• Journal of Families and Better Life
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• v.28 no.1
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• pp.145-157
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• 2010

This is a study that explores the influence of self-differentiation and acculturation among married immigrant women on their feelings of marriage satisfaction by residential area. The aim was to verify the level of self-differentiation and acculturation that married immigrant women secure from multicultural marriage. To achieve this objective, the study widely distributed a questionnaire that targeted women who were participating in education and other services at health and family support centers, multi-cultural family support centers, general social welfare centers, immigrant women shelters, and Korean language classrooms which are located in Seoul, Gyeonggi, Chungcheong, Jeolla, and Gyeongsang. Data analysis involved frequency analysis, descriptive statistics, one-way-Anova, and multiple regression analysis. Based on the results of descriptive statistics, two factors, namely, (1)interpersonal-relation differentiation, a sub-scale of self-differentiation, and (2)marginalization, a sub-scale of acculturation, had the lowest average. In verifying its various hypotheses, the study achieved the following results. Firstly, among demographic characteristics, there was difference of the mean in the marriage period, average monthly income, the frequency of meetings with the married woman´s parents-in-law and her own parents, and the average cost of supporting the woman's parents-in-law and her own parents. Secondly, among demographic characteristics, the variable of influencing marriage satisfaction showed negative influence in the case of women dwelling in farming and fishing villages. This shows that women residing in cities whether small, medium or large have higher marriage satisfaction. Also, as a result of verifying whether self-differentiation has influence on marriage satisfaction, the element of interpersonal-relations differentiation had a negative influence on marriage satisfaction. Thirdly, as for influence of acculturation upon marriage satisfaction, the study showed that just integration, which is a sub-scale of acculturation had positive effect on marriage satisfaction. In other words, the study showed that the interpersonal-relation differentiation among the sub-scales of self-differentiation among married immigrant women had a negative influence, and that integration among the sub-scales in acculturation had a positive influence on marriage satisfaction. Based on these results, in order to increase interpersonal-relation differentiation, as well as marriage satisfaction among immigrant women, the study suggests the integration of the women's families with the nuclear and extended families in the communities where the women reside.

• Korean Journal of Health Education and Promotion
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• v.18 no.3
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• pp.11-36
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• 2001

