• The Korean Journal of Applied Statistics
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• v.20 no.3
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• pp.423-440
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• 2007

Although various models have been developed to establish the enterprise credit scoring, no model has utilized the enterprise human resource so far. The purpose of this study was to build an enterprise credit scoring model using enterprise human resource factors. The data to measure the enterprise credit score were made by the first-year research material of HCCP was used to investigate the enterprise human resource and 2004 Credit Rating Score generated from KIS-Credit Scoring Model. The independent variables were chosen among questionnaires of HCCP based on Mclagan(1989)'s HR wheel model, and the credit score of Korean Information Service was used for the dependent variables. The statistical method used for data analysis was logistic regression. As a result of constructing a model, 22 variables were selected. To see these specifically by each large area, 6 variables in human resource development(HRD) area, 15 in human resource management(HRM) area, and 1 in the other area were chosen. As a consequence of 10 fold cross validation, misclassification rate and G-mean were 30.81 and 68.27 respectively. Decile having the highest response rate was bigger than the one having the lowest response rate by 6.08 times, and had a tendency to decrease. Therefore, the result of study showed that the proposed model was appropriate to measure enterprise credit score using enterprise human resource variables.

• The Korean Journal of Applied Statistics
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• v.30 no.1
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• pp.41-55
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• 2017

This study predicts the average scores of top 150 PGA golf players on 132 PGA Tour tournaments (2013-2015) using data mining techniques and statistical analysis. This study also aims to predict the Top 10 and Top 25 best players in 4 different playoffs. Linear and nonlinear regression methods were used to predict average scores. Stepwise regression, all best subset, LASSO, ridge regression and principal component regression were used for the linear regression method. Tree, bagging, gradient boosting, neural network, random forests and KNN were used for nonlinear regression method. We found that the average score increases as fairway firmness or green height or average maximum wind speed increases. We also found that the average score decreases as the number of one-putts or scrambling variable or longest driving distance increases. All 11 different models have low prediction error when predicting the average scores of PGA Tournaments in 2015 which is not included in the training set. However, the performances of Bagging and Random Forest models are the best among all models and these two models have the highest prediction accuracy when predicting the Top 10 and Top 25 best players in 4 different playoffs.

• Korean Journal of Microbiology
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• v.42 no.2
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• pp.83-88
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• 2006

This research was performed to determine the production of lovastatin and its HMG-CoA reductase activity produced by fruit bodies and mycelial liquid cultures of domestic edible mushrooms (8 fungal strains). By deter-mining TLC analysis for the confirmation of the presence of lovastatin, all the extracts from fruit bodies and mycelial liquid culture showed same Rf value (0.46), whick was identical to that of the standard lovastatin. In order to extract lovastatin from fruit body, the mixture of water/acetonitrile/methanol was chosen as the most effective solvent. Extracts from fruit body and mycelial liquid culture of pleurotus ostreatus produced the high-est lovastatin 0.98 mg/g based on dry biomass, and 21.90 mg/L, respectively. In the inhibition rate of 3-hydroxy-3-methylglutaryl-CoA (HMG-CoA) reductase, the highest was obtained in P. ostreatus as 67.8% among fruit bodies, and the rates of mycelial liquid culture extracts from P. ostreatus and Laetiporus sulphureus were 37.2% and 29.1%, respectively. Unusually L. sulphureus showed high inhibition rate with low content of lovastatin due to the contribution of campesterol and gamma-sitosterol with hypocholesterolemic activity as metabolites.

• The Mathematical Education
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• v.59 no.1
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• pp.63-80
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• 2020

This study analyzed 3,114 articles published in KCI journals and 1,636 articles published in SSCI journals from 2000 to 2019 in order to compare domestic and international research trends of mathematics education using a topic modeling method. Results indicated that there were 16 similar research topics in domestic and international mathematics education journals: algebra/algebraic thinking, fraction, function/representation, statistics, geometry, problem-solving, model/modeling, proof, achievement effect/difference, affective factor, preservice teacher, teaching practice, textbook/curriculum, task analysis, assessment, and theory. Also, there were 7 distinct research topics in domestic and international mathematics education journals. Topics such as affective/cognitive domain and research trends, mathematics concept, class activity, number/operation, creativity/STEAM, proportional reasoning, and college/technology were identified from the domestic journals, whereas discourse/interaction, professional development, identity/equity, child thinking, semiotics/embodied cognition, intervention effect, and design/technology were the topics identified from the international journals. The topic related to preservice teacher was the most frequently addressed topic in both domestic and international research. The topic related to in-service teachers' professional development was the second most popular topic in international research, whereas it was not identified in domestic research. Domestic research in mathematics education tended to pay attention to the topics concerned with the mathematical competency, but it focused more on problem-solving and creativity/STEAM than other mathematical competencies. Rather, international research highlighted the topic related to equity and social justice.

