• Journal of Korean Society of Transportation
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• v.35 no.5
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• pp.423-433
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• 2017

During recent 5 years, it was recorded that 20% of total accident frequency and 30% of total number of death have been occurred due to drowsy driving. Drowsy driving accident is result from the loss of driving ability due to driver's accumulated fatigue. Continuous driving time can be measured as a surrogate variable to quantify the level of fatigue. The main purpose of this research is to investigate statistical correlation between the proportion of continuous driving vehicle (more than 2 hours) and the number of drowsy accidents. To carry this out, continuous driving time was measured using GPS route-guidance trajectory data. Also, accident frequency, traffic volume and segment length were collected to estimate safety performance function (SPF) for Jungbunearuk expressway in Korea. Through various types of estimated SPFs, statistical correlation was analyzed based on estimated statistical indices. This research can provide theoretical background for enforcement to regulate commercial vehicle driver's continuous driving time. In addition, throughout the trajectory data expansion, it is expected that strategy for anti-drowsy driving facilities installation can be established based on the suggested methodology.

• Journal of Cadastre & Land InformatiX
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• v.49 no.2
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• pp.135-149
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• 2019

This article analyzed economic effects of changes in spatial accessibility on regional touruism expenditure structure resulting from highway investments on Gangwon province, Korea. The Seemingly Unrelated Regress(SUR) model is applied to analyze the structure of change in tourism expenditure of Gangwon Province, and the competition and complementary relationship of tourist demand were analyzed among 18 counties in Gangwon by Dendrinos-Sonis method. The spatial accessibility has a significant effect on the increase in amount of tourist expenditure, but by 1% increase in the accessibility resulted in a reduction of length of stay -0.18% and travel costs -0.34% by respectively. The most powerful variable for improving the on-site economy is the tourist service establishment, which increases one unit, the amount of tourist expenditure increased by 3.6%. Moreover, the competition and supplemental relationship of tourism demands in Gangwon was decided by the conditions under which traffic flow with passing occurred, such as inland or ocean-typed travel attractions, adjacent or remote regions to the highway. The limitations of this study were not able to use raw data of tourism expenditures before and after the opening the highway due to the restriction of priority data, and further research on the appropriate level of spatial accessibility for the regional economy is needed.

• Korean Journal of Environment and Ecology
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• v.33 no.4
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• pp.440-446
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• 2019

This study examined the growth and fruiting characteristics and the acorns biomass allometric equation of Quercus acuta to provide reference data related to the growth and seed supply during the restoration of evergreen forest in the warm temperate zone in Wando Island, Korea. For the growth survey, we selected and cut three sample trees having a mean diameter at breast height (DBH) to investigate the growth analysis through a stem analysis. We then developed the allometric equation (Y=aX+b) of DBH and tree height growth characteristic (Y) according to the average tree age (X) of sampled trees and estimated the DBH and tree height according to the age of Quercus acuta. For the fruiting survey, we selected and cut three sample trees with full fruit in August when, they are at the early mature fruiting stage, for the analysis. To develop the acorns/tree biomass allometric equation of Quercus acuta, we selected and cut ten sample trees of evenly divided diameters. The acorns biomass allometric equation ($Y=aX^b$) was derived by analyzing the biomass (Y) and the growth characteristics (X), such as the DBH, tree height, crown width, and crown height. The allometric equations of average tree age according to DBH and tree height were Y=0506X-2.064 ($R^2=0.999$) and Y=0.321X+0689 ($R^2=0.992$), respectively. The developed allometric equations estimated that the DBH were 3.0cm, 8.1cm, 13.1cm and 18.2cm while the tree heights were 3.9m, 7.1m, 10.3m, and 13.5m when the tree ages were 10, 20, 30, and 40 years, respectively. The analysis results of fruiting characteristics showed that the length, the diameter, the number of fruits, and the number of acorns per fruiting branch had the statistically significant difference and tended to decrease from the upper part to the lower part of crown downward. The total number of acorns was 1,312 acorns/tree in the upper part, 115 acorns/tree in the middle part, and 5 acorns/tree in the lower part of the crown. The allometric equation for the amount of acorns with DBH as an independent variable was $Y=0.003X^{4.260}$ with the coefficient of determination at 0.896. Although the coefficient of determination of the allometric equation using only DBH as the independent variable was lower than that using DBH and tree height ($D^2H$), it would be more practical to consider only DBH as the independent variable because of measurement errors.

