• Journal of Industrial Convergence
• /
• v.20 no.2
• /
• pp.1-9
• /
• 2022

As the need for improvement of transparency and fairness in the selection of national R&D projects has been continuously raised, we analyzed the impact on the evaluation selection results by evaluation indexes for The land transportation technology commercialization support project and searched for ways to improve indexes using the analysis results. As for the research data, it were applied as selection results of new R&D projects and evaluation indexes in two fields(SME innovation and start-up) in 2021. Logistic regression analysis is used for the influence of each evaluation indexes on the evaluation result, and for the regression model, evaluation indexes with low influence are removed in advance through artificial neural network multiple perceptron analysis to improve the reliability of the analysis results. As a result of the analysis, in the field of SME innovation, the influence of the evaluation index on the workforce planning was the lowest and the influence of the appropriateness of commercialization promotion plan was the highest. In the start-up field, the influence of the evaluation indexes for technology development suitability, marketability, and suitability for carrying out the project were estimated to be similar to each other, and the influence of the technology evaluation index was found to be the lowest. The analysis results of this thesis suggest the need for continuous improvement of selection and evaluation indexes, and by using the analysis results to select a fair R&D institution according to the selection of appropriate indexes, it will be possible to contribute to deriving excellent research results and fostering excellent companies in the field of land transportation.

• The Korean Journal of Applied Statistics
• /
• v.36 no.2
• /
• pp.141-166
• /
• 2023

This paper investigates synthetic data generation methods and their evaluation measures. There have been increasing demands for releasing various types of data to the public for different purposes. At the same time, there are also unavoidable concerns about leaking critical or sensitive information. Many synthetic data generation methods have been proposed over the years in order to address these concerns and implemented in some countries, including Korea. The current study aims to introduce and compare three representative synthetic data generation approaches: Sequential regression, nonparametric Bayesian multiple imputations, and deep generative models. Several evaluation metrics that measure the utility and disclosure risk of synthetic data are also reviewed. We provide empirical comparisons of the three synthetic data generation approaches with respect to various evaluation measures. The findings of this work will help practitioners to have a better understanding of the advantages and disadvantages of those synthetic data methods.

• Korean Journal of Social Welfare Studies
• /
• v.41 no.3
• /
• pp.443-475
• /
• 2010

Despite the rapidly increased concern on the circularity of the program development activities in the social service agencies, there are only a few studies about the phenomena. This study is to describe the characteristics of program development in the process of adopting, developing, and disseminating the social programs and to figure out the factors influencing differences in the 3 activities in social service agencies. The researchers constructed an explanative model including each 12-13 independent variables with 3 consecutive dependent variables on the basis of reviewing the related literatures. A multiple regression analysis was applied to predict the features of the program development using 195 questionaries responded from social workers in community service centers. As the result of the descriptive analysis, the two noticeable features are found; (1) the agencies have very actively adopted outside programs, developed appropriate programs for the agency, and disseminated the programs into other agencies in the community. (2) there are some positive aspects of the factors in related to the process of the program development. The results of the regression analysis show that the three dependent variables of the adoption, development, and dissemination are very closely interconnected with each others, showing the evidence of the circularity in the agencies. In addition, the 5 independent variables at the value of p .01 are statistically related with the circularity of the three dependent activities. The implication of major findings were discussed in academic and practical perspectives in Korea, including future research works in the area.