• Resources Recycling
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• v.29 no.1
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• pp.17-24
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• 2020

In this study, rare earth elements (REE) leaching from a refractory REE ore containing goethite as a major gangue mineral was conducted, introducing a two-stage method of chemical decomposition-acid leaching. At the chemical decomposition step, using one of alkaline agent, NaOH, the ore was decomposed, changing NaOH concentration from 20 to 50 wt% at 10% (w/w) of pulp density and the maximum temperature achieved without boiling at each NaOH concentration. With increasing NaOH concentration, light REE (Ce, La and Nd) and iron were concentrated in the solid phase which is the decomposed product, while aluminum (Al) and phosphorus (P) were removed to the liquid phase, and their concentrations in the solid phase were down to 0.96 and 0.17%, respectively. In addition, through XRD analysis, it was found that the crystallinity of goethite was considerably decreased. At the acid leaching step, the product decomposed by 50 wt% NaOH was leached at 3.0 M HCl and 80 ℃ for 3 hr, then the REE leaching efficiency was above 94% (Ce 80%), and the leaching efficiencies of Al and P were decreased to 12 and 0%, respectively. Therefore, in terms of both REE leaching efficiency and impurity removal, those decomposition and leaching conditions were chosen as optimum processing methods of the investigated material. In terms of REE leaching mechanism, because REE and iron leaching efficiencies showed the positive correlation each other, so it can be concluded that decreasing crystallinity of goethite affect the improvement of REE leaching.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.416-416
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• 2019

육상 담수는 대기중 이산화탄소($CO_2$) 배출의 중요한 발생원으로 주목되고 있다. 하천 및 강에서 대기중으로 배출되는 $CO_2$는 전 세계 탄소순환의 핵심요소이며, 대부분의 하천과 강은 $CO_2$로 과포화 되어있다. 세계적으로 하천 및 강의 $CO_2$ 배출량은 호수 및 저수지의 배출량보다 약 5배 많은 것으로 보고되고 있으나, 국내연구에서는 연구사례가 드물다. 따라서 본 연구의 목적은 낙동강 중하류에 위치해있는 강정고령보(GGW), 달성보(DSW), 합천창녕보(HCW), 창녕함안보(CHW)에서 발생되는 순 대기 배출 플럭스(Net Atmospheric Flux, NAF)의 동적 변동 특성을 분석하고, 데이터마이닝 기법을 적용하여 쉽게 수집할 수 있는 물리적 및 수질 변수로 $CO_2$ NAF를 추정하는데 사용할 수 있는 간략한 예측 모델을 개발하는데 있다. $CO_2$ NAF는 대기-수면 경계면에서의 $CO_2$ 부분압($pCO_2$)의 차에 기체전달속도를 곱하여 산정하였으며, 기체전달속도는 Cole and Caraco(1998)가 제안한 식을 사용하였다. 담수와 해수의 탄산염 시스템에서 열역학적 화학평형을 모두 고려한 $CO_2$SYS 프로그램을 사용하여 수중의 $pCO_2$를 산정하였고, $CO_2$ NAF는 Henry의 법칙과 Fick의 1차 확산법칙을 사용하여 계산하였다. $CO_2$ NAF의 시간적 변동성에 영향을 미치는 환경요인을 평가하기 위해서 상관분석, 주성분분석(Principal Component Analysis; PCA), 단계적다중회귀모델(Step-wise Multiple Linear Regression; SMLR), 랜덤포레스트(Random Forest; RF)방법을 사용하였다. SMLR 모델은 R package인 olsrr, RF 모델은 R package인 caret, randomForest를 이용하여 분석하였다. 연구 결과, 4개 보 상류 하천구간은 조류의 성장이 활발한 일부 기간을 제외한 대부분의 기간에서 $CO_2$를 대기로 배출하는 종속영양시스템(Heterotrophic system)을 보였다. $CO_2$ NAF의 중위값은 HCW에서 최소 $391.5mg-CO_2/m^2day$, DSW에서 최대 $1472.7mg-CO_2/m^2day$였다. 모든 보에서 NAF는 pH와 강한 음의 상관관계를 보였으며, $pCO_2$와 Chl-a도 음의 상관관계를 보였다. 이는 조류가 수중에서 $CO_2$를 소비하고 pH를 증가시키기 때문이다. PCA 분석 결과, NAF와 $pCO_2$가 높은 공분산을 보였으며, pH와 Chl-a는 반대 방향으로 군집되어 상관분석과 동일한 결과를 보였다. 이 연구를 통해 개발된 SMLR 모델과 RF 모델의 Adj. $R^2$ 값은 모든 보에서 0.77 이상으로 나왔으며, $pCO_2$ 측정 데이터가 없더라도 하천의 $CO_2$ NAF를 추정하는 방법으로 사용될 수 있을 것으로 평가된다.

