• Korean Journal of Social Welfare
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• v.68 no.2
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• pp.5-25
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• 2016

Despite previous literatures suggesting the co-occurrence of substance abuse and behavioral addiction, their relationship has not been systematically explored. Especially, college students are a high risk group for alcohol use and gambling activities and they have various psychosocial problems due to addictive behaviors. This study aimed to empirically examine that drinking problems predict gambling problems among college students. A total of 455 college students who experienced drinking and gambling completed a survey. Logistic regression analysis were performed. After adjusting for demographics and family related variables, drinking problems predicted the occurrence of problem gambling. Implications for social work practice, policy planning and research area on addiction are discussed.

• Annual Conference of KIPS
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• 2012.11a
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• pp.1373-1376
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• 2012

복무적합도 검사는 정신질환이나 사고가능성이 있는 병사를 감별하고, 입대 후 적응문제로 조기 전역할 수 있는 집단을 예측하는 신인성검사 중 하나로, 현재 군에서 징병 및 입영단계에 실시하는 인성검사이다. 이는 전체 검사대상자를 상대로 정신과적 문제 식별을 위한 개별면담이 불가능하기 때문에 위 검사를 통해 대상자를 효율적으로 선별하기 위함이다. 본 연구는 데이터 마이닝을 통해 복무적합도 검사의 판정을 예측 할 수 있을지 확인하고자 하였다. 이를 위해 데이터 마이닝의 기법 중 회귀분석의 로지스틱 회귀분석 기법이 복무적합도검사 판정에 우수한 성능을 보임을 확인하였고, 로지스틱 회귀분석의 추정된 회귀계수를 이용하여 만든 반응확률에 대한 예측 모형식은 높은 정분류율을 보였고 평가 결과 통계적으로 의미가 있음을 증명하였다. 따라서 본 연구 결과를 활용하면 소수의 문항으로 복무적합도 검사 이전의 선별용 검사 개발이나 자가 진단용 검사 개발로 활용이 가능 할 것으로 기대한다.

• The Korean Journal of Financial Management
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• v.25 no.2
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• pp.1-31
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• 2008

Before the financial crisis in 1997, Korean bond markets had been those of corporate bonds with relatively high market yield. During the period, most of major institutional investors tend to utilize passive strategies such as buying and holding. After the crisis, however, they could not help choosing active bond management strategies because of lowed yield level and intensified competition among the financial institutions. This study is forced on the yield curve, which is the reflection of all information on the bond investment environments. The study also make analysis on the major economic and securities market factors and its structural relationship with the shape of the curve such as level, curvature and slope. For these purposes, an empirical model based on the Nelson-Siegel Model is estimated with the data during $1999{\sim}2006$. Out-of-sample forecasting is also made to test the usefulness of the estimated model. In addition, the dependent variables which are the estimates of level and slope are estimated on the macro variables and securities market variables. VAR and SUR models are used for the estimation. Estimation results show that level and slope of the yield curve are influenced by the target call rate change, exchange rate change rate, inflation rate. These results provide practical implications for the active managements in the overall treasury bond markets.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.14 no.6
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• pp.60-68
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• 2015

Recent proliferation of advanced technologies such as wireless communication, mobile, sensor technology and so on has caused significant changes in a traffic environment. Human beings, in particular drivers, as well as roads and vehicles were advanced on information, intelligence and automation thanks to those advanced technologies; Intelligent Transport Systems (ITS) and autonomous vehicles are the results of changes in a traffic environment. This study proposed considerations when designing a simulation model for future transportation environments, which are difficult to predict the change by means of advanced technologies. First of all, approximability, flexibility and scalability were defined as a macroscopic concept for a simulation model design. For actual similarity, calibration is one of the most important steps in simulation, and Physical layer and MAC layer should be considered for the implementation of the communication characteristics. Interface, such as API, for inserting the additional models of future traffic environments should be considered. A flexible design based on compatibility is more important rather than a massive structure with inherent many functions. Distributed computing with optimized H/W and S/W together is required for experimental scale. The results of this study are expected to be used to the design of future traffic simulation.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.14 no.3
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• pp.379-390
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• 1994

The recent study of the delay models have assumed random arrival which has a constant average flow rate throughout the cycle. However, where signals are spaced closely together or form part of progressive system, platoon flows are common and more closely represent reality. In such cases, those results are quite different pattern of estimated delay from that of observed one. In order to solve this problem, the 1985 HCM takes Progression Adjustment Factor (PAF) into account. In the 1985 HCM, however, it has deficiencies in defining and applying it, such as platoon ratio ($R_p$) and platoon arrival type. The Purpose of this study is to investigate theoretically the predictive ability of the individual models concerned through comparing the estimated delay and PAF suggested by NCHRP Report 339, KHCM or USHCM (1985) with the observed obtained by field survey at a signalized intersection.