• Asia Marketing Journal
• /
• v.15 no.1
• /
• pp.107-128
• /
• 2013

When Korea's retail industry was liberalized, new store formats such as large discount stores and Super Supermarket(SSMs) have grown. New types of business have borne significant influence on traditional market. Traditional markets have been in gradual decline since they fail to meet to consumer's purchasing behavior. The South Korean government has been making sustained efforts to revitalize the modernization of traditional markets since 2004. This research is conducted to analyze how changes in the distribution of different types of distributors influence the consumer's purchasing behaviors depending on the changes in the market environment. The purpose of this research is to present a policy to invigorate consumer-oriented traditional markets by analyzing the consumption behavior among major retail channels at a point when competition among retail channels is becoming intensified. In order to examine the effect of the spatial competitive landscape among major retail channels on consumption behavior, an empirical analysis was conducted with 613 consumers in 6 cities nationwide, using the multiple regression model. This research identified three main areas of factors. The analysis result indicates that the physical factor (time required to go to the traditional market), socioeconomic factors (the number of vehicles owned and average monthly income), and competitive factors (intensity of competition in spatial locations and average monthly spending in supermarkets) have significant influence on consumption patterns of consumers. The findings present that the Korean government should go ahead with policies aimed to revitalize traditional markets, keeping in mind the factors that influence the consumption patterns of customers based on these results. We propose that the policy supporting traditional markets need to be a customized-strategy, considering traditional market's characteristic.

• Journal of the Korean Applied Science and Technology
• /
• v.39 no.6
• /
• pp.783-791
• /
• 2022

This Research is a descriptive study conducted to identify the academic stress, interpersonal relationships, and degree of adaptation to college life of nursing students who experienced COVID-19, and to identify factors influencing college life adaptation. The subjects of the research were sophomore students enrolled in 3 university nursing departments in G city. For data analysis, descriptive statistics, t-test, ANOVA, Pearson's correlation coefficient, and multiple regression analysis were analyzed. The research result showed a significant negative correlation (r=-.584, p<.001) for academic stress and college life adaptation, and a significant positive correlation (r=.505, p<.001) for interpersonal relationships and college life adaptation. The regression model to confirm the influencing factors on college life adaptation was shown to be significant (F=64.462 p<.001). Academic stress (β=-.542, p<.001), interpersonal relationships (β=.339, p<.001), and housing type (β=.199, p<.001) were found to be significant predictive factors. The explanatory power of these variables was 54.6%. Through the results of this research, it will be possible to provide basic data for developing educational programs to reduce academic stress, improve positive and smooth interpersonal relationships, and improve emotional support for college life adaptation.

• Journal of Advanced Technology Convergence
• /
• v.3 no.1
• /
• pp.1-11
• /
• 2024

This study was a descriptive research study conducted to determine how nursing students' good death awareness and nursing attitudes toward dying patients affect their empathy. The subjects of the study were 155 nursing students, and data were collected using an online survey method. Data analysis was performed using descriptive statistics, independent t-test, one-way ANOVA, and multiple regression using the IBM SPSS Statistics 26. Higher attitude toward care of dying (B=.312) had a statistically significant positive effect on empathy capacity (p<.010). The variables that affected nursing students' empathy capacity were end-of-life experiences of relatives (𝛽=.226) and attitude toward care of dying (𝛽=.220). The regression model was statistically significant (F=6.968, p<.001), explained 10.4% of empathy. This study is expected to be used as basic data for the development of programs to strengthen the empathy capacity of nursing students in the future.

• Korean Chemical Engineering Research
• /
• v.56 no.4
• /
• pp.568-576
• /
• 2018

In order to investigate the adsorption characteristics of the antibiotics trimethoprim (TMP) by activated carbon (WCAC) prepared from waste citrus peel, the effects of operating parameters on the TMP adsorption were investigated by using a response surface methodology (RSM). Batch experiments were carried out according to a four-factor Box-Behnken experimental design with four input parameters : concentration ($X_1$: 50-150 mg/L), pH ($X_2$: 4-10), temperature ($X_3$: 293-323 K), adsorbent dose ($X_4$: 0.05-0.15 g). The experimental data were fitted to a second-order polynomial equation by the multiple regression analysis and examined using statistical methods. The significance of the independent variables and their interactions was assessed by ANOVA and t-test statistical techniques. Statistical results showed that concentration of TMP was the most effective parameter in comparison with others. The adsorption process can be well described by the pseudo-second order kinetic model. The experimental data of isotherm followed the Langmuir isotherm model. The maximum adsorption amount of TMP by WCAC calculated from the Langmuir isotherm model was 144.9 mg/g at 293 K.