The purpose of this study is to analyze health behavior by comparing the difference between self-perceived health status and health examination results. The study subjects consist of 7,702 people aged over 20, surveyed by Health Interview survey, Health Examination survey, Dietary Life survey, Health Consciousness and Behavior survey. Data used in the study are drawn from raw data from a 1998 National Health and Nutrition survey. General characteristics variables are sex, age, education level, residential area, marital status, occupation, and living standard while dichotomous variables, ‘not healthy’ and ‘healthy’ are used to measure self-perceived health status. Variables for health examination results are high blood pressure, high cholesterol, diabetes, liver diseases, liver inflammation, kidney diseases, normal weight, regular diet, optimum sleeping time(7-8 hours), regular health examination and health behavior practice group. Major findings of the study are as follows: 1) Analysis of self-perceived health status and health behavior by disease: Variables significantly correlated with high self-perceived health status have strong associations with high health behavior practice, which supports the hypothesis that as one has high self-perceived health status, one is more likely to practice health promoting behavior. The results of analysis of health behavior differences by dividing subjects into two categories, ‘cases of illness’ and ‘cases of no illness’ indicate that drinking, sleeping time, health examination are significant variables (p〈0.001, 0.05) whereas smoking, weight control, regular exercise, regular diet are not significant. 2) Analysis of disparity patterns between self-perceived health status and health examination: The hypothesis that health behaviors would be different according to the disparity pattern between self-perceived health status and health examination is supported as a result of χ2 test. Among Type I : Self-perceived health status is high and actual health status is good (no disease) Type II: Self-perceived health status is high and actual health status is poor(have disease) Type III: Self-perceived health status is low and actual health status is good(no disease) Type IN: Self-perceived health status is low and actual health status is poor(have disease) Type I and Type IV show no disparity, Type I shows the highest health promoting behavior whereas Type IV shows the lowest health promoting behavior. Type II, and III, compared to Type I, practise lower health promoting behavior. Multi-logistics regression analysis was conducted to find out the degree of impact on health behavior. Independent variables are general characteristics, self-perceived health status and health examination result and presence of illness, while the dependent variable is health promoting behavior. The analysis of the impact of self-perceived health status on the health promoting behavior shows that smoking, drinking, weight control, regular exercise, health examination practice, and/or regular diet are significantly correlated to self-perceived health status. High self-perceived health status is inversely related to high health promoting behavior. This finding supports the hypothesis that the higher one perceives one's health, the more likely one is to practice health promoting behavior. On the contrary, the presence of illness has little impact on health promoting behavior. 3) Multiple logistics analysis on how disparity patterns between self-perceived health status and health examination affect health behavior: The results of multiple logistics analysis made on health behavior variables compared to the standard variable are as follows: When analyzed on the standard of Type I, smoking is a significant risk factor for the Type IV. In case of drinking, all the patterns show a high probability of relative risk ratio. With regard to weight control, it is a risk factor for Type II while all the patterns show high probability of not practising when analyzed on the standard of type IV. Type III and IV show high probability of not doing regular exercise while Type IV, shows a high probability of not taking appropriate sleeping time. When analyzed on the standard of type IV, all the patterns show a high probability of not taking health examinations. Type III and IV show a high probability of not having regular meals. As for overall health promoting behavior, Type III and IV show a high relative risk ratio. These two groups have low self-perceived health status. It implies that self-perceived health status has significant impact on health promoting behavior. This is also supported by the fact that Type I with high self-perceived health status and no illness shows a high practice rate of health promoting behavior. Types II and III the groups with high disparity between self-perceived health status and health examination results, show a low practice rate of health promoting behavior when compared to Type I. Type IV, that is the group with low self-perceived health status and actual illness, shows the lowest practice of health promoting behavior. It is highly probable that this type proves to be the poorest health group.

• Korean Security Journal
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• no.42
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• pp.7-36
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• 2015

This study aims at analyzing difference of social Security network, Community unity and local government trust according to socio-demographical features, exploring the relation of social Security network, Community unity and local government trust according to socio-demographical features, presenting results between each variable as a model and verifying the property of mutual ones. This study sampled general citizens in Gwangju for about 15 days Aug. 15 through Aug. 30, 2014, distributed total 450 copies using cluster random sampling, gathered 438 persons, 412 persons of whom were used for analysis. This study verified the validity and credibility of the questionnaire through an experts' meeting, preliminary test, factor analysis and credibility analysis. The credibility of questionnaire was ${\alpha}=.809{\sim}{\alpha}=.890$. The inout data were analyzed by study purpose using SPSSWIN 18.0, as statistical techniques, factor analysis, credibility analysis, correlation analysis, independent sample t verification, ANOVA, multi-regression analysis, path analysis etc. were used. the findings obtained through the above study methods are as follows. First, building a social Security network has an effect on Community institution. That is, the more activated a, the higher awareness on institution. the more activated street CCTV facilities, anti-crime design, local government Security education, the higher the stability. Second, building a social Security network has an effect on trust of local government. That is, the activated local autonomous anti-crime activity, anti-crime design. local government's Security education, police public oder service, the more increased trust of policy, service management, busines performance. Third, Community unity has an effect on trust of local government. That is, the better Community institution is achieved, the higher trust of policy. Also the stabler Community institution, the higher trust of business performance. Fourth, building a social Security network has a direct or indirect effect on Community unity and local government trust. That is, social Security network has a direct effect on trust of local government, but it has a higher effect through Community unity of parameter. Such results showed that Community unity in Gwangju Region is an important factor, which means it is an important variable mediating building a social Security network and trust of local government. To win trust of local residents, we need to prepare for various cultural events and active communication space and build a social Security network for uniting them.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.147-168
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• 2017

