• 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.

• The Journal of the Korea Contents Association
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• v.12 no.2
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• pp.349-357
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• 2012

Although u-healthcare service is emerged as an alternative method for effective chronic disease management services, the service has not yet been applied for real healthcare setting. The objective of this study is to explore the doctors' perception and influential factors on intention to use u-healthcare service. We conducted survey for physicians about u-healthcare service provision to compare characteristics by different groups. In addition, logistic regression analysis is conducted to find out factors affecting the usage intention. As a result, doctors responded only 16.0% of total respondents had experience of u-healthcare services, but also showed that as high as 70.1% had intention to use service. Also, respondents answered that u-healthcare services is appropriate to apply for chronic disease prevention and diabetes and hypertension are suggested as the most appropriate diseases in order. The intention to use the u-healthcare service by non-university hospital doctors was 3.7 times higher than university hospital doctor. This study shows that identifying the differences of doctors' awareness and also the intention to use about the u-healthcare services will contribute to develop more effective business model.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1089-1098
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• 2020

In this study, we propose a method to detect red tide Cochlodinium Polykrikoide using by machine learning and geostationary marine satellite images. To learn the machine learning model, GOCI Level 2 data were used, and the red tide location data of the National Fisheries Research and Development Institute was used. The machine learning model used logistic regression model, decision tree model, and random forest model. As a result of the performance evaluation, compared to the traditional GOCI image-based red tide detection algorithm without machine learning (Son et al., 2012) (75%), it was confirmed that the accuracy was improved by about 13~22%p (88~98%). In addition, as a result of comparing and analyzing the detection performance between machine learning models, the random forest model (98%) showed the highest detection accuracy.It is believed that this machine learning-based red tide detection algorithm can be used to detect red tide early in the future and track and monitor its movement and spread.

• Korean Journal of Psychosomatic Medicine
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• v.30 no.1
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• pp.46-53
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• 2022

Objectives : This study aimed to assess the possible association of dental pain with suicidal ideation among adolescents by analysing data from the 2018 Korean Youth Risk Behavior Survey, a nationwide online survey. Methods : Of 62,823 adolescent middle and high school students in Korea, 60,040 participants were selected for analysis, after excluding cases with missing values. Participants were given a questionnaire about their self-evaluation of health including dental pain and suicidal ideation. Logistic regression analysis demonstrated the relationships between dental pain and suicidal ideation after controlling for potential confounding factors. Results : The proportion of Korean adolescents reporting suicidal ideation was 13.3%. The proportion of adolescents who experienced dental pain was 23.4%. Compared to adolescents who did not report dental pain, adolescents who reported experiencing dental pain were significantly more likely to experience suicidal ideation (OR=1.94, p<0.001). In two multivariate models, the relationships between dental pain and suicidal ideation (AOR=1.24, p<0.001) were statistically significant. Conclusions : Dental pain was associated with increased risk of suicidal ideation among Korean adolescents, even when controlling for sociodemographic factors and other health conditions. It is necessary to consider screening adolescent patients who present with dental pain for suicidal ideation.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.4
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• pp.148-163
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• 2021

The diversification of the New Deal urban regeneration projects, that started in 2017 in accordance with the "Special Act on Urban Regeneration Activation and Support", generated the increased demand for the accuracy of data-driven diagnosis and project type forecast. Thus, this research was conducted to develop an application model able to identify the most appropriate New Deal project type for "eup", "myeon" and "dong" across the country. Data for application model development were collected through Statistical geographic information service(SGIS) and the 'Urban Regeneration Comprehensive Information Open System' of the Urban Regeneration Information System, and data for the analysis model was constructed through data pre-processing. Four models were derived and simulations were performed through polynomial regression analysis and multinomial logistic regression analysis for the application of the appropriate New Deal project type. I verified the applicability and validity of the four models by the comparative analysis of spatial distribution of the previously selected New Deal projects by targeting the sites located in Seoul by each model and the result showed that the DI-54 model had the highest concordance rate.

