• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.41 no.2
• /
• pp.123-131
• /
• 2021

The buffer is a key component of an engineered barrier system that safeguards the disposal of high-level radioactive waste. Buffers are located between disposal canisters and host rock, and they can restrain the release of radionuclides and protect canisters from the inflow of ground water. Since considerable heat is released from a disposal canister to the surrounding buffer, the thermal conductivity of the buffer is a very important parameter in the entire disposal safety. For this reason, a lot of research has been conducted on thermal conductivity prediction models that consider various factors. In this study, the thermal conductivity of a buffer is estimated using the machine learning methods of: linear regression, decision tree, support vector machine (SVM), ensemble, Gaussian process regression (GPR), neural network, deep belief network, and genetic programming. In the results, the machine learning methods such as ensemble, genetic programming, SVM with cubic parameter, and GPR showed better performance compared with the regression model, with the ensemble with XGBoost and Gaussian process regression models showing best performance.

• The Korean Journal of Applied Statistics
• /
• v.29 no.3
• /
• pp.399-408
• /
• 2016

There is a need to detect heteroscedasticity in a regression analysis; however, it invalidates the standard inference procedure. The diagnostics on heteroscedasticity may be distorted when both outliers and heteroscedasticity exist. Available heteroscedasticity detection methods in the presence of outliers usually use robust estimators or separating outliers from the data. Several approaches have been suggested to identify outliers in the heteroscedasticity problem. In this article conventional tests on heteroscedasticity are modified by using a sequential outlier detection methods to separate outliers from contaminated data. The performance of the proposed method is compared with original tests by a Monte Carlo study and examples.

• The Journal of the Korea Contents Association
• /
• v.14 no.8
• /
• pp.394-405
• /
• 2014

Purposes of this study are to find difference in efficiency, productivity change and determinants on efficiency of life insurance industry between pre and post-execution of capital market law on the basis of pre(2005-2008) and post-execution(2009-2012) data. Main results of this research are as follows. First, there was no significant difference of mean value for efficiency between pre and post execution of capital market law. Second, difference of mean value for malmquist index between pre and post execution of capital market law was statistically significant at 5% level. This imply increase in productivity for post-execution of capital market law. Third, one of the important determinant for efficiency was the weight of life planner in both pre and post-execution of capital market law. The weight of life planner had significant positive effect on efficiency.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.17 no.4
• /
• pp.112-118
• /
• 2013

The selection of appropriate ground motions and reasonable modification are becoming increasingly critical in reliable prediction on seismic performance of structures. A widely used amplitude scaling approach is not sufficient for robust structural evaluation considering a site specific seismic hazard because only one spectral value is matched to the design spectrum typically at the structural fundamental period. Hence alternative approaches for ground motion selection and modifications have been suggested. However, there is no means to evaluate such methodologies yet. In this study, it is focused to describe the main questions resided in the amplitude scaling approach and to propose a regression model for structural damage as point of comparison. Spectrum compatible approach whose resulting spectrum matches the design spectrum at the entire range of the structural period is considered as alternative to be compared to the amplitude scaling approach. The design spectrum is generated according to ASCE7-05.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.9 no.4
• /
• pp.871-877
• /
• 2008

It is important to investigate the relationship between weld process parameters and weld bead geometry for adaptive arc robot welding. Howeve, it is difficult to predict an exact back-bead owing to gap in process of butt welding. In this paper, the quantitative prediction system to specify the relationship external weld conditions and weld bead geometry was developed to get suitable back-bead in butt welding which is widely applied on industrial field. Multiple regression analysis for the prediction of process parameters was used as the research method. And, the results of the prediction method were compared and analyzed.

• The Korean Journal of Applied Statistics
• /
• v.13 no.2
• /
• pp.407-414
• /
• 2000

Carroll and Ruppert(1988) analyzed the esterase assay data with regression model based on quasi-likelihood. Jung and Lee(1997) introduced a goodness-of-fit test for testing the adequacy of the quasi-likelihood and claimed that there is no gross inadequacy with the model because their test was not rejected. However, Lee and Xelder(199S)'s residual plots revealed that the model did not sufficiently reflect the increase of the variance with that of the mean. In this paper, we re-analyze the esterase assay data with the joint modelling of mean and dispersion in Lee and l\elder(1998) and evaluate the validity of the fitted model by applying the residual plots. And it is illustrated that Lee and Nelder(199S)'s restricted likelihood is more efficient in goodness-of-fit test for the dispersion model.

