• Nuclear Engineering and Technology
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• v.48 no.2
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• pp.559-571
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• 2016

This paper studies the feasibility of renewable energy as a substitute for nuclear and energy by considering Korean customers' willingness to pay (WTP). For this analysis, we use the contingent valuation method to estimate the WTP of renewable energy, and then estimate its value using ordered logistic regression. To replace nuclear power and fossil energy with renewable energy in Korea, an average household is willing to pay an additional 102,388 Korean Won (KRW) per month (approx. US $85). Therefore, the yearly economic value of renewable energy in Korea is about 19.3 trillion KRW (approx. US $16.1 billion). Considering that power generation with only renewable energy would cost an additional 35 trillion KRW per year, it is economically infeasible for renewable energy to be the sole method of low-carbon energy generation in Korea.

• Journal of Microbiology and Biotechnology
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• v.11 no.2
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• pp.199-203
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• 2001

A simple quantitative method to measure the degree of silanization was developed, based on the reaction of ninhydrin with the silanization reagent (3-aminopropyltriethoxysilane, 3-APTES). At low concentrations (0.001-0.005%, v/v) of 3-APTES, a good linearity was obtained when 3-APTES reacted with undiluted ninhydrin for 30 min. On the other hand, at high levels of 3-APTES, a linearity was obtained when 3-APTES reacted with 3-fold diluted ninhydrin for 20 min. The reliability of regression curves mentioned above was expressed as a regression coefficient ($R^2$) of more than 0.99. Immobilization of different enzymes was introduced via silanization by using the 3-APTES in order to confirm the validity of the ninhydrin method. When yield for each step in the immobilizatio process were compared, yields of both glutaraldehyde and protein were founc to have the same tendency to silanization. These results shw that the ninhydrin method was suitable for quatitative analysis of silanization and that yields of immobilization could be pre-estimated by measuring silanization levels using the ninhydrin method.

• International Journal of Advanced Culture Technology
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• v.7 no.1
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• pp.133-142
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• 2019

This study aims to understand how different construction methods impact scenic preference of green wall and clarify features of each construction method to help select the most suitable construction method for the wanted image of a green wall by providing the basic data for further development and distribution of green wall. Questionnaire developed by the Repertory Grid technique proved that 11 adjectives can be used to describe scenic features of a green wall and 4 preference elements. The result of the Scenic evaluation, the Felt type scored high in 'Aesthetic' and 'Maintenance' meaning that it is the most suitable method when constructing a green wall to improve urban scenery. Regression analysis was conducted to understand the link between the preference elements and scenic impression of a green wall. The result is that the higher the preference is on the design of a green wall, the higher the score is for 'Aesthetic'. Also, the higher the preference is on Bio-Diversity, Design, Growth, the higher the score is for 'Natural'. The above findings can be important measures and reference for selection of the right construction method when planning a green wall.

• Proceedings of the Korean Nuclear Society Conference
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• 2002.05a
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• pp.54-54
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• 2002

• Journal of the Korea Society of Computer and Information
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• v.25 no.3
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• pp.109-115
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• 2020

In this paper, we propose an artificial intelligence(AI) technology analysis using partial least square(PLS) regression model. AI technology is now affecting most areas of our society. So, it is necessary to understand this technology. To analyze the AI technology, we collect the patent documents related to AI from the patent databases in the world. We extract AI technology keywords from the patent documents by text mining techniques. In addition, we analyze the AI keyword data by PLS regression model. This regression model is based on the technique of partial least squares used in the advanced analyses such as bioinformatics, social science, and engineering. To show the performance of our proposed method, we make experiments using AI patent documents, and we illustrate how our research can be applied to real problems. This paper is applicable not only to AI technology but also to other technological fields. This also contributes to understanding other various technologies by PLS regression analysis.

• Journal of Korea Water Resources Association
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• v.41 no.9
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• pp.943-958
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• 2008

This study employs Bayesian regression analysis for fitting discharge rating curves. The parameter estimates using the Bayesian regression analysis were compared to ordinary least square method using the t-distribution. In these comparisons, the mean values from the t-distribution and the Bayesian regression are not significantly different. However, the difference between upper and lower limits are remarkably reduced with the Bayesian regression. Therefore, from the point of view of uncertainty analysis, the Bayesian regression is more attractive than the conventional method based on a t-distribution because the data size at the site of interest is typically insufficient to estimate the parameters in rating curve. The merits and demerits of the two types of estimation methods are analyzed through the statistical simulation considering heteroscedasticity. The validation of the Bayesian regression is also performed using real stage-discharge data which were observed at 5 gauges on the Anyangcheon basin. Because the true parameters at 5 gauges are unknown, the quantitative accuracy of the Bayesian regression can not be assessed. However, it can be suggested that the uncertainty in rating curves at 5 gauges be reduced by Bayesian regression.

