• The Journal of Society for e-Business Studies
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• v.23 no.1
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• pp.47-63
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• 2018

This study suggested quantitative models to calculate a royalty rate as an important input factor of the relief from royalty method which has the characteristics of income approach method and market approach method that are generally used in the valuation of intangible assets. This study built a royalty rate regression model by referring to the patent infringement damages cases based on royalties, i.e., by using the royalty rates as a dependent variable and the patent indexes of the corresponding patent right as independent variables. Then, a logistic regression model was constructed by referring to inter-partes review cases of patent rights, i.e. by using not-unpatentable results as a dependent variable and the patent indexes of the corresponding patent right as independent variables. A final royalty rate was calculated by matching the royalty rate from the royalty rate regression model with a not-unpatentable probability from the logistic regression model. The suggested royalty rate was compared with the royalty rate obtained by the traditional methods to check its reliability.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.5
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• pp.2096-2109
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• 2012

This study investigates empirical issues that have received little attention in the previous research in the Korean capital market. It is to find any financial determinants on the capital structure for the firms listed in the KOSDAQ(Korea Securities Dealers Automated Quotation). Another test is performed to find any possible discriminating factors by utilizing a robust methodology, which may distinguish between the firms belonging the 'prime section' and the 'venture section' in terms of their financial aspects. Moreover, the null hypothesis that the changing trend or movement of a firm's capital structure with respect to its industry mean (or median) may be random, is also tested. For the book-value based debt ratios, size(INSIZE), growth(GROWTH), Market to book value of equity(MVBV), volatility(VOLATILITY), market value of equity (MVE) and section dummy (SECTION) showed their statistically significant effects on the book-value based leverage ratios, respectively, while size(INSIZE), growth(GROWTH), market value of equity(MVE), beta(BETA) and section dummy (SECTION) showed their statistically significant effects on the market-value based leverage ratios. This study also found an interesting result that a firm belonging to each corresponding industry has a tendency for reversion toward its mean and median leverage ratios over the five-year tested period.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.3
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• pp.133-140
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• 2022

We investigate the performance of deep learning-based Korean language models on a task of predicting the score range of Korean essays written by foreign students. We construct a data set containing a total of 304 essays, which include essays discussing the criteria for choosing a job ('job'), conditions of a happy life ('happ'), relationship between money and happiness ('econ'), and definition of success ('succ'). These essays were labeled according to four letter grades (A, B, C, and D), and a total of eleven essay score range prediction experiments were conducted (i.e., five for predicting the score range of 'job' essays, five for predicting the score range of 'happiness' essays, and one for predicting the score range of mixed topic essays). Three deep learning-based Korean language models, KoBERT, KcBERT, and KR-BERT, were fine-tuned using various training data. Moreover, two traditional probabilistic machine learning classifiers, naive Bayes and logistic regression, were also evaluated. Experiment results show that deep learning-based Korean language models performed better than the two traditional classifiers, with KR-BERT performing the best with 55.83% overall average prediction accuracy. A close second was KcBERT (55.77%) followed by KoBERT (54.91%). The performances of naive Bayes and logistic regression classifiers were 52.52% and 50.28% respectively. Due to the scarcity of training data and the imbalance in class distribution, the overall prediction performance was not high for all classifiers. Moreover, the classifiers' vocabulary did not explicitly capture the error features that were helpful in correctly grading the Korean essay. By overcoming these two limitations, we expect the score range prediction performance to improve.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.3D
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• pp.381-389
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• 2009

Korean government is investing 1.8 billion won on infrastructure and investment on PPP projects constitutes 16.3%. This phenomenon is to promote private investment as well as lessening government burdens of public budgeting. However, the only criterion to be considered is government financial status in selecting public or private highway investment projects. So systematic decision support model is required in choosing public or private highway investment projects. So, this paper suggests a systematic decision support model for deciding public or private highway investment at the early stage of project planning. Furthermore, this paper identifies key decision variables with respect to economic, politic, project management criterions based on the related literatures and feedbacks from experts. This paper analyzed 30 cases of government investment and PPP projects and got the survey result from highway specialists. As a result, this paper presents an interval with respect to economic criteria using mean and standard deviation and a logistic regression equation which can predict the possibility of PPP project. Through this study, decision maker of central or local government can decide public or PPP highway project more systematically and reasonably.

