• 대기
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• 제24권4호
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• pp.491-502
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• 2014

As a part of the KIAPS (Korea Institute of Atmospheric Prediction Systems) Package for Observation Processing (KPOP), we have developed the modules for Advanced Microwave Sounding Unit-A (AMSU-A) pre-processing and its bias correction. The KPOP system calculates the airmass bias correction coefficients via the method of multiple linear regression in which the scan-corrected innovation and the thicknesses of 850~300, 200~50, 50~5, and 10~1 hPa are respectively used for dependent and independent variables. Among the four airmass predictors, the multicollinearity has been shown by the Variance Inflation Factor (VIF) that quantifies the severity of multicollinearity in a least square regression. To resolve the multicollinearity, we adopted simple linear regression and Principal Component Regression (PCR) to calculate the airmass bias correction coefficients and compared the results with those from the multiple linear regression. The analysis shows that the order of performances is multiple linear, principal component, and simple linear regressions. For bias correction for the AMSU-A channel 4 which is the most sensitive to the lower troposphere, the multiple linear regression with all four airmass predictors is superior to the simple linear regression with one airmass predictor of 850~300 hPa. The results of PCR with 95% accumulated variances accounted for eigenvalues showed the similar results of the multiple linear regression.

• Journal of the Korean Statistical Society
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• 제31권4호
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• pp.509-518
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• 2002

This paper is concerned with semiparametric Bayesian analysis of the proportional intensity regression model of the Poisson process for multiple event time data. A nonparametric prior distribution is put on the baseline cumulative intensity function and a usual parametric prior distribution is given to the regression parameter. Also we allow heterogeneity among the intensity processes in different subjects by using unobserved random frailty components. Gibbs sampling approach with the Metropolis-Hastings algorithm is used to explore the posterior distributions. Finally, the results are applied to a real data set.

• 대한조선학회논문집
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• 제51권6호
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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.

• Journal of the Korean Statistical Society
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• 제16권2호
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• pp.55-70
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• 1987

The principal components regression is one of the substitues for least squares method when there exists multicollinearity in the multiple linear regression model. It is observed graphically that the performance of the principal components regression is strongly dependent upon the values of the parameters. Accordingly, a new deletion criterion which determines proper principal components to be deleted from the analysis is developed and its usefulness is checked by simulations.

• International Journal of Air-Conditioning and Refrigeration
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• 제10권2호
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• pp.65-71
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• 2002

The objective of this study is to evaluate the design parameters and to develop the cooling and heating load prediction equations of office buildings. The building load calculation simulation was carried out using the DOE-2.1E program. The results of the simulation were used as data for multiple regression analysis which could develop the load prediction equations.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2007년도 춘계학술대회 논문집
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• pp.397-399
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• 2007

This paper presents multiple regression analysis and data update to improve present spatial electric load forecasting algorithm of the DISPLAN. Spatial electric load forecasting considers a local economy, the number of local population and load characteristics. A Case study is performed for Jeon-Ju and analyzes a trend of the spatial load for the future 20 years. The forecasted information can contribute to an asset management of distribution systems.

• 한국건축시공학회지
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• 제12권6호
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• pp.636-647
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• 2012

Construction projects have become so large, complicated and incredibly high-tech that process management is currently considered one of the most important issues. Unlike typical manufacturing industries, most major construction activities are performed in the open air and thus are exposed to various environmental factors. Many studies have been conducted with the goal of establishing efficient techniques and tools for overcoming these limitations. Productivity analysis and prediction, one of the related research subjects, must be considered when evaluating approaches to reducing construction duration and costs. The aim of this research is to present a quantified impact analysis of construction delay factors on construction productivity of a steel staircase system, which has been widely applied to high rise building construction. It is also expected to improve the process by managing the factors, ultimately achieving an improvement in construction productivity. To achieve the research objectives, this paper analyzed different delay factors affecting construction duration by means of multiple regression analysis focusing on steel staircase systems, which have critical effects on the preceding and subsequent processes in structure construction. Statistical analysis on the multiple linear regression model indicated that the environment, labor and material delay factors were statistically significant, with 0.293, 0.491, and 0.203 as the respective quantified impacts on productivity.

• 지질공학
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• 제16권1호
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• pp.85-96
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• 2006

본 연구는 지질특성에 따라 해당 지역의 지질조건과 특성을 최대한 반영한 RMR 항목별 가중치를 결정하는 방법을 제안하기 위하여 수행되었다. 이를 위해 본 연구에서는 우선 울산지역에 분포하는 백악기 퇴적암을 대상으로 RMR 분류의 인자별 배점체계를 검토하였다. 이 과정에서 기존 연구들이 수행한 경험적 연구방법을 탈피하여 다중회귀분석을 이용한 객관적 과학적 방법으로 인자별 배점을 설정하였다. 이를 통해 인자별 배점설정의 객관적 근거를 확보한 수정된 RMR 분류체계를 제안하고, 그 결과를 지질학적 관점에서 평가하였다. 다중회귀분석을 위해 RMR 항목과 사면경사를 각각 독립변수와 종속변수로 설정하였다. 다중회귀분석을 통한 수정된 RMR 항목별 가중치는 무결암의 강도가 30, RQD가 18, 불연속면 간격이 8, 불연속면 조건이 32, 그리고 지하수 12로 나타났다.

• 한국대기환경학회지
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• 제32권5호
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• pp.513-525
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• 2016

To propose an effective ensemble methods in predicting $PM_{10}$ concentration, six experiments were designed by different ensemble average methods (e.g., non-weighted, single weighted, and cluster weighted methods). The single weighted method was calculated the weighted value using both multiple regression analysis and singular value decomposition and the cluster weighted method was estimated the weighted value based on temperature, relative humidity, and wind component using multiple regression analysis. The effects of ensemble average methods were significantly better in weighted average than non-weight. The results of ensemble experiments using weighted average methods were distinguished according to methods calculating the weighted value. The single weighted average method using multiple regression analysis showed the highest accuracy for hourly $PM_{10}$ concentration, and the cluster weighted average method based on relative humidity showed the highest accuracy for daily mean $PM_{10}$ concentration. However, the result of ensemble spread analysis showed better reliability in the single weighted average method than the cluster weighted average method based on relative humidity. Thus, the single weighted average method was the most effective method in this study case.

• 한국건축시공학회지
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• 제16권5호
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• pp.437-445
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• 2016

태풍은 인류에 큰 피해를 주는 재난재해로 몇몇 선진국에서는 태풍으로 인한 건축물 피해액 사전예측 모델에 관한 연구가 진행되고 있다. 국내에서도 해외 연구를 토대로 국내에 적용시키는 연구가 진행되었지만, 태풍의 특성이나 크기 등이 차이가 나므로 국내에 적합한 모델이 필요한 실정이다. 또한, 국내의 연구는 태풍의 특성, 지역적 특성만을 고려하여 진행 하였으나, 태풍은 복합재해로서 태풍의 특성, 지리적 특성만이 아닌 태풍의 진로, 건설환경, 등 다양한 요인을 고려하여야한다. 이에 본 연구에서는 국내에 영향을 미친 태풍을 7가지 타입으로 분류하여 건물피해액 영향인자를 도출하고, 회귀분석을 실시하여 태풍 타입별 건물피해율 예측모델을 개발 목적으로 한다. 이는 선진국의 자연재해 예측모델들과 같이 국내의 상황에 맞는 태풍에 따른 피해를 예측하기 위한 모델 개발을 위한 자료로 활용 될 것이다.