression is a method for reducing the dimension of the explanatory variable X without going through any parametric or nonparametric model fitting process. This method explores the simplicity of the inverse view of regression; that is, instead of regressing the univariate output varable y against the multivariate X, we regress X against y. In this article, we propose bivariate sliced inverse regression, whose method regress the multivariate X against the bivariate output variables $y_1, Y_2$. Bivariate sliced inverse regression estimates the e.d.r. directions of satisfying two generalized regression model simultaneously. For the application of bivariate sliced inverse regression, we decompose the output variable y into two variables, one variable y gained by projecting the output variable y onto the column space of X and the other variable r through projecting the output variable y onto the space orthogonal to the column space of X, respectively and then estimate the e.d.r. directions of the generalized regression model by utilize two variables simultaneously. As a result, bivariate sliced inverse regression of considering the variable y and r simultaneously estimates the e.d.r. directions efficiently and steadily when the regression model is linear, quadratic and nonlinear, respectively.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.74-78
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• 2009

우리 사회가 발전함에 따라 재해의 위험으로부터도 안전하게 살고자 하는 대중들의 욕구 또한 증가하고 있다. 하지만 최근의 기후변화와 이상홍수의 사례에서 볼 때 현재 우리가 처해 있는 자연재해로부터의 위협은 과거와는 상이하다는 것을 알 수 있다. 이러한 위협에 대처하기 위해서는 우리에게 노출된 재해의 특성을 평가하는 과정이 무엇보다 선행되어져야 한다. 홍수로 인한 피해는 대부분이 인명이나 재산피해가 주를 이루기 때문에 홍수위험도의 평가결과도 발생 가능한 인명이나 재산피해로 표현되는 것이 적절하다고 판단된다. 따라서 본 연구에서는 지역회귀분석을 적용하여 가능 홍수 피해금액을 추산하고, 이를 통해 각 지역별 홍수위험도를 평가하는 방법을 제안하였다. 지역회귀분석은 강우유출모형이나 확률분포모형의 매개변수들을 유역 특성인자들로 표현하기 위해 수문학 분야에서 사용되어져 왔으며 본 연구에서는 이 방법을 홍수 피해금액 추정에 응용하였다. 지역회귀방법의 절차는 먼저 계측지역에서는 홍수 피해금액과 시강우량 자료를 바탕으로 비선형회귀분석을 실시한 후 이 회귀식의 계수를 다시 해당 지역의 인문 사회 경제학적 인자들로 표현하였다. 이러한 방법을 통해 지역적 인자들이 홍수 피해에 미치는 영향을 정량적으로 분석할 수 있었으며 궁극적으로 미계측지역에서도 지역적 인자들을 통해 특정 빈도에 발생 가능한 홍수 피해금액을 추정할 수 있었다. 최종적으로 추정된 홍수 피해금액과 지역 총 자산의 비를 통해 홍수위험지도를 작성하였다. 본 연구결과를 수자원장기종합계획에서 홍수위험도 평가를 위해 사용된 홍수피해잠재능(Potential Flood Damage; PFD)과 비교해 보면 PFD에서는 각 인자들의 가중치 산정에서 전문가의 주관이 개입될 수 있다는 단점이 있었으나 과거 피해금액과의 상관관계를 분석한 본 연구에서는 이러한 단점을 극복할 수 있었다.

• Journal of the Korean Society of Marine Environment & Safety
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• v.18 no.6
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• pp.545-550
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• 2012

Wind resource assessment is necessary when designing wind farm. To get the assessment, we must use a long term(20 years) observed wind data but it is so hard. so that we usually measured more than a year on the planned site. From the wind data, we can calculate wind energy related with the wind farm site. However, it calculate wind energy to collect the long term data from Met-mast(Meteorology Mast) station on the site since the Met-mast is unstable from strong wind such as Typhoon or storm surge which is Non-periodic. To solve the lack of the long term data of the site, we usually derive new data from the long term observed data of AWS(Automatic Weather Station) around the wind farm area using mathematical interpolation method. The interpolation method is called MCP(Measure-Correlative-Predict). In this study, based on the MCP Regression Model proposed by us, we estimated the wind energy at Handong site using AEP(Annual Energy Production) from Gujwa AWS data in Jeju. The calculated wind energy at Handong was shown a good agreement between the predicted and the measured results based on the linear regression MCP. Short term AEP was about 7,475MW/year. Long term AEP was about 7,205MW/year. it showed an 3.6% of annual prediction different. It represents difference of 271MW in annual energy production. In comparison with 20years, it shows difference of 5,420MW, and this is about 9 months of energy production. From the results, we found that the proposed linear regression MCP method was very reasonable to estimate the wind resource of wind farm.

• Composites Research
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• v.30 no.1
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• pp.71-76
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• 2017

The purpose of this study was to predict the long-term performance using the interlaminar shear strength of carbon fiber/epoxy composites exposed to environmental factors. Interlaminar shear specimens, manufactured by the filament winding method, were exposed to the conditions of drying at $50^{\circ}C$, $70^{\circ}C$, and $100^{\circ}C$ and of immersion at $25^{\circ}C$, $50^{\circ}C$, and $70^{\circ}C$ for up to 3000 hours, respectively. According to the results, the interlaminar shear strength did not vary significantly with the exposure time for the drying at $50^{\circ}C$ and $70^{\circ}C$, but it increased somewhat for the drying at $100^{\circ}C$ due to the post curing as the exposure time increased. The interlaminar shear strength of the specimens exposed to the immersion at $25^{\circ}C$ did not change significantly at the beginning of exposure, but it decreased with the exposure time and the degree of decrease increased as the environmental temperature increased. The linear regression equations for the environmental temperatures were obtained from the interlaminar shear strength of the specimens exposed to the immersion for up to 3000 hours. Using these linear regression equations, the interlaminar shear strength was estimated to be within 5.5% of the measured value at $25^{\circ}C$ and $50^{\circ}C$, and 2.3% of the measured value at $70^{\circ}C$. Therefore, the proposed performance prediction procedures can predict well the long-term interlaminar shear strength of carbon fiber/epoxy composites exposed to environmental factors.

• Journal of Korean Society of Environmental Engineers
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• v.39 no.3
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• pp.118-123
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• 2017

This study investigated the feasibility of a statistical approach for the prediction of BaP and total PAHs as pyrogenic sources. As results of regression, excellent linear and multiple correlations ($r^2$ > 0.94) were observed between BaP (or ${\Sigma}PAH$) and Pyr concentrations. When a developed prediction equation was applied to other investigations as validation and application studies, outstanding prediction results were obtained. The predictive model showed very good correlation between the measured and calculated ${\Sigma}PAH$. From this equation, Pyr was an apparently important hydrocarbon for the prediction of PAH. This model might provide a potentially useful tool for the calculation of average BaP and ${\Sigma}PAH$ in a certain region without additional tests.

• Journal of the Society of Naval Architects of Korea
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• v.51 no.6
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• pp.530-538
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• 2014

The weight estimation of floating offshore structures such as FPSO, TLP, semi-Submersibles, Floating Offshore Wind Turbines etc. in the preliminary design, is one of important measures of both construction cost and basic performance. Through both literature investigation and internet search, the weight data of floating offshore structures such as FPSO and TLP was collected. In this study, the weight estimation model was suggested for FPSO. The weight estimation model using non-linear regression analysis was established by fixing independent variables based on this data and the multiple regression analysis was introduced into the weight estimation model. Its reliability was within 4% of error rate.