• Journal of Preventive Medicine and Public Health
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• v.38 no.4
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• pp.408-414
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• 2005

Objectives : The purpose of this study was to determine the impact of the sociodemographic and health characteristics on the out-of-pocket health spending of the individuals aged 20 and older in Korea. Methods : We used the data from the 2001 National Public Health and Nutrition Survey. The final sample size was 26,154 persons. Multiple linear regression models were used according to the age groups, that is, one model was used for those people under the age of sixty-five and the other was used for those people aged sixty-five and older. In these analyses, the expenditures were transformed to a logarithmic scale to reduce the skewness of the results. Results : Out-of-pocket health expenditures for those people under the age of 65 averaged 14,800 won per month, whereas expenditures for those people aged 65 and older averaged 27,200 won per month. In the regression analysis, the insurance type, resident area, self-reported health status, acute or chronic condition and bed-disability days were the statistically significant determinants for both age groups. Gender and age were statistically significant determinants only for the non-elderly. Conclusions : The findings from this study show that the mean out-of-pocket health expenditures varied according to the age groups and also several diverse characteristics. Thus, policymakers should consider the out-of-pocket health expenditure differential between the elderly and non-elderly persons. Improvement of the insurance coverage for the economically vulnerable subgroups that were identified in this study should be carefully considered. In addition, it is necessary to assess the impact of out-of-pocket spending on the peoples' health care utilization.

• Analytical Science and Technology
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• v.11 no.1
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• pp.47-53
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• 1998

This study is to improve the diagnosis of diabetes mellitus and the self-monitoring of blood glucose in people with diabetes by providing a non-invasive method of monitoring blood glucose. A near-infrared (NIR) spectrophotometer was used to measure absorption spectra of 80 glucose samples ranges from 1 mg/dL to 200 mg/dL, and shows the standard error of prediction 1.8 mg/dL. Also, to investigate the effect of interference in blood, NaCl and sand were added in glucose and found the standard error of prediction of 2.8 mg/dL and 3.8 mg/dL, respectively. A new and more accurate calibration system for the spectrophotometer was developed from systematic study of light scattering, which cause nonlinear spectrophotometer response.

• Journal of The Korean Society of Agricultural Engineers
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• v.65 no.4
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• pp.1-10
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• 2023

As a result of climate change, non-point source pollution (NPS) from farmland with the steep slope during the rainy season is expected to have a significant impact on the water system. This study aimed to evaluate the effect of furrow mulching using alfalfa and PAM (Polyacrylamide) materials for each rainfall event, while considering the load characteristics of NPS. The study was conducted in Wanju-gun, Jeollabuk-do, in 2022, with a testbed that had a slope of 13%, sandy loam soil, and maize crops. The testbed was composed of four plots: bare soil (Bare), No mulching (Cont.), Vegetation mulching (VM), and PAM mulching (PM). Runoff was collected from each rainfall event using a 1/40 sampler and the NPS load was calculated by measuring the concentrations of SS, T-N, T-P, and TOC. During farming season, the reduction efficiency of NPS load was 37.1~59.5% for VM and 38.2~75.7% for PM. The analysis found that VM had a linear regression correlation (R²=0.28~0.86, P-value=0.01~0.1) with elapsed time of application, while PM had a quadratic regression correlation (R²=0.35~0.80, P-value=0.1). These results suggest that the selection of furrow mulch materials and the appropriate application method play a crucial role in reducing non-point pollution in farmland. Therefore, further studies on the time-series reduction effect based on the application method are recommended to develop more effective preemptive reduction technologies.