• Journal of the Korean Data and Information Science Society
• /
• v.21 no.3
• /
• pp.513-521
• /
• 2010

Most of universities have carried out course evaluation to apply the performance appraisal for professor. But, course evaluation depends on characteristics of each class such as class size, type of lecture, evaluator's grade and so on. As the results, such characteristics of each class lead to serious bias which makes lecturers distrust the course evaluation results. Hence, we propose a equating method for the course evaluation by regression analysis which use stepwise variable selection. And we compare proposed method with the other method by Cho et al. (2009) with respect to efficiencies. Also we give the example to which the method is applied.

• Composites Research
• /
• v.17 no.2
• /
• pp.1-8
• /
• 2004

The present paper is concerned with the optimum design of taper spring, in which the static spring rate of the fiber-reinforcement composite material spring is fitted to that of the steel leaf spring. The thickness and width of springs were selected as design variables. The object functions of the regression model were obtained through the analysis with a common analytic program. After regression coefficients were calculated to get functions of the regression model, optimal solutions were calculated with DOT. E-glass/epoxy and carbon/epoxy were used as fiber reinforcement materials in the design, which were compared and analyzed with the steel leaf spring. The result of the static spring rates show that optimized composite leaf springs agree with steel leaf spring within 1%.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.35 no.1
• /
• pp.153-164
• /
• 2015

The methods of design of mat foundation may be classified as the rigid method and the flexible method according to the assumptions used. In the rigid method, the mat is assumed to be infinitely rigid and the contact pressure is assumed planar distribution. However, the contact pressure is not planar but curved surface because the real mat is not rigid. Therefore, it is not precise to analyze the mat foundation using the rigid method, and so there is no choice but to accept an error. On the other hand, in the flexible method, the mat is considered as the plate on the elastic foundation. This elastic plate theory is for the infinite plate acting a concentrated load on the elastic foundation. However, the functions for the moment, shear, and the deflection by the flexible method are very complex, there are many difficulties for the designer to use them. Also, it is impossible to use the design aid figures as a substitute of the complex functions, because they do not cover the values at the critical sections for the moment and shear. Therefore, in this research, the simplified functions for the moment, shear, and the deflection are proposed by regression analysis for an designer to use easily the flexible method. The simplified functions are very accurate and very ease to use.

• Korean Journal of Remote Sensing
• /
• v.31 no.6
• /
• pp.523-530
• /
• 2015

A Multiple Regression Method (MRM) is used for the first time with Ozone Monitoring Instrument (OMI) and Moderate Resolution Imaging Spectroradiometer (MODIS) data to estimate formaldehyde (HCHO) Vertical Column Density (VCD). For a 3.5-year period from January 2005 through July 2008, HCHO VCD estimation is investigated in cities over Asia in two categorized areas: (1) Major cities in Northeast Asia (Beijing, Seoul, and Tokyo), (2) Major cities in Southeast Asia (New Delhi, Dhaka, and Bangkok). In the Major cities in Northeast Asia, there are good agreements between HCHO estimated by the multiple linear regression method ($HCHO_{MRM}$) and HCHO measured by OMI ($HCHO_{OMI}$) (0.78 < $R^2$ < 0.82). However, in Major cities in Southeast Asia, there were poor agreements between $HCHO_{OMI}$ and $HCHO_{MRM}$ (0.24 < $R^2$ < 0.39). In addition, an unbiased assessment of the MRM performance using modeling and validation groups shows that the performance of the MRM based on separate modeling and validation groups is comparable to that using all the data for deriving Multiple Regression Equations (MREs). This study demonstrates that MRM can be an alternative tool for HCHO estimation in certain areas over Asia.