• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.2
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• pp.511-516
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• 2013

The thickness and deflection of melting parts of the glass edge reach the biggest effect on the intensity and thermal insulation performance. During the sealing process using a hydrogen mixed gas torch, the thickness and the deflection effect of the edge part are affected by process parameters. In order to analyze the correlative relationship of the thickness prediction and the deflection of the edge part according to the process parameters, data was obtained by conducting sealing experiments. The main effects and interaction effects of process parameters for the thickness and the shape of the glass edge parts were analyzed through the design of experiment. A mathematical experiment equation that can predict the thickness and deflection of the edge part according to the process parameters was developed by conducting multiple regression equations.

• Journal of Korea Water Resources Association
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• v.55 no.4
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• pp.257-266
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• 2022

Inundation damage is increasing every year due to localized heavy rain and an increase of rainfall exceeding the design frequency. Accordingly, the importance of hydraulic structures for flood control and defense is also increasing. The hydraulic structures are designed according to its purpose and performance, and the amount of flood is an important calculation factor. However, in Korea, design rainfall is used as input data for hydrological analysis for the design of hydraulic structures due to the lack of sufficient data and the lack of reliability of observation data. Accurate probability rainfall and its temporal distribution are important factors to estimate the design rainfall. In practice, the regression equation of temporal distribution for the design rainfall is calculated using the cumulative rainfall percentage of Huff's quartile method. In addition, the 6^th order polynomial regression equation which shows high overall accuracy, is uniformly used. In this study, the optimized regression equation of temporal distribution is derived using the variable selection method according to the principle of parsimony in statistical modeling. The derived regression equation of temporal distribution is verified through the significance test. As a result of this study, it is most appropriate to derive the regression equation of temporal distribution using the stepwise selection method, which has the advantages of both forward selection and backward elimination.

• Journal of Korea Water Resources Association
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• v.51 no.3
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• pp.263-272
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• 2018

For the design of hydraulic structures, the design flood discharge corresponding to a specific frequency is generally used by using the design storm calculated according to the rainfall-runoff relationship. In the past, empirical equations such as rational equations were used to calculate the peak flow rate. However, as the duration of rainfall is prolonged, the outflow patterns are different from the actual events, so the accuracy of the temporal distribution of the probability rainfall becomes important. In the present work, Huff's quartile method is used for the temporal distribution of rainfall, and the third quartile is generally used. The regression equation for Huff's quadratic curve applies a sixth order polynomial equation because of its high accuracy throughout the duration of rainfall. However, in statistical modeling, the regression equation needs to be concise in accordance with the principle of simplicity, and it is necessary to determine the regression coefficient based on the statistical significance level. Therefore, in this study, the statistical significance test for regression equation for temporal distribution of the Huff's quartile method, which is used as the temporal distribution method of design rainfall, is conducted for 69 rainfall observation stations under the jurisdiction of the Korea Meteorological Administration. It is statistically significant that the regression equation of the Huff's quartile method can be considered only up to the 4th order polynomial equation, as the regression coefficient is significant in most of the 69 rainfall observation stations.

• Journal of Navigation and Port Research
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• v.26 no.5
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• pp.529-535
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• 2002

Design and implementation methodologies on the performance evaluation system of Two-Channel Ring-Core Flux-Gate Compass (TCRC FG-Compass) are described, with evaluation procedures and methods based on the polynomial regression models. Performance evaluation system consists of a step motor driving unit, a bearing transmitting unit and evaluation programs derived from polynomial regression formulae. Newly designed performance evaluation system enabled the accuracy of TCRC FG-Compass to be ascertained. It was confirmed that the size of residual deviation of TCRC FG-Compass is $2^{\circ}$, while that of the conventional one is $4^{\circ}$. In addition, the design methodology to the self estimation and correction of residual deviations is also discussed.