• Survey Research
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• v.4 no.2
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• pp.1-21
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• 2003

We proposed imputation strategies for marital status in the Population and Housing Census 2000 in Korea to illustrate the effective missing value imputation methods for social survey. The marital status which have relatively high non-response rates in the Census are considered to develope the effective missing value imputation procedures. The Classification and Regression Tree(CART)is employed to construct the imputation cells for hot-deck imputation, as well as to predict the missing value by model-based approach. We compare to imputation methods which include the CART model-based imputation and the sequential hot-deck imputation based on CART. Also we check whether different modeling for each region provides the more improved results. The results suggest that the proposed hot-deck imputation based on CART is very efficient and strongly recommendable. And the results show that different modeling for each region is not necessary.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.452-452
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• 2018

최근에는 지구온난화의 영향으로 집중 호우, 가뭄 등 기상 이변이 빈번하게 발생하고 있어서 물 관리의 복잡성과 불확실성이 매우 높아지고 있다. 특히 중소하천은 하천의 경사가 급하고 하천길이가 짧아 홍수파의 이동시간이 짧기 때문에 홍수대비를 위한 시간이 매우 짧다. 국회예산정책처(2012)에 따르면 과거 5개년간(2007~11) 발생한 자연재해 피해 중 87%가 집중 호우 및 태풍에 의한 피해이며, 이 중 98.7%가 중 소규모 하천에서 발생하였다. 그러나, 중소하천 관리를 담당하는 지자체는 열악한 재정 여건으로 인해 예방보다 사후복구에 집중할 수밖에 없는 상황이다. 또한 최근 하천 고수부지에 주차장, 위락시설 등 공간적 활용이 증가함에 따라 홍수발생시 위험성도 증가하고 있어 예방 및 대응 중심의 홍수대응기준 수립이 시급하다. ICT 인프라를 이용하여 유관기관의 실시간 수문자료를 공유하고, 수집된 실시간 수문자료를 활용한 홍수분석 및 홍수대응 기준 대응기준 수립을 통한 예방적 재난대응 체계를 마련해야 한다. 이에 본 연구에서는 A지자체를 대상으로 홍수분석 모형을 구축하고, 구축된 모형의 계산결과를 활용하여 예상강우별 도달시간, 수위상승 등을 예측하고, 하천수위별 홍수대응기준을 수립하였다. 수위별 대응기준은 현장에서 계측되는 수위값을 기준으로 홍수예보기준, 하천기본계획의 제방고 및 주변 제약 사항을 고려하여 직접알람 기준으로 활용하였다. 특히 A지자체는 상류에 B댐이 위치하여 홍수시 B댐 수위 및 운영의 영향을 반영하는 홍수대응기준의 수립이 요구된다. 본 연구에서는 강우 시나리오별 모형분석을 통하여 예상되는 호우사항에 대한 홍수대응기준을 제시하였다. 향후 본 연구로 산정된 홍수대응기준의 지속적인 보완, 실시간 수문자료 공유체계 확대, 재해관련 책임기관과의 유기적인 기술교류 등을 통해 지자체 및 중소하천 홍수피해 저감을 위한 능동적인 홍수대응 체계 구축이 수행되어야 할 것이다.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.157-176
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• 2018