There have been many studies on accurate stock market forecasting in academia for a long time, and now there are also various forecasting models using various techniques. Recently, many attempts have been made to predict the stock index using various machine learning methods including Deep Learning. Although the fundamental analysis and the technical analysis method are used for the analysis of the traditional stock investment transaction, the technical analysis method is more useful for the application of the short-term transaction prediction or statistical and mathematical techniques. Most of the studies that have been conducted using these technical indicators have studied the model of predicting stock prices by binary classification - rising or falling - of stock market fluctuations in the future market (usually next trading day). However, it is also true that this binary classification has many unfavorable aspects in predicting trends, identifying trading signals, or signaling portfolio rebalancing. In this study, we try to predict the stock index by expanding the stock index trend (upward trend, boxed, downward trend) to the multiple classification system in the existing binary index method. In order to solve this multi-classification problem, a technique such as Multinomial Logistic Regression Analysis (MLOGIT), Multiple Discriminant Analysis (MDA) or Artificial Neural Networks (ANN) we propose an optimization model using Genetic Algorithm as a wrapper for improving the performance of this model using Multi-classification Support Vector Machines (MSVM), which has proved to be superior in prediction performance. In particular, the proposed model named GA-MSVM is designed to maximize model performance by optimizing not only the kernel function parameters of MSVM, but also the optimal selection of input variables (feature selection) as well as instance selection. In order to verify the performance of the proposed model, we applied the proposed method to the real data. The results show that the proposed method is more effective than the conventional multivariate SVM, which has been known to show the best prediction performance up to now, as well as existing artificial intelligence / data mining techniques such as MDA, MLOGIT, CBR, and it is confirmed that the prediction performance is better than this. Especially, it has been confirmed that the 'instance selection' plays a very important role in predicting the stock index trend, and it is confirmed that the improvement effect of the model is more important than other factors. To verify the usefulness of GA-MSVM, we applied it to Korea's real KOSPI200 stock index trend forecast. Our research is primarily aimed at predicting trend segments to capture signal acquisition or short-term trend transition points. The experimental data set includes technical indicators such as the price and volatility index (2004 ~ 2017) and macroeconomic data (interest rate, exchange rate, S&P 500, etc.) of KOSPI200 stock index in Korea. Using a variety of statistical methods including one-way ANOVA and stepwise MDA, 15 indicators were selected as candidate independent variables. The dependent variable, trend classification, was classified into three states: 1 (upward trend), 0 (boxed), and -1 (downward trend). 70% of the total data for each class was used for training and the remaining 30% was used for verifying. To verify the performance of the proposed model, several comparative model experiments such as MDA, MLOGIT, CBR, ANN and MSVM were conducted. MSVM has adopted the One-Against-One (OAO) approach, which is known as the most accurate approach among the various MSVM approaches. Although there are some limitations, the final experimental results demonstrate that the proposed model, GA-MSVM, performs at a significantly higher level than all comparative models.

• Journal of the Korea Convergence Society
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• v.9 no.6
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• pp.329-337
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• 2018

This study was designed to investigate the effect of medical education on the health behavior of elderly people aged 65 years or older who have the diagnosis of metabolic syndrome. From 2014 community health survey data, 9042 people with hypertension and diabetes who had disease management education experience in elderly people over 65 years old were used as the final analysis data. Data were analyzed by Chi-square test and multi-variable logistic regression. The results of the study showed that the perceived rate of cardiovascular health factors was higher in the elderly living alone with disease management education (p <.001), and the probability of walking more than 3 days per week was 1.145(P = .002), not drinking at the present time was 1.212 times (p <.001), eating thinly was 1.184 times (p = .002). From the results of this study, it is required to develop the health promotion policy that reflects the importance of education by health care providers and the health information provided to the elderly living alone.