• Journal of Ginseng Culture
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• v.5
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• pp.97-114
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• 2023

Despite numerous studies, research on ginseng in aspect of an economic and business perspective are insufficient. Recently, research to reveal the economic cause of ginseng products purchase is drawing attention. The purpose of this study is to analyze empirically the factors of ginseng products purchase by international consumers from a microeconomic perspective. Using the survey data, we empirically investigate the determinants of ginseng products purchase by international consumers visiting Korea. We use a multinomial logistic model to find the determinants that influence the purchase of ginseng products. This study finds the followings. First, the economic factor is an important determinant of ginseng products purchase. The average daily expenditure has a greater impact on ginseng products purchase than household income does. Even though the average daily expenditure is high, they tend to buy less ginseng products when they prefer other products. Second, demographically, gender and age are also important determinants of ginseng products purchase. It has been found that elderly male consumers are more likely to buy ginseng products. Third, international consumers for leisure purposes have a higher probability of buying ginseng products than tourism consumers for other purposes do. Finally, destination attributes such as security (safety), ease of use of mobile/Internet, and ease of finding directions are also important factors affecting ginseng products purchase. In addition, it is found that the convenience of using the mobile/Internet, the ease of finding directions, and the convenience of shopping increase the probability of buying ginseng products by international consumers. This study is meaningful in that it explored the determinants of ginseng products purchase by analyzing individual consumers' ginseng products choices.

• 한국노년학
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• v.39 no.1
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• pp.37-60
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• 2019

This study was attempt to derive the aging trajectories of Korean elderly people and identify its characteristics. In particular, this study used the successful aging model of Rowe and Kahn as an analytical framework. Using the Korean Longitudinal Study of Ageing(KLoSA), this study applied group-based multi-trajectory analysis to identify multiple aging trajectories in sample of Korean elder aged 65~74(n=2,682). This study also used several demographic characteristics as baseline predictors to identify the characteristics of each aging trajectory. Five dimensions were analyzed in the multi-trajectory model: chronic disease, physical functional limitation, cognitive functioning, depressive symptom and social engagement. As a result of the analysis, five aging trajectories were identified: successful aging(17.8%), usual aging (33.9%), health declining aging(18.2%), pathological aging(7.9%), and aging with mild cognitive impairment(22.1%). In general, the odds of experiencing successful aging were high in men, low-aged, highly educated, high-income, and spousal elderly. On the other hand, for the elderly, who are under-educated, low-income, and high-aged, there was a high probability of experiencing a relatively difficult aging process. In particular, the odds of experiencing a mild cognitive impairment aging was high in older, lower-income women without a spouse.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2002.11a
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• pp.535-542
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• 2002