• Proceedings of the KSRS Conference
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• 2006.03a
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• pp.113-116
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• 2006

지표 변수는 지면 근처의 기후변화 및 상태를 파악하는데 중요한 역할을 하기 때문에, 충분히 높은 정확성을 가진 값이 산출되어야 한다. 하지만 이러한 지표 변수는 구름과 눈, 그리고 강수등에 의해서 그 값이 변화하게 된다. 이러한 오차 값을 줄이기 위해 구름제거, 지리보정, 대기보정 등의 위성 전처리 과정이 수행되었다. 하지만 위성 전처리 과정을 수행한 이후에도 정규식생지수 시계열 자료에는 여전히 노이즈가 남아 있기 때문에 이전에 연구에서는 이동 평균등과 같은 다양한 방법으로 노이즈를 제거하고자 하였다. 하지만 이동평균 방법은 참값에 가까운 최고값도 제거하기 때문에 문제점을 가지고 있다. 본 연구에서는 다중 다항회귀식을 이용하여 정규식생지수 시계열 자료 산출시 발생하는 노이즈를 제거 하였다.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2002.11a
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• pp.13-19
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• 2002

Design and implementation methodologies on the performance evaluation system of two-channel ring-core Flux-Gate Compass (FG-Compass) are described, with evaluation procedures and methods based on the polynomial regression models. Performance evaluation system is consists of a step motor driving unit, a bearing transmitting unit and, evaluation programs using polynomial regression formulae. Through performance evaluation tests, total residual deviation tests, total residual deviation of $\pm$4$^{\circ}$ and eigen residual deviation of $\pm$2$^{\circ}$ are obtained from the FG-Compass. The result is more accurate values than the typical FG-Compass with eigen residual deviation of $\pm$4$^{\circ}$ and is provide a possibility to develop a high performance FG-Compass. In addition, the design methodology of a smart FG-Compass with the self estimation and correction of residual deviations is also discussed.

• Korean Journal of Remote Sensing
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• v.30 no.4
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• pp.505-510
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• 2014

Normalized Difference Vegetation Index (NDVI) is a major indicator for monitoring climate change and detecting vegetation coverage. In order to retrieve NDVI, it is preprocessed using cloud masking and atmospheric correction. However, the preprocessed NDVI still has abnormally low values known as noise which appears in the long-term time series due to rainfall, snow and incomplete cloud masking. An existing method of using polynomial regression has some problems such as overestimation and noise detectability. Thereby, this study suggests a simple method using amoving average approach for correcting NDVI noises using SPOT/VEGETATION S10 Product. The results of the moving average method were compared with those of the polynomial regression. The results showed that the moving average method is better than the former approach in correcting NDVI noise.

• Korean Journal of Remote Sensing
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• v.26 no.4
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• pp.403-410
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• 2010

NDVI(Normalized Difference Vegetation Index), which is broadly used as short-term data composite, is an important parameter for climate change and long-term land surface monitoring. Although atmospheric correction is performed, NDVI dramatically appears several low peak noise in the long-term time series. They are related to various contaminated sources, such as cloud masking problem and wet ground condition. This study suggests a simple method through harmonic analysis for reducing NDVI noise using SPOT/VGT NDVI 10-day MVC data. The harmonic analysis method is compared with the polynomial regression method suggested previously. The polynomial regression method overestimates the NDVI values in the time series. The proposed method showed an improvement in NDVI correction of low peak and overestimation.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.6B
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• pp.543-550
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• 2009

The traditional simple extrapolation type short term quantitative rainfall forecast can not realize the evolution of rainfall generating weather system. To overcome the drawback of the linear extrapolation type rainfall forecasting model, the history of a weather system from sequential weather radar information and a polynomial regression technique were used to generate forecast fileds of x-directional, y-directional velocities and radar reflectivity which considered the nonlinear behavior related to the evolution of weather systems. Results demonstrated that test statistics of forecasts using the developed model is better than that of 2-CAPPI forecast. However there is still a large room to improve the forecast of spatial and temporal evolution of local storms since the model is not based on a fully physical approach but a statistical approach.

• Journal of Korea Water Resources Association
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• v.57 no.1
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• pp.9-19
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• 2024

As using public monitoring data, analysing a trends of water quality change, establishing a criteria to determine abnormal status and constructing a regression model that can predict Chlorophyll-a, an indicator of eutrophication, was studied. Accordingly, the three freshwater lakes were selected, approximately 20 years of water quality monitoring data were analyzed for periodic changes in water quality each year using regression analysis, and a method for determining abnormalities was presented by the standard deviation at confidence level 95%. By calculating the temporal change rate of Chlorophyll-a from irregular observed data, analyzing correlations between the rate and other water quality items, and constructing regression models, a method to predict changes in Chlorophyll-a was presented. The results of this study are expected to contribute to freshwater lake water quality management as an approximate water quality prediction method using the statistical model.