• Journal of the Korean Data and Information Science Society
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• v.28 no.6
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• pp.1427-1435
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• 2017

The smart farm is a remarkable system because it utilizes information and communication technologies in agriculture to bring high productivity and excellent qualities of crops. It automatically measures the growth environment of the crops and accumulates huge amounts of environmental information in real time growing in smart farms using multi-variable control of environmental factors. The statistical model using the collected big data will be helpful for decision making in order to control optimal growth environment of crops in smart farms. Using data collected from a smart farm of tomato, we carried out multiple regression analysis to determine the relationship between yield and environmental factors and to predict yield of tomato. In this study, appropriate parameter modification was made for environmental factors considering tomato growth. Using these new factors, we fit the model and derived the optimal environmental factors that affect the yields of tomato. Based on this, we could predict the yields of tomato. It is expected that growth environment can be controlled to improve tomato productivities by using statistical model.

• Journal of Astronomy and Space Sciences
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• v.21 no.2
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• pp.141-152
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• 2004

Optics fabrication process for precision space optical parts includes bound abrasive grinding, loose abrasive lapping and polishing. The traditional bound abrasive grinding with bronze bond cupped diamond wheel leaves the machine marks of about $20{mu}m$ rms in height and the subsurface damage of about 1 ${mu}m$ rms in height to be removed by subsequent loose abrasive lapping. We explored an efficient quantitative control of precision CNC grinding. The machining parameters such as grain size, work-piece rotation speed and feed rate were altered while grinding the work-piece surfaces of 20-100 mm in diameter. The input grinding variables and the resulting surface quality data were used to build grinding prediction models using empirical and multi-variable regression analysis. The effectiveness of such grinding prediction models was then examined by running a series of precision CNC grinding operation with a set of controlled input variables and predicted output surface quality indicators. The experiment achieved the predictability down to ${pm}20$ nm in height and the surface roughness down to 36 nm in height. This study contributed to improvement of the process efficiency reaching directly the polishing and figuring process without the need for the loose abrasive lapping stage.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.4
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• pp.1728-1736
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• 2013

The purpose of this study is to identify prognostic predictors of postoperative acute renal failure(ARF) for the patient undergoing cardiac surgery. Retrospectively review the electronic hospital database at a A hospital from Jan 2008 to Dec. 2011. 483 patients were included in this study. They were divided into a occurrence of ARF group(n=59) and a non occurrence of ARF group(n=424). ARF occurred in 59 cases (12.2%). Multi-variable logistic regression analysis identified that preoperative risk factors include creatinine(OR 3.92, p=<.001), advanced age(OR 2.142, p=.015), female(OR 2.165, p=.015), hypertension(OR 2.513, p=.005), NYHA(New York Heart Association) class II(OR 3.081, p=.003), and III(OR 6.759, p=.004), and intraoperative risk factor includes blood transfusion(OR 3.753, p=<.001), and postoperative factors include bilirubin(OR 4.541, p=.028), creatine(OR 8.554, p=.003), and cardiac output(OR 0.214, p=.033) as a prognostic predictors. The development of postoperative ARF could be a reason for increase in rate of complication and mortality after cardiac surgery. therefore the prevention of ARF is of paramount importance and treatment strategies are urgently needed.