최근 IT 산업이 급성장하면서 화상 회의, 게임, 채팅 등에서의 아바타(avatar) 제어를 위한 자연스러운 인터페이스 기술이 요구되고 있다. 본 논문에서는 동적 윤곽선 모델(active contour models; snakes)을 이용하여 복잡한 배경이 있는 컬러 CCD 카메라 영상에서 얼굴과 눈, 입, 눈썹, 코 등의 얼굴 요소에 대해 윤곽선을 추출하거나 위치를 파악하는 방법을 제안한다. 일반적으로 스네이크 알고리즘은 잡음에 민감하고 초기 모델을 어떻게 설정하는가에 따라 추출 성능이 크게 좌우되기 때문에 주로 단순한 배경의 영상에서 정면 얼굴의 추출에 사용되어왔다 본 연구에서는 이러한 단점을 파악하기 위해, 먼저 YIQ 색상 모델의 I 성분을 이용한 색상 정보와 차 영상 정보를 사용하여 얼굴의 최소 포함 사각형(minimum enclosing rectangle; MER)을 찾고, 이 얼굴 영역 내에서 기하학적인 위치 정보와 에지 정보를 이용하여 눈, 입, 눈썹, 코의 MER을 설정한다. 그런 다음, 각 요소의 MER 내에서 1차 미분과 2차 미분에 근거한 내부 에너지와 에지에 기반한 영상 에너지를 이용한 스네이크 알고리즘을 적용한다. 이때, 에지 영상에서 얼굴 주변의 복잡한 잡음을 제거하기 위하여 색상 정보 영상과 차 영상에 각각 모폴로지(morphology)의 팽창(dilation) 연산을 적용하고 이들의 AND 결합 영상에 팽창 연산을 다시 적용한 이진 영상을 필터로 사용한다. 총 7명으로부터 양 눈이 보이는 정면 유사 방향의 영상을 20장씩 취득하여 총 140장에 대해 실험한 결과, MER의 오차율은 얼굴, 눈, 입에 대해 각각 6.2%, 11.2%, 9.4%로 나타났다. 또한, 스네이크의 초기 제어점을 얼굴은 44개, 눈은 16개, 입은 24개로 지정하여 MER추출에 성공한 영상에 대해 스네이크 알고리즘을 수행한 결과, 추출된 영역의 오차율은 각각 2.2%, 2.6%, 2.5%로 나타났다.해서 Template-based reasoning 예를 보인다 본 방법론은 검색노력을 줄이고, 검색에 있어 Feasibility와 Admissibility를 보장한다.매김할 수 있는 중요한 계기가 될 것이다.재무/비재무적 지표를 고려한 인공신경망기법의 예측적중률이 높은 것으로 나타났다. 즉, 로지스틱회귀 분석의 재무적 지표모형은 훈련, 시험용이 84.45%, 85.10%인 반면, 재무/비재무적 지표모형은 84.45%, 85.08%로서 거의 동일한 예측적중률을 가졌으나 인공신경망기법 분석에서는 재무적 지표모형이 92.23%, 85.10%인 반면, 재무/비재무적 지표모형에서는 91.12%, 88.06%로서 향상된 예측적중률을 나타내었다.ting LMS according to increasing the step-size parameter $\mu$ in the experimentally computed. learning curve. Also we find that convergence speed of proposed algorithm is increased by (B+1) time proportional to B which B is the number of recycled data buffer without complexity of computation. Adaptive transversal filter with proposed data recycling buffer algorithm could efficiently reject ISI of channel and increase speed of convergence in avoidance burden of computational complexity in reality when it was experimented having the same condition of

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.1
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• pp.62-71
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• 2016

Food insecurity is associated with poor health outcomes. In particular, previous studies marked the adverse outcomes on mental health. This study examined the association of food insecurity and mental health in Korean adults using the data from the 2013 Korean National Health and Nutrition Examination Survey (KNHANES). The study population was 5,685 adults in Korea. Food insecurity was examined using 18-items. A diagnosis of depression was considered to be depression. Depressive symptoms were defined as more than 2 weeks of depression feelings. Multivariate logistic regression models examined the associations between food insecurity and depression and depressive symptom. The overall prevalence of depression was 3.8% in the participants. Food insecurity was associated significantly with depression and depressive symptom in the unadjusted and age and sex adjusted model. Food insecurity was associated with depression, depressive symptoms in the multivariate logistic regression model (OR:3.49, OR:3.70). Marginal food insecurity was not associated with depression in the multivariate logistic regression model. The results showed that food insecurity is associated with depression and depressive symptoms in adults. Multi-disciplinary interventions are needed including nutrition, health, health policy, and a healthy environment for the food insecurity group to achieve a better health outcome, especially mental health.

• The Journal of Bigdata
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• v.3 no.2
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• pp.71-78
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• 2018

The study is based on machine learning techniques to increase the accuracy of the forest fire predictive model. It used 14 years of data from 2003 to 2016 in Gang-won-do where forest fire were the most frequent. To reduce weather data errors, Gang-won-do was divided into nine areas and weather data from each region was used. However, dividing the forest fire forecast model into nine zones would make a large difference between the date of occurrence and the date of not occurring. Imbalance issues can degrade model performance. To address this, several sampling methods were applied. To increase the accuracy of the model, five indices in the Canadian Frost Fire Weather Index (FWI) were used as derived variable. The modeling method used statistical methods for logistic regression and machine learning methods for random forest and xgboost. The selection criteria for each zone's final model were set in consideration of accuracy, sensitivity and specificity, and the prediction of the nine zones resulted in 80 of the 104 fires that occurred, and 7426 of the 9758 non-fires. Overall accuracy was 76.1%.