• 한국사회복지학회:학술대회논문집
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• 2003.10a
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• pp.185-216
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• 2003

This study was aimed at investigating important factors influencing career success among regular workers. The current researcher scrutinized the degree to which variables and factors affect the career success and occupational turnover rates of the research participants. At the same tune, two hypothetical path models established by the researcher were examined using linear multiple regression methods and the LISREL. After examining the differences among the factors of career success, a comparison was made between the disabled worker group and the non-disabled worker group. A questionnaire using the 5-point Likert scale was distributed to a group of 374 workers with disabilities and 463 workers without disabilities. For the data analysis purpose, the structural equation model, factor analysis, correlation analysis, and multiple regression analysis were carried out. The results of this study ran be summarized as follows. First, the results of factor analysis showed important categories of conceptual themes of career success. The initial conceptual factor model did not accord with the empirical one. A three-factorial model revealed categories of personal, family, and organizational factor respectively. The personal factor was composed of the self-esteem and self-efficiency. The family factor was consisted of the multi-roles stress and the number of children. Finally, the organizational factor was composed of the capacity for utilizing resources, networking, and the frequency of mentoring. In addition, the total 10 sub areas of career success were divided by two important aspects; the subjective career success and the objective career success. Second, both research participant groups seemed to be influenced by their occupational types. However, all predictive variables excluding the wage rate and the average length of work years had significant impact on job success for the disabled work group, while all the variables excluding the frequency of advice and length of working years had significant impact on job success for the non-disabled worker group. Third, the turnover rate was significantly influenced by the age and the experience of turnover of the research participants. However, the number of co-workers was the strongest predictive variable for the worker group with disabilities, but the occupation choice variable for the worker group without disabilities. For the disabled worker group, the turnover rate was differently influenced by the type of occupation, the length of working years, while multi-role stress and the average working years at the time of turnover for the worker group without disabilities. Fifth, as a result of verifying the hypothetical path model, it showed that the first model was somewhat proper and could predict the career success on both research participant groups. In the second model, the Chi-square, the degree of freedom (($x^2=64.950$, df=61, P=0.341), and the adjusted Goodness of Fit Index (AGFI) were .964, and the Comparative Fit Index (CFI) were .997, and the Root Mean Squared Residual (RMR) was respectively. .038. The model was best fitted and could predict the career success more highly because the goodness of fit index in the whole models was within the allowed range. In conclusion, the following research implications can be suggested. First, the occupational type of research participants was one of the most important variables to predict the career success for both research participant groups. It means that people with disabilities require human development services including education. They need to improve themselves in this knowledge-based society. Furthermore, for maintaining the career success, people with disabilities should be approached by considering the subjective career success aspects including wages and the promotion opportunities than the objective career success aspects.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.111-124
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• 2018

This study aims to develop a classification model for predicting the occurrence of hyperlipidemia, one of the chronic diseases. Prior studies applying data mining techniques for predicting disease can be classified into a model design study for predicting cardiovascular disease and a study comparing disease prediction research results. In the case of foreign literatures, studies predicting cardiovascular disease were predominant in predicting disease using data mining techniques. Although domestic studies were not much different from those of foreign countries, studies focusing on hypertension and diabetes were mainly conducted. Since hypertension and diabetes as well as chronic diseases, hyperlipidemia, are also of high importance, this study selected hyperlipidemia as the disease to be analyzed. We also developed a model for predicting hyperlipidemia using SVM and meta learning algorithms, which are already known to have excellent predictive power. In order to achieve the purpose of this study, we used data set from Korea Health Panel 2012. The Korean Health Panel produces basic data on the level of health expenditure, health level and health behavior, and has conducted an annual survey since 2008. In this study, 1,088 patients with hyperlipidemia were randomly selected from the hospitalized, outpatient, emergency, and chronic disease data of the Korean Health Panel in 2012, and 1,088 nonpatients were also randomly extracted. A total of 2,176 people were selected for the study. Three methods were used to select input variables for predicting hyperlipidemia. First, stepwise method was performed using logistic regression. Among the 17 variables, the categorical variables(except for length of smoking) are expressed as dummy variables, which are assumed to be separate variables on the basis of the reference group, and these variables were analyzed. Six variables (age, BMI, education level, marital status, smoking status, gender) excluding income level and smoking period were selected based on significance level 0.1. Second, C4.5 as a decision tree algorithm is used. The significant input variables were age, smoking status, and education level. Finally, C4.5 as a decision tree algorithm is used. In SVM, the input variables selected by genetic algorithms consisted of 6 variables such as age, marital status, education level, economic activity, smoking period, and physical activity status, and the input variables selected by genetic algorithms in artificial neural network consist of 3 variables such as age, marital status, and education level. Based on the selected parameters, we compared SVM, meta learning algorithm and other prediction models for hyperlipidemia patients, and compared the classification performances using TP rate and precision. The main results of the analysis are as follows. First, the accuracy of the SVM was 88.4% and the accuracy of the artificial neural network was 86.7%. Second, the accuracy of classification models using the selected input variables through stepwise method was slightly higher than that of classification models using the whole variables. Third, the precision of artificial neural network was higher than that of SVM when only three variables as input variables were selected by decision trees. As a result of classification models based on the input variables selected through the genetic algorithm, classification accuracy of SVM was 88.5% and that of artificial neural network was 87.9%. Finally, this study indicated that stacking as the meta learning algorithm proposed in this study, has the best performance when it uses the predicted outputs of SVM and MLP as input variables of SVM, which is a meta classifier. The purpose of this study was to predict hyperlipidemia, one of the representative chronic diseases. To do this, we used SVM and meta-learning algorithms, which is known to have high accuracy. As a result, the accuracy of classification of hyperlipidemia in the stacking as a meta learner was higher than other meta-learning algorithms. However, the predictive performance of the meta-learning algorithm proposed in this study is the same as that of SVM with the best performance (88.6%) among the single models. The limitations of this study are as follows. First, various variable selection methods were tried, but most variables used in the study were categorical dummy variables. In the case with a large number of categorical variables, the results may be different if continuous variables are used because the model can be better suited to categorical variables such as decision trees than general models such as neural networks. Despite these limitations, this study has significance in predicting hyperlipidemia with hybrid models such as met learning algorithms which have not been studied previously. It can be said that the result of improving the model accuracy by applying various variable selection techniques is meaningful. In addition, it is expected that our proposed model will be effective for the prevention and management of hyperlipidemia.