The purpose of this research is to develop a detection model for companies designated as administrative issue in KOSDAQ market using financial data. Administration issue designates the companies with high potential for delisting, which gives them time to overcome the reasons for the delisting under certain restrictions of the Korean stock market. It acts as an alarm to inform investors and market participants of which companies are likely to be delisted and warns them to make safe investments. Despite this importance, there are relatively few studies on administration issues prediction model in comparison with the lots of studies on bankruptcy prediction model. Therefore, this study develops and verifies the detection model of the companies designated as administrative issue using financial data of KOSDAQ companies. In this study, logistic regression and decision tree are proposed as the data mining models for detecting administrative issues. According to the results of the analysis, the logistic regression model predicted the companies designated as administrative issue using three variables - ROE(Earnings before tax), Cash flows/Shareholder's equity, and Asset turnover ratio, and its overall accuracy was 86% for the validation dataset. The decision tree (Classification and Regression Trees, CART) model applied the classification rules using Cash flows/Total assets and ROA(Net income), and the overall accuracy reached 87%. Implications of the financial indictors selected in our logistic regression and decision tree models are as follows. First, ROE(Earnings before tax) in the logistic detection model shows the profit and loss of the business segment that will continue without including the revenue and expenses of the discontinued business. Therefore, the weakening of the variable means that the competitiveness of the core business is weakened. If a large part of the profits is generated from one-off profit, it is very likely that the deterioration of business management is further intensified. As the ROE of a KOSDAQ company decreases significantly, it is highly likely that the company can be delisted. Second, cash flows to shareholder's equity represents that the firm's ability to generate cash flow under the condition that the financial condition of the subsidiary company is excluded. In other words, the weakening of the management capacity of the parent company, excluding the subsidiary's competence, can be a main reason for the increase of the possibility of administrative issue designation. Third, low asset turnover ratio means that current assets and non-current assets are ineffectively used by corporation, or that asset investment by corporation is excessive. If the asset turnover ratio of a KOSDAQ-listed company decreases, it is necessary to examine in detail corporate activities from various perspectives such as weakening sales or increasing or decreasing inventories of company. Cash flow / total assets, a variable selected by the decision tree detection model, is a key indicator of the company's cash condition and its ability to generate cash from operating activities. Cash flow indicates whether a firm can perform its main activities(maintaining its operating ability, repaying debts, paying dividends and making new investments) without relying on external financial resources. Therefore, if the index of the variable is negative(-), it indicates the possibility that a company has serious problems in business activities. If the cash flow from operating activities of a specific company is smaller than the net profit, it means that the net profit has not been cashed, indicating that there is a serious problem in managing the trade receivables and inventory assets of the company. Therefore, it can be understood that as the cash flows / total assets decrease, the probability of administrative issue designation and the probability of delisting are increased. In summary, the logistic regression-based detection model in this study was found to be affected by the company's financial activities including ROE(Earnings before tax). However, decision tree-based detection model predicts the designation based on the cash flows of the company.

• Korean Journal of Construction Engineering and Management
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• v.19 no.3
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• pp.33-42
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• 2018

In the aftermath of an earthquake, seismic-damaged infrastructure systems loss estimation is the first step for the disaster response. However, lifeline systems' ability to supply service can be volatile by external factors such as disturbances of nearby facilities, and not by own physical issue. Thus, this research develops the bayesian model for probabilistic inference on common-cause and cascading failure of seismic-damaged lifeline systems. In addition, the authors present network robustness estimation metrics in the context of failure propagation. In order to quantify the functional loss and observe the effect of the mitigation plan, power and water supply system in Daegu-Gyeongbuk in South Korea is selected as case network. The simulation results show that reduction of cascading failure probability allows withstanding the external disruptions from a perspective of the robustness improvement. This research enhances the comprehensive understanding of how a single failure propagates to whole lifeline system performance and affected region after an earthquake.

• The Journal of the Korea Contents Association
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• v.20 no.1
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• pp.519-529
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• 2020

The purpose of this study is to establish a model for predicting academic achievement of college students and to reveal the interrelationship and relative influence of each factor. For this, we surveyed the personal factors and learning strategy factors of 1,310 learners at J University, and analyzed the discriminant factors and patterns of the predictors of academic achievement through the decision tree analysis, a data mining method, and examined the relative effects of each factor. Binary logistic regression analysis was performed for viewing. As a result, the most important factor for predicting academic achievement was efficacy, and other factors such as motivation, time management, and depression were predictive of academic achievement. The patterns of factors predicting academic achievement were found to be high in efficacy and time management, and high in motivation for learning even if the efficacy was moderate. Low efficacy and learning motivation, and high depression have been shown to decrease academic achievement. Based on these results, the study suggested the efficacy and motivation to improve academic achievement of college students, strengthening time management education, and managing negative emotions.