• Communications for Statistical Applications and Methods
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• v.19 no.5
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• pp.721-729
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• 2012

Various methods control the influence of a covariate on a response variable. These methods are analysis of covariance(ANCOVA), RANK ANCOVA, ANOVA of (covariate-adjusted) residuals, and Kruskal-Wallis tests on residuals. Covariate-adjusted residuals are obtained from the overall regression line fit to the entire data set that ignore the treatment levels or factors. It is demonstrated that the methods on covariate-adjusted residuals are only appropriate when the regression lines are parallel and covariate means are equal for all treatments. In this paper, we proposed the new nonparametric method on the ANCOVA model, as applying joint placement in a one-way layout on residuals as described in Chung and Kim (2007). A Monte Carlo simulation study is adapted to compare the power of the proposed procedure with those of the previous procedure.

• Computers and Concrete
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• v.13 no.2
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• pp.173-185
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• 2014

Multivariate analysis is a statistical technique that investigates relationship between multiple predictor variables and response variable and it is a very commonly used statistical approach in cement and concrete industry. During model building stage, however, many predictor variables are included in the model and possible collinearity problems between these predictors are generally ignored. In this study, use of partial least squares (PLS) analysis for evaluating the relationships among the cement and concrete properties is investigated. This regression method is known to decrease the model complexity by reducing the number of predictor variables as well as to result in accurate and reliable predictions. The experimental studies showed that the method can be used in the multivariate problems of cement and concrete industry effectively.

• Journal of the Korean Geosynthetics Society
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• v.15 no.2
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• pp.65-75
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• 2016

In this study, we constructed a database by collecting field pullout test data of the soil nailing using pressurized grouting, and suggested a method to estimate the ultimate pullout resistance using nonlinear regression analysis to overcome the problems of ultimate pullout resistance estimation using graphical methods. The load-displacement curve estimated by nonlinear regression showed a very high correlation with the field pullout test data. Estimated ultimate pullout load by nonlinear regression method was average 29% higher than estimated ultimate pullout load using previous graphical method. A sigmoidal growth model was found to be the best-fitting nonlinear regression model against rapid pullout failure. Further, an asymptotic regression model was found to be the best fit against progressive nail pullout. The unit ultimate skin friction suggested in this research reflected in the domestic geotechnical characteristics and the specifications of the pressurized grouting method. This research is expected to contribute towards establishing an independent design standard for the soil nailing by providing solutions to the problems that occur when using design charts based on foreign research.

• Molecules and Cells
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• v.26 no.4
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• pp.338-343
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• 2008

An embryonic stem cell is a powerful tool for investigation of early development in vitro. The study of embryonic stem cell mediated neuronal differentiation allows for improved understanding of the mechanisms involved in embryonic neuronal development. We investigated expression profile changes using time course cDNA microarray to identify clues for the signaling network of neuronal differentiation. For the short time course microarray data, pattern analysis based on the quadratic regression method is an effective approach for identification and classification of a variety of expressed genes that have biological relevance. We studied the expression patterns, at each of 5 stages, after neuronal induction at the mRNA level of embryonic stem cells using the quadratic regression method for pattern analysis. As a result, a total of 316 genes (3.1%) including 166 (1.7%) informative genes in 8 possible expression patterns were identified by pattern analysis. Among the selected genes associated with neurological system, all three genes showing linearly increasing pattern over time, and one gene showing decreasing pattern over time, were verified by RT-PCR. Therefore, an increase in gene expression over time, in a linear pattern, may be associated with embryonic development. The genes: Tcfap2c, Ttr, Wnt3a, Btg2 and Foxk1 detected by pattern analysis, and verified by RT-PCR simultaneously, may be candidate markers associated with the development of the nervous system. Our study shows that pattern analysis, using the quadratic regression method, is very useful for investigation of time course cDNA microarray data. The pattern analysis used in this study has biological significance for the study of embryonic stem cells.