• Journal of Environmental Science International
• /
• v.23 no.11
• /
• pp.1827-1833
• /
• 2014

This study has been carried out to present the valuation system of soil carbon sequestration potentials of soil in accordance with the new climate change scenarios(RCP). For that, by analyzing variation of soil carbon of the each type of agricultural land use, it aims to develop technology to increase the amount of carbon emissions and sequestration. Among the factors which affects the estimation of determining the soil carbon model and influence power after the measurement on soil organic carbon, under the center of a causal relationship between the explanatory variables this study were investigated. Chemical fertilizers (NPK) decreased with increasing the amount of soil organic carbon and as with the first experimental results, when cultivating rice than pepper, the fact that soil organic carbon content increased has been found out. The higher the carbon dioxide concentration, the higher the amount of organic carbon in the soil and this result is reliable under a 10% significance level. On the other hand, soil organic carbon, humus carbon and hot water extractable carbon has been found out that was not affected the soils depth, sames as the result of the first year. The higher concentration of carbon dioxide, the higher carbon content of humus and hot water extractable carbon content. According to IPCC 2006 Guidelines and the new climate change scenario RCP 4.5 and the measurement results of the total amount of soil organic carbon to the crops due to abnormal climate weather, 1% increase in atmospheric carbon dioxide concentration was found to be small when compared to the growing rate of increasing 0.01058% of organic carbon in the soil.

• Journal of the Economic Geographical Society of Korea
• /
• v.19 no.4
• /
• pp.694-711
• /
• 2016

This study analyzes the spatial characteristics of knowledge industry which has shown relatively rapid growth in the low-growth economy situation in recent years. In particular, we catch hold of the locational characteristics of the knowledge service industry which occupies the highest ratio by professional-expert jobs favoured by young generations, as well as estimate their occupational employment opportunities. By applying Location Quotient(LQ) and LISA, we reveal the spatial distribution patterns of publishing business, information service business and education service business in the Seoul Metropolitan area, and examine the changes in the spatial patterns during the last ten years. In order to understand the socio-economic factors which explain their locations, we apply the stepwise multiple regression analysis. Furthermore, we predict the changes distribution of Knowledge service industrial employment by applying Markov Chain Model. As the result, we found their clusters at the specific locations, while there is the significant variations in the socio-economic variables related their locations respectively. The related job opportunities of the knowledge service businesses in the Seoul Metropolitan area are predicted steady growth trend for the next four years, even though dull or stagnant trend is expected for other industries. This study provides basic resources to the planning for young generation employment problem.

• Analytical Science and Technology
• /
• v.24 no.6
• /
• pp.544-555
• /
• 2011

The acute toxicity in the rainbow trout (Oncorhynchus mykiss) was analyzed and predicted using quantitative structure-activity relationships (QSAR). The aquatic toxicity, 96h $LC_{50}$ (median lethal concentration) of 275 organic pesticides, was obtained from EU-funded project DEMETRA. Prediction models were derived from 558 2D molecular descriptors, calculated in PreADMET. The linear (multiple linear regression) and nonlinear (support vector machine and artificial neural network) learning methods were optimized by taking into account the statistical parameters between the experimental and predicted p$LC_{50}$. After preprocessing, population based forward selection were used to select the best subsets of descriptors in the learning methods including 5-fold cross-validation procedure. The support vector machine model was used as the best model ($R^2_{CV}$=0.677, RMSECV=0.887, MSECV=0.674) and also correctly classified 87% for the training set according to EU regulation criteria. The MLR model could describe the structural characteristics of toxic chemicals and interaction with lipid membrane of fish. All the developed models were validated by 5 fold cross-validation and Y-scrambling test.