• Nuclear Engineering and Technology
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• v.42 no.2
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• pp.163-174
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• 2010

As pointed out in the OECD BEMUSE Program, when a high computation time is taken to obtain the relevant output values of a complex physical model (or code), the number of statistical samples that must be evaluated through it is a critical factor for the sampling-based uncertainty analysis. Two alternative methods have been utilized to avoid the problem associated with the size of these statistical samples: one is based on Wilks' formula, which is based on simple random sampling, and the other is based on the conventional nonlinear regression approach. While both approaches provide a useful means for drawing conclusions on the resultant uncertainty with a limited number of code runs, there are also some unique corresponding limitations. For example, a conclusion based on the Wilks' formula can be highly affected by the sampled values themselves, while the conventional regression approach requires an a priori estimate on the functional forms of a regression model. The main objective of this paper is to assess the feasibility of the ACE-RSM approach as a complementary method to the Wilks' formula and the conventional regression-based uncertainty analysis. This feasibility was assessed through a practical application of the ACE-RSM approach to the LOFT L2-5 LBLOCA PCT uncertainty analysis, which was implemented as a part of the OECD BEMUSE Phase III program.

• Proceedings of the Korean Geotechical Society Conference
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• 2009.09a
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• pp.1065-1078
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• 2009

The shear strength has been managed as an important factor in soil mechanics. The shear strength estimation model was developed to evaluate the shear strength using only a few soil properties by the linear regression analysis model which is one of the statistical methods. The shear strength is divided into two part; one is the internal friction angle ($\Phi$) and the other is the cohesion (c). Therefore, some valid soil factors among the results of soil tests are selected through the correlation analysis using SPSS and then the model are formulated by the linear regression analysis based on the relationship between factors. Also, the developed model is compared with the result of direct shear test to prove the rationality of model. As the results of analysis about relationship between soil properties and shear strength, the internal friction angle is highly influenced by the void ratio and the dry unit weight and the cohesion is mainly influenced by the void ratio, the dry unit weight and the plastic index. Meanwhile, the shear strength estimated by the developed model is similar with that of the direct shear test. Therefore, the developed model may be used to estimate the shear strength of soils in the same condition of study area.

• Steel and Composite Structures
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• v.37 no.2
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• pp.193-209
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• 2020

In this paper, the efficiency of five Machine Learning (ML) methods consisting of Deep Learning (DL), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and Gradient Tree Booting (GTB) for regression and classification of the Ultimate Load Factor (ULF) of nonlinear inelastic steel frames is compared. For this purpose, a two-story, a six-story, and a twenty-story space frame are considered. An advanced nonlinear inelastic analysis is carried out for the steel frames to generate datasets for the training of the considered ML methods. In each dataset, the input variables are the geometric features of W-sections and the output variable is the ULF of the frame. The comparison between the five ML methods is made in terms of the mean-squared-error (MSE) for the regression models and the accuracy for the classification models, respectively. Moreover, the ULF distribution curve is calculated for each frame and the strength failure probability is estimated. It is found that the GTB method has the best efficiency in both regression and classification of ULF regardless of the number of training samples and the space frames considered.

• Water for future
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• v.17 no.2
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• pp.107-112
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• 1984

It si presented here that in order to estimate reservoir sedimentation rate through the use of reservoir survey data of 66 irrigation reservoir in 3 major watersheds in this country, the correlation between reservoir sedimentation rate and the following factors; watershed area, trap-efficiency, watershed slope, shape factor of water shed, and reservoir deposition age in two models simple regression model and multiple regression model. Appropriatness of the proposed models have been calibrated from the survey data and as a result, it has been determined that the multiple regression model is much more accurate than the simple regression model. The annual sediment yield is correlated with watershed area and reservoir trap efficiency. It has been found that variation of the annual average sedimentation rate and the annual reservoir capacity loss rate are influenced by the trap efficiency of reservoir.

• Journal of the Korean Society of Clothing and Textiles
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• v.35 no.11
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• pp.1322-1332
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• 2011

This study investigates the influence of social desirability to questionnaire response and data analysis in order to identify the need for social desirability control in clothing consumer research. A questionnaire measuring social desirability, social face sensitivity, clothing shopping behavior, and demographic characteristics was developed. Responses of 234 respondents were analyzed using factor analysis, simple regression analysis, hierarchical regression analysis, descriptive analysis, and Cronbach's alpha analysis. The results were as follow. First, respondents were influenced by social desirability when they responded to items measuring other-conscious social face. Second, the result of regression analysis (that the independent variable was social formality) was less influenced by social desirability control because the influence of social desirability to social formality was insignificant. Conversely, the result of regression analysis (that the independent variable was other-conscious social face) was more influenced by social desirability control because the influence of social desirability to other-conscious social face was significant. This study is an initial study that notices the need for social desirability control in clothing consumer research.