• International Journal of Advanced Culture Technology
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• v.10 no.2
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• pp.307-313
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• 2022

Although telecommuting of SMEs has been activated due to the COVID-19 phenomenon, the security model for this is insufficient. Accordingly, the study was divided into threats centered on smartphones and threats centered on smartphone users. As a result of the study, one-third of SMEs are working from home. At this company with 100 employees, more than 50% of them work from home.and In the metal, machinery and chemical industries with factories, 20% of them work from home. As a result of analyzing the correlation between telecommuting according to the presence or absence of a factory, the correlation coefficient (r=-.385) has a clear linear relationship. And, as a result of the regression analysis, the R-squared value was 0.148, so companies with factories are highly related to telecommuting. In other words, we found that SMEs with factories do not want to work from home. In addition, as a result of analyzing the level of security threats, there were great concerns about theft, hacking, and phone taking during remote work. As limitations of the study, there were difficulties in selecting SMEs from the population in a non-face-to-face work environment, and there were limitations in the questionnaire items for deriving a non-face-to-face work environment.

• Animal Bioscience
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• v.34 no.4
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• pp.506-515
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• 2021

Objective: Initial consequence of inbreeding is inbreeding depression which impairs the performance of growth, production, health, fertility and survival traits in different animal breeds and populations. The effect of inbreeding on economically important traits should be accurately estimated. The effect of inbreeding depression on growth traits in sheep has been reported in many breeds. Based on this, the main objective of the present research was to evaluate the impact of inbreeding on some growth traits of Iranian Baluchi sheep breed using quantile regression model. Methods: Pedigree and growth traits records of 13,633 Baluchi lambs born from year 1989 to 2016 were used in this research. The traits were birth weight, weaning weight, six-month weight, nine-month weight, and yearling weight. The contribution, inbreeding and co-ancestry software was used to calculate the pedigree statistics and inbreeding coefficients. To evaluate the impact of inbreeding on different quantiles of each growth trait, a series of quantile regression models were fitted using QUANTREG procedure of SAS software. Annual trend of inbreeding was also estimated fitting a simple linear regression of lamb's inbreeding coefficient on the birth year. Results: Average inbreeding coefficient of the population was 1.63 percent. Annual increase rate of inbreeding of the flock was 0.11 percent (p<0.01). The results showed that the effect of inbreeding in different quantiles of growth traits is not similar. Also, inbreeding affected differently on growth traits, considering lambs' sex and type of birth. Conclusion: Quantile regression revealed that inbreeding did not have similar effect on different quantiles of growth traits in Iranian Baluchi lambs indicating that at a given age and inbreeding coefficient, lambs with different sex and birth type were not equally influenced by inbreeding.

• Journal of Korean Society of Transportation
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• v.27 no.3
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• pp.81-90
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• 2009

The safety management of a road network comprises four basic inter-related components：identification of sites(black spot) requiring safety investigation, diagnosis of safety problems, selection of feasible treatments for potential treatment candidates, and prioritization of treatments given limited budgets(Persaud, 2001). Identification process of selecting black spot is very important for efficient investigation of sites. In this study, the accident prediction model for EB method was developed by using accident data and geometric conditions of black spots selected from four-leg signalized intersections in In-cheon City for three years (2004-2006). In addition, by comparing the rank nomination technique using EB method to that by using accident counts, we managed to show the problems which the existing method have and the necessity for developing rational prediction model. As a result, in terms of total number of accidents, both the counts predicted by existing non-linear regression model and that by EB method have high good of fitness, but EB method, considering both the accident counts by sites and total number of accident, has better good of fitness than non-linear poison model. According to the result of the comparison of ranks nominated for treatment between two methods, the rank for treatment of almost sites does not change but SeoHae intersection and a few other intersections have significant changes in their rank. This shows that, with the technique proposed in the study, the RTM problem caused by using real accident counts can be overcome.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.19-31
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• 2022

In Machine learning, we usually divide the entire data into training data and test data, train the model using training data, and use test data to determine the accuracy and generalization performance of the model. In the case of models with low generalization performance, the prediction accuracy of newly data is significantly reduced, and the model is said to be overfit. This study is about a method of generating training data based on central limit theorem and combining it with existed training data to increase normality and using this data to train models and increase generalization performance. To this, data were generated using sample mean and standard deviation for each feature of the data by utilizing the characteristic of central limit theorem, and new training data was constructed by combining them with existed training data. To determine the degree of increase in normality, the Kolmogorov-Smirnov normality test was conducted, and it was confirmed that the new training data showed increased normality compared to the existed data. Generalization performance was measured through differences in prediction accuracy for training data and test data. As a result of measuring the degree of increase in generalization performance by applying this to K-Nearest Neighbors (KNN), Logistic Regression, and Linear Discriminant Analysis (LDA), it was confirmed that generalization performance was improved for KNN, a non-parametric technique, and LDA, which assumes normality between model building.