• Korean Journal of Medicinal Crop Science
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• v.2 no.1
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• pp.7-13
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• 1994

This study was conducted to investigate the relationships between yearly variations of climatic factors and yearly variations of productivity in Alisma plantago L. In addition, correlation coefficients among yield and yield components were estimated. The data of yield and yield components were collected from the Statistical Year Book of Seungju province, Reserach Report of Seungju Extension Station of Rural Development Administration, and farmers for 10 years from 1983 to 1992. The meteorological data gathered at the Seungju Weather Station for the same period were used to find out the relationships between climatic factors and productivity. Yearly variation of the amount of precipitation in October and the minimum temperature in November were large with coefficients of variation(C.V.) of 106.44, 144.08%, respectively, but the variation of the average temperature, maximum temperature, minimum temperature from July to September were relatively small. Fresh weight and dry weight of roots vary greatly with C. V. of 30.62, 31.85%, respectivly. Plant height and stem length show more or less small C. V. of 5.51, 6. 26%, respectively and leaf width, leaf length, number of stems and root diameter show still less variation. Correlation coefficients between maximum temperature in November and plant height, stem diamter, number of stems, root diamter and dry weight of roots are positively significant at the 5% level. There are high signficant positive correlations observed, between yield and yield components. The maximum temperature would be used as a predictive variable for the estimation of dry weight of roots and number of stems. Simple linear regression equations by the least square method are estimated for number of stems $(Y_1)$ and the maximum temperature in November(X) as $Y_1=4.7114+0.5333\;X\;(R^2=0.4410)$, and for dry weight of roots$(Y_2)$ and the maximum temperature in November(X) as $Y_2=55.0405+14.3233\;X\;(R^2=0.4511)$

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.2
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• pp.138-148
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• 2001

Digital Video (DV) coding standards for digital video cassette recorder are based mainly on DCT and variable length coding. DV has low hardware complexity but high compressed bit rate of about 26 Mb/s. Thus, it is necessary to encode video with low complex video coding at the studios and then transcode compressed video into MPEG-2 for video-on-demand system. Because these coding methods exploit DCT, transcoding in the DCT domain can reduce computational complexity by excluding duplicated procedures. In transcoding DV into MPEC-2 intra coding, multiplying matrix by transformed data is used for 4:1:1-to-4:2:2 chroma format conversion and the conversion from 2-4-8 to 8-8 DCT mode, and therefore enables parallel processing. Variance of sub block for MPEG-2 rate control is computed completely in the DCT domain. These are verified through experiments. We estimate motion hierarchically using DCT coefficients for transcoding into MPEG-2 inter coding. First, we estimate motion of a macro block (MB) only with 4 DC values of 4 sub blocks and then estimate motion with 16-point MB using IDCT of 2$\times$2 low frequencies in each sub block, and finish estimation at a sub pixel as the fifth step. ME with overlapped search range shows better PSNR performance than ME without overlapping.