• Journal of the Korea Society for Simulation
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• v.32 no.4
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• pp.19-26
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• 2023

The increase in the dropout rate of college students nationwide has a serious negative impact on universities and society as well as individual students. In order to proactive identify students at risk of dropout, this study built a decision tree, random forest, logistic regression, and deep learning-based dropout prediction model using academic data that can be easily obtained from each university's academic management system. Their performances were subsequently analyzed and compared. The analysis revealed that while the logistic regression-based prediction model exhibited the highest recall rate, its f-1 value and ROC-AUC (Receiver Operating Characteristic - Area Under the Curve) value were comparatively lower. On the other hand, the random forest-based prediction model demonstrated superior performance across all other metrics except recall value. In addition, in order to assess model performance over distinct prediction periods, we divided these periods into short-term (within one semester), medium-term (within two semesters), and long-term (within three semesters). The results underscored that the long-term prediction yielded the highest predictive efficacy. Through this study, each university is expected to be able to identify students who are expected to be dropped out early, reduce the dropout rate through intensive management, and further contribute to the stabilization of university finances.

• Journal of The Geomorphological Association of Korea
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• v.26 no.2
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• pp.1-14
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• 2019

This study analyzed the relativity between block stream and talus distributions by employing a likelihood ratio approach. Possible distribution sites for each debris slope landform were extracted by applying a spatial integration model, in which we combined fuzzy set model, Bayesian predictive model, and logistic regression model. Moreover, to verify model performance, a success rate curve was prepared by cross-validation. The results showed that elevation, slope, curvature, topographic wetness index, geology, soil drainage, and soil depth were closely related to the debris slope landform sites. In addition, all spatial integration models displayed an accuracy of over 90%. The accuracy of the distribution potential area map of the block stream was highest in the logistic regression model (93.79%). Eventually, the accuracy of the distribution potential area map of the talus was also highest in the logistic regression model (97.02%). We expect that the present results will provide essential data and propose methodologies to improve the performance of efficient and systematic micro-landform studies. Moreover, our research will potentially help to enhance field research and topographic resource management.

• Journal of Digital Convergence
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• v.15 no.11
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• pp.245-250
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• 2017

Flash floods is defined as the flooding of intense rainfall over a relatively small area that flows through river and valley rapidly in short time with no advance warning. So that it can cause damage property and casuality. This study is to establish the flash-flood warning system using 38 accident data, reported from the National Disaster Information Center and Land Surface Model(TOPLATS) between 2009 and 2012. Three variables were used in the Land Surface Model: precipitation, soil moisture, and surface runoff. The three variables of 6 hours preceding flash flood were reduced to 3 factors through factor analysis. Decision tree, random forest, Naive Bayes, Support Vector Machine, and logistic regression model are considered as big data methods. The prediction performance was evaluated by comparison of Accuracy, Kappa, TP Rate, FP Rate and F-Measure. The best method was suggested based on reproducibility evaluation at the each points of flash flood occurrence and predicted count versus actual count using 4 years data.