• Quality Improvement in Health Care
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• v.9 no.2
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• pp.116-133
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• 2002

Background : It is very common in Korea to take care of non-acute patients in an acute setting, due to the lack of long-term facilities. Long term hospitalization increase medical expenses and decreases the bed utilization, which can affect the urgent and emergent admissions, and eventually jeopardize the hospital financially. In this study, strategies for effective transfers to the lower levels of care, and to decrease the length of stay were presented by surveying and analyzing the patient's knowledge of the transfer needs, and the willingness to transfer those whose hospital length of stay was more than 30days. Method : The survey is subject to a group of 251 patients who have been hospitalized over 30 days in a general hospital in Seoul. Excluding those that were in the Intensive Care Unit and psychiatric ward, 214 in-patients were used as participants. They were surveyed from April 9, 2002 to April 17, 2002. One hundred and thirty seven out of 214 were responded which made the response rate 64%. Data were analyzed by SAS and SPSS. Result : Multi-variable Logistic Regression Analysis showed a significant effect in medical expenses, knowledge of referral system and the information of the receiving hospital. The financial burden in medical expenses made the patient 10.7 times more willing to be transferred, knowledge of the referral system made them 5 times more willing to be transferred, and the information of receiving hospital makes 6.5 times more willing to be transferred. Reasons for willing to be transferred to a lower level of care were the phase of physical therapy, the distance from home, the attending physician's advice and being unable to be treated as an out patient. Reasons for refusing to be transferred were the following. The attending physician's competency, not being ready to be discharged, not trusting the receiving hospital's competency due to the lack of information, or never hearing about the referring system by the attending physician. Conclusion : Based on this, strategies for the effective transfer to the lower levels of care were suggested. It is desirable for the attending physician to be actively involved by making an effort to explain the transfer need, and referring to the Healthcare Coordinating Center, which can help the patient make the right decision. Nationwide networking for the referral system is the another key factor that may need to be suggested as an alternative to decrease the medical expenses. Collaborating with the Home Health Agency for the early discharge planning and the Social Service Department for financial aid are also needed. It is recommended that the hospital should expedite the transfer process by prioritizing the cost and the information as medical expenses, knowledge of referring system and the information of the receiving hospital, are the most important factors to the willingness to transfer to a lower level of care.

• Korea Journal of Hospital Management
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• v.12 no.3
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• pp.47-67
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• 2007

This paper aims at suggesting several ways lo change financial vulnerability and to improve managerial capability of local public hospitals (LPHs) in Korea through the identification of factors affecting profitability. Several findings of the research are as follows: To begin with, LPHs exhibited a statistically significant difference in their profitability from one another, according to tile analyses of their profitable margins from tile general characteristics. It depends on the number of hospitals in the area, the population of the hospital-built area, the number of competing hospitals, the number of staff per 100 beds, the opening of special clinic, the educational function, and the capacity of rooms. However, there was no variable in the managerial characteristics, presenting a significant difference, in contrast with hospitals which have been managed by private companies and made a great amount of profits. Second, according to the analyses of profit differences in behavioral effort-characteristics, a statistically significant difference was revealed upon the basis of the efforts to improve the clinic service, invite special patients, and shorten the period of being hospitalized. Third, the result of analyses about the difference of profitability from medical care and finance is statistically significant in the rate of labor cost, the rate of management cost, bed-occupancy rate, and the period of being hospitalized. Fourth, according to the analyses of the factors influencing the net profit ratio of the entire capital, Adjusted explanatory power(Adjusted $R^2$) was shown up to 65.2%, which is high. To compare the adjusted explanatory power stage by stage, the first stage model applying only two variables such as structural and strategic characteristics exhibited 23.8%, and the second stage model adding financial characteristics showed 51.5%. The explanatory power was much improved up to 65.2% when the third stage model incorporated the outcome of medical care performance. When the return on investment(ROI) was examined by using the multi-variate linear regression analysis at the final model of third stage, it was found that ROI had a positive relationship with the increase rate of patients, labor costs per doctor, and medical care rate of socially protected inpatients. However, it revealed that ROI had a negative relationship with the ratio of labor costs, the number of patients per managerial staff, and occupancy rate of rooms, respectively. The research suggests that in order for LPHs to increase profitability, LPH, should make efforts not only to attract patients to the hospitals without any discrimination of the patients depending on their financial status, but also to develop efficient management methods to reduce labor costs.