• Asian-Australasian Journal of Animal Sciences
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• v.29 no.8
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• pp.1145-1151
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• 2016

This study was conducted to evaluate the relative bioavailability (RBV) of 25-hydroxycholecalciferol (25-OH-$D_3$) to cholecalciferol (vitamin $D_3$) in 1- to 21-d-old broiler chickens fed with calcium (Ca)- and phosphorus (P)-deficient diets. On the day of hatch, 450 female Ross 308 broiler chickens were assigned to nine treatments, with five replicates of ten birds each. The basal diet contained 0.50% Ca and 0.25% non-phytate phosphorus (NPP) and was not supplemented with vitamin D. Vitamin $D_3$ was fed at 0, 2.5, 5.0, 10.0, and $20.0{\mu}g/kg$, and 25-OH-$D_3$ was fed at 1.25, 2.5, 5.0, and $10.0{\mu}g/kg$. The RBV of 25-OH-$D_3$ was determined using vitamin $D_3$ as the standard source by the slope ratio method. Vitamin $D_3$ and 25-OH-$D_3$ intake was used as the independent variable for regression analysis. The linear relationships between the level of vitamin $D_3$ or 25-OH-$D_3$ and body weight gain (BWG) and the weight, length, ash weight, and the percentage of ash, Ca, and P in femur, tibia, and metatarsus of broiler chickens were observed. Using BWG as the criterion, the RBV value of 25-OH-$D_3$ to vitamin $D_3$ was 1.85. Using the mineralization of the femur, tibia, and metatarsus as criteria, the RBV of 25-OH-$D_3$ to vitamin $D_3$ ranged from 1.82 to 2.45, 1.86 to 2.52, and 1.65 to 2.05, respectively. These data indicate that 25-OH-$D_3$ is approximately 2.03 times as active as vitamin $D_3$ in promoting growth performance and bone mineralization in broiler chicken diets.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2002.09a
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• pp.314-317
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• 2002

The purpose of this study was to evaluate the clinical application of Patlak tool on GE PET workstation for quantitative analysis of dynamic PET images in cardiac patients. Three patients including coronary artery disease (CAD), myocardial infarction (MI), and angina were studied. All subjects underwent dynamic cardiac PET scan using a GE Advance scanner. After 10 min transmission scan for attenuation correction using two rotating $\^$68/Ge rod sources, three patients with cardiac disease were performed dynamic cardiac PET scan after the administration of approximately 370 MBq of FDG. The dynamic scan consisted of 36 frames with variable frame length (12${\times}$10s, 6${\times}$20s, 6${\times}$60s, 12${\times}$300s) for a total time of 70 min. Blood samples were obtained to determine the plasma substrate concentration. Region of interest of circular and rectangular shape to acquire input functions and tissue data were placed on left ventricle and myocardium. A value of 0.67 was used for lumped constant. Mean plasma substrate concentrations for three patients were 100 mg/dl (CAD), 100 mg/dl (MI), 132 mg/dl (angina), respectively. Regional MMRGlc values (mean${\pm}$SD) at lateral myocardium area for CAD, MI, and angina were 8.43${\pm}$0.24, 4.08${\pm}$0.16, and 6.15${\pm}$0.23 mg/min/100ml, respectively. Patlak tool on GE PET workstation appeared to be useful for quantitative analysis of dynamic PET images in cardiac patients, although further studies may be required for absolute quantitation.

• Journal of Preventive Medicine and Public Health
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• v.29 no.2 s.53
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• pp.311-329
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• 1996

This study was designed to compare the level of medical utilization between the urban and rural areas of Korea and to explain the differences between the two regions. Data from the National Health Interview Survey performed by the Korean Institute of Health & Social Affairs in 1992 were used for this study utilizing a sample size of 21,841 people. The level of medical utilization such as the number of physician visits and the number of hospital admissions was compared between the regions with ANOVA. Various determinants for medical use were also compared by univariate analysis. Statistical models which included enabling factors, predisposing factors, need factors and region were constructed for bivariate analysis in order to further elucidate the level of medical utilization. The results were as follows: 1. There was greater medical use, both in terms of physician visits and inpatient care in the rural areas in spite of insufficient health resources. The particular reasons for higher medical utilization in rural areas were attributed to a higher number of initial physician visits as well as a longer the length of stay per hospital admission. Therefore, indicators representing the degree of met need (utilization/need) showed no significant difference between rural and urban areas in spite of the fact that the medical need is larger in rural areas. 2. Use of public health facilities received a significant portion of physician visits in the rural area. The government's effort to enhance primary health care through health centers, health subcenters and the nurse practitioner's post in rural areas has contributed to the increase of access to medical care in the rural areas. 3. There were some differences in the socio-demographic characteristics between two regions ; There were more elderly people over the age of 65: unstable marital status, less education and lower incomes also characterized the rural areas. Therefore, among rural people, there were more predisposing factors for medical use. Additionaly, need factors such as poor self-reported health status and high morbidity level were also high in the rural area. 4. In contrast it was learned that, the supply of health resources was mostly concentrated in the urban areas except for public health facilities. Therefore, geographical access to medical care was lower in the rural area both in terms travel time and travel cost. 5. The coefficient of the region variable was insignificant in the regression model which controlled the supply factor only. However, utilization was significantly higher in urban areas if the model included predisposing factors and need factors in addition to the supply factor. The results were interpreted as rural people have greater medical needs.