• Journal of the Korean Geotechnical Society
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• v.31 no.6
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• pp.59-69
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• 2015

Recently, landslide and debris-flow disasters caused by severe rain storms have frequently occurred. Many researches related to landslide susceptibility analysis and debris-flow hazard analysis have been conducted, but there are not many researches related to mobilization analysis for landslides transforming into debris-flow in slope areas. In this study, statistical analyses such as discriminant analysis and logistic regression analysis were conducted to develop a mobilization criterion using geomorphological and geological factors. Ten parameters of geomorphological and geological factors were used as independent variables, and 466 cases (228 non-mobilization cases and 238 mobilization cases) were investigated for the statistical analyses. First of all, Fisher's discriminant function was used for the mobilization criterion. It showed 91.6 percent in the accuracy of actual mobilization cases, but homogeneity condition of variance and covariance between non-mobilization and mobilization groups was not satisfied, and independent variables did not follow normal distribution, either. Second, binomial logistic analysis was conducted for the mobilization criterion. The result showed 92.3 percent in the accuracy of actual mobilization cases, and all assumptions for the logistic analysis were satisfied. Therefore, it can be concluded that the mobilization criterion for debris-flow using binomial logistic regression analysis can be effectively applied for the prediction of debris-flow hazard analysis.

• The Journal of Korean Society for School & Community Health Education
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• v.16 no.1
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• pp.51-63
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• 2015

목적: 사하라 이남 아프리카 지역 중저소득국가 모성보건지표의 더딘 개선율은 MDG 5(모자보건향상) 미달성의 주요 원인 중 하나로 꼽힌다. 따라서 본 연구는 우간다 동부 카프초르와 구의 모성사망과 밀접한 산후건강관리(PNC, Postnatal care)서비스 이용결정요인을 파악하는데 있다. 이를 통해 지역건강관리자들에게 PNC 서비스 이용 개선을 위한 정책 수립 및 방안 마련에 기초자료를 제공하고, 궁극적으로는 MDG 5 지표 개선에 일조하고자 한다. 방법: 표본 집단은 카프초르와 구의 15세~49세 여성들 중 최근 1년 내에 출산을 경험한 자들을 대상으로 편의추출 되었다. 조사기간은 2014년 7월부터 10월까지였으며, 구조화된 설문에 총 171명이 응답하였고, 19명의 주요 정보제공자와의 심층면담도 실시하였다. 응답자의 사회인구학적 특성 및 PNC 이용행태를 알아보기 위해 빈도분석을 실시하였으며, 각 독립변수가 PNC 이용에 어떤 영향을 미치는지 파악하기 위해 로지스틱 회귀분석을 실시하였다. 결과: 응답자의 55%만이 의료시설의 PNC 서비스를 받은 것으로 나타났다. 로지스틱 회귀분석을 통해서는 응답자의 연령과 사회적 네트워크, 인지된 건강상태, 산전관리서비스 이용이 PNC 서비스 이용에 긍정적인 영향을 미치는 것으로 나타났으며 의료시설과의 거리, 가족의 규모는 부정적인 영향을 미치는 것으로 나타났다. 결론: PNC 서비스 이용개선을 위해서는 먼저 여성의 사회적 자본 확충 및 개선을 위한 모성보건교육인 소프트 인프라 지원이 지자체 차원에서 실시되어야 할 것이며, 서비스 이용을 가능케 하고 접근성을 높이는 응급후송체계 구축과 같은 물리적 인프라 지원도 도입되어야할 것이다. 또한 가족계획 서비스를 제공하는 등 모성보건관리에 대한 지자체의 민감성을 높이는 것도 필요하겠다.

• Land and Housing Review
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• v.13 no.3
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• pp.57-68
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• 2022

This study investigates what influences the 2030 households' intention to utilize housing support policies for the younger generation. Using the logistic regression model, our empirical results show that the 'the recognition of youth housing support projects', 'the housing occupation', 'employment type', 'housing type', and 'age' factors have a significant effect on the intention to use the housing support policies. Specifically, the intention is positively associated with economic activity, one-room residence, monthly rent, employment status during the Covid-19 period, and policy recognition, while negatively related to age. In addition, willingness to use the housing support policies is greater when respondents lived in a studio, lived on a monthly rent, recognized the policy, and improved their employment status. The results suggest that housing support programs need to be expanded and improved. Moreover, information on housing support policies should be efficiently delivered to eligible households, and more sophisticated housing support policies should be provided for young people early in their careers.