• Journal of the Korean GEO-environmental Society
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• v.4 no.2
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• pp.77-91
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• 2003

The Hoek-Brown failure criterion for rock masses developed first in 1980 is widely accepted and has been applied in a variety of rock engineering problems including slope analyses. The failure criterion was modified over the years because rock mass strength by the original failure criterion in 1980 was overestimated. The modified failure criterion, named Generalized Hoek-Brown Failure Criterion, was proposed with a new classification called the Geological Strength Index(GSI) in 1994. Generally, Hoek-Brown failure criterion is applied in numerical analyses of rock mass behaviors using equivalent Mohr-Coulomb parameters estimated by linear regression method. But these parameters estimated by this method have some inaccuracies to be applied and to be incorporated into numerical models and limit equilibrium programs. The most important issue is that this method cannot take account of non-linear characteristics of Hoek-Brown criterion, therefore, equivalent Mohr-Coulomb parameters is used as constant values regardless of field stress distribution in rock masses. In this study, the numerical analysis on rock slope stability considering non-linear characteristics of Hoek-Brown failure criterion was carried out. Futhermore, by the latest Hoek-Brown failure criterion in 2002, the revised estimating method of equivalent Mohr-Coulomb parameters was applied and rock mass damage criterion is introduced to account for the strength reduction due to stress relaxation and blast damge in slope stability.

• Journal of the Korea Computer Graphics Society
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• v.29 no.3
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• pp.117-125
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• 2023

In the development of clothing design through virtual simulation, it is essential to minimize the differences between the virtual and the real world as much as possible. The most critical task to enhance the similarity between virtual and real garments is to find simulation parameters that can closely emulate the physical properties of the actual fabric in use. The simulation parameter optimization process requires manual tuning by experts, demanding high expertise and a significant amount of time. Especially, considerable time is consumed in repeatedly running simulations to check the results of applying the tuned simulation parameters. Recently, to tackle this issue, artificial neural network learning models have been proposed that swiftly estimate the results of drape test simulations, which are predominantly used for parameter tuning. In these earlier studies, relatively simple linear stiffness models were used, and instead of estimating the entirety of the drape mesh, they estimated only a portion of the mesh and interpolated the rest. However, there is still a scarcity of research on non-linear stiffness models, which are commonly used in actual garment design. In this paper, we propose a learning model for estimating the results of drape simulations for non-linear stiffness models. Our learning model estimates the full high-resolution mesh model of drape. To validate the performance of the proposed method, experiments were conducted using three different drape test methods, demonstrating high accuracy in estimation.

• Journal of Convergence for Information Technology
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• v.9 no.6
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• pp.1-6
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• 2019

In this study, the development of a weight estimation model of electronic scale with nonlinear characteristics is presented using polynomial regression analysis. The output voltage of the load cell was measured directly using the reference mass. And a polynomial regression model was obtained using the matrix and curve fitting function of MS Office Excel. The weight was measured in 100g units using a load cell electronic scale measuring up to 5kg and the polynomial regression model was obtained. The error was calculated for simple($1^{st}$), $2^{nd}$ and $3^{rd}$ order polynomial regression. To analyze the suitability of the regression function for each model, the coefficient of determination was presented to indicate the correlation between the estimated mass and the measured data. Using the third order polynomial model proposed here, a very accurate model was obtained with a standard deviation of 10g and the determinant coefficient of 1.0. Based on the theory of multi regression model presented here, it can be used in various statistical researches such as weather forecast, new drug development and economic indicators analysis using logistic regression analysis, which has been widely used in artificial intelligence fields.