• Journal of Digital Convergence
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• v.17 no.10
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• pp.49-58
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• 2019

In the era of the 4th industrial revolution, the development of information technology brought various benefits, but it also increased social interest in privacy issues. As the possibility of personal privacy violation by big data increases, academic discussion about privacy management has begun to be active. While the traditional view of privacy has been defined at various levels as the basic human rights, most of the recent research trends are mainly concerned only with the information privacy of online privacy protection. This limited discussion can distort the theoretical concept and the actual perception, making the academic and social consensus of the concept of privacy more difficult. In this study, we analyze the privacy concept that is exposed on the internet based on 12,000 news data of the portal site for the past one year and compare the difference between the theoretical concept and the socially accepted concept. This empirical approach is expected to provide an understanding of the changing concept of privacy and a research direction for the conceptualization of privacy for current situations.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.29 no.3
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• pp.289-297
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• 2019

Objectives: To identify the positive rate of and the risk factors associated with latent tuberculosis infection(LTBI) in mine workers, the objectives of the present study evaluated those among former mine workers. Methods: Between January 2015 and May 2017, former male mine workers who had been subjects for epidemiology research for work-related chronic obstructive pulmonary disease(COPD) and had received QuantiFERON-$TB^{(R)}$ Gold In-Tube(QFT-GIT) from the Institute of Occupation and Environment(IOE) under Korea Workers' Compensation and Welfare Service(KCOMWEL) were selected as the study subjects. To identify significant variables for increased risk of LTBI, logistic regression analysis was performed. Results: A total of 736 male former mine workers were selected as study subjects. The positive rate of LTBI among subjects was 69.2%(509/736). The current smoking[odds ratio(OR), 2.3; 95% confidence interval(CI), 1.1-4.9], COPD(OR, 1.4; 95% CI, 0.9-2.3), department loading(OR, 1.8; 95% CI, 0.9-3.4) and mining(OR, 1.5; 95% CI, 0.9-2.5), and working duration of over 20(OR, 1.6; 95% CI, 0.9-3.1) and over 30 years(OR, 2.2; 95% CI, 0.9-4.9) were associated with increased risk of LTBI. The interferon-gamma(IFN-${\gamma}$) level after stimulation with Mycobacterium tuberculosis(MTB)-specific antigens showed a significantly negative correlation with age(r=-0.126). Conclusions: The present study determined that the high positive rate of LTBI among mine workers was associated with not only the host factors but also the occupational exposure to mine dust.

• Journal of the Korea Society of Computer and Information
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• v.17 no.2
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• pp.215-224
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• 2012

This study performs an exploratory investigation of the needs of the customers who use academic information service from a research institute, K, that provides information services for domestic academic institutions of natural science and technology. K institute is planning customized services in order to improve customer satisfaction on the academic information service And therefore, the institute begins the research on customer needs analysis and customer segmentation. The research is regarded as well-timed, because CRM implementation in public organizations has been activated recently. Data mining and data warehousing techniques were used for pilot analyses. For the purpose of customer segmentation, a mixed segmentation model, which adds product life cycle concept to the 'balanced customer segmentation' model, which in turn considers the value of customers from the organizational viewpoint and the value of organizations from the customer's viewpoint, simultaneously, was applied. The result of investigation indicated that, in the case of K, 'balanced customer segmentation' and 'contents reach approach' which uses data warehouse/OLAP, rather than those customer segmentation techniques that are often used within the industry, are the more potent ways of approach. This exploratory case study is expected to provide a useful guideline for 'deriving an organizationally unique CRM model' that recently is one of the hot topics in the CRM area.

• Journal of Digital Convergence
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• v.19 no.12
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• pp.11-20
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• 2021

This study extracts topics related to university education through newspaper articles and analyzes the characteristics of each topic and the reporting patterns of each newspaper. The 9 topics were discovered using LDA. Topic 1 and Topic 3 are related to university support projects for education, but Topic 3 is focused on local universities. Topic 2 is about university education after COVID-19, Topic 4 teaching-learning methods, Topic 5 government policies, Topic 6 the high school education contribution university support projects, Topic 7 the university education vision, Topic 8 internationalization, and Topic 9 the entrance exam. The Chosun Ilbo, Kyunghyang, and Hankyoreh reported a lot of articles associated to lectures after COVID-19, government policies, and comments on university education. Relevant articles since 2016 have been analyzed by newspaper type and before/after COVID-19 through which differences in the topics were studied and discussed. These findings would suggest a basic policy guideline for university education and imply that the positive and negative effects of the media need to be considered.