• Journal of the Society of Naval Architects of Korea
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• v.58 no.4
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• pp.234-242
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• 2021

In this study, we introduce the prediction of brake power for low-speed full ships and container carriers using the linear regression and a machine learning approach. The residual resistance coefficient, wake fraction coefficient, and thrust deduction factor are predicted by regression models using the main dimensions of ship and propeller. The brake power of a ship can be calculated by these coefficients according to the 1978 ITTC performance prediction method. The mean absolute error of the predicted power was under 7%. As a result of several validation cases, it was confirmed that the machine learning model showed slightly better results than linear regression.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.4
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• pp.118-126
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• 2022

This study attempts a comparison between AHP(Analytic Hierarchy Process) in which the importance weight is structured by individual subjective values and regression model with importance weight based on statistical theory in determining the importance weight of casual model. The casual model is designed by for students' satisfaction with university, and SERVQUAL modeling methodology is applied to derive factors affecting students' satisfaction with university. By comparison of importance weights for regression model and AHP, the following characteristics are observed. 1) the lower the degree of satisfaction of the factor, the higher the importance weight of AHP, 2) the importance weight of AHP has tendency to decrease as the standard deviation(or p-value) increases. degree of decreases. the second sampling is conducted to double-check the above observations. This study empirically checks that the importance weight of AHP has a relationship with the mean and standard deviation(or p-value) of independence variables, but can not reveal how exactly the relationship is. Further research is needed to clarify the relationship with long-term perspective.

• Analytical Science and Technology
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• v.34 no.6
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• pp.241-250
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• 2021

Calibration curves are essential in quantitative methods and for improving the accuracy of analyte measurements in biological samples. In this study, a statistical analysis model built in the R language (The R Foundation for Statistical Computing) was used to identify a set of weighting factors and regression models based on a stepwise selection criteria. An LC-MS/MS method was used to detect the presence of urinary methamphetamine, amphetamine, and 11-nor-9-carboxy-Δ⁹ -tetrahydrocannabinol in a sample set. Weighting factors for the calibration curves were derived by calculating the heteroscedasticity of the measurements, where the presence of heteroscedasticity was determined via variance tests. The optimal regression model and weighting factor were chosen according to the sum of the absolute percentage relative error. Subsequently, the order of the regression model was calculated using a partial variance test. The proposed statistical analysis tool facilitated selection of the optimal calibration model and detection of methamphetamine, amphetamine, and 11-nor-9-carboxy-Δ⁹-tetrahydrocannabinol in urine. Thus, this study for the selection of weighting and the use of a complex regression equation may provide insights for linear and quadratic regressions in analytical and bioanalytical measurements.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.38 no.2
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• pp.108-117
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• 2020

This study takes the Gyeongju Historic Areas Daereungwon Area as the research object, uses the Virtual Walkthrough method for virtual experience and evaluation, the visual characteristics were analyzed. To analyze landscape visual characteristics and to find out the relationship between image factors and visual preferences, the factor analysis and multiple regression analysis were conducted. The results are as follows: the results of preference analysis of the Daereungwon Area show that the preference of scene3 where located on the western boundary of Noseo-rl tumuli got the highest score, and the preference score of scene5 where located on the western boundary of Daereungwon less than three points. The results of factor analysis of visual characteristics, three factors were analyzed: regularity factor, spatiality factor and historical factor. The analysis of variance and multiple regression analysis results of the relationship between factor scores and visual preferences show that regularity factor was analyzed as the biggest factor that affects the visual preference of the Historical-cultural landscape Daereungwon Area. Virtual Walkthrough method has a strong three-dimensional and strong production of the real landscape scene, it's an effective method in landscape analysis. The results of research provides data and information for improving the visual quality of Historical-cultural landscape and it's expected to be applied in the future of landscape planning.

• Tunnel and Underground Space
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• v.22 no.2
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• pp.120-129
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• 2012

Recently, mining facilities have being installed in an underground space according to a social demand for environment-friendly mine development. The underground structures for mining facilities usually requires a large volume of space with width greater than height, and thus the stability assessment of the large-scale underground mine structure is an important issue. In this study, we analysed a factor of safety based on strength reduction method, and proposed a numerical design approach to determining the dimension of underground mine structures in combination with a strength reduction method and a multivariate regression analysis. Input design parameters considered in the present study were the stress ratio and shear strength of rock mass, and the width and cover depth of underground mine structures. The stabilities of underground mine structures were assessed in terms of factor of safety under different conditions of the above input parameters. It was calculated by the strength reduction method, and several kinds of fit functions were obtained through various multivariate regression analyses. Using a best-fit regression model, we proposed the charts which provide preliminary design information on the dimension of underground mine structures.