• Journal of Korean Society of Transportation
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• v.22 no.3 s.74
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• pp.109-125
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• 2004

In recent decades, microscopic simulation models have become powerful tools to analyze traffic flow on highways and to assist the investigation of level of service. The existing microscopic simulation models simulate an individual vehicle's speed based on a constant free-flow speed dominantly specified by users and driver's behavior models reflecting vehicle interactions, such as car following and lane changing. They set a single free-flow speed for a single vehicle on a given link and neglect to consider the effects of highway design elements to it in their internal simulation. Due to this, the existing models are limitted to provide with identical simulation results on both curved and tangent sections of highways. This paper presents a model developed to estimate the change of free-flow speeds based on highway design elements. Nine neural network models were trained based on the field data collected from seven different freeway curve sections and three different locations at each section to capture the percent changes of free-flow speeds: 100 m upstream of the point of curve (PC) and the middle of the curve. The model employing seven highway design elements as its input variables was selected as the best : radius of curve, length of curve, superelevation, the number of lanes, grade variations, and the approaching free-flow speed on 100 m upstream of PC. Tests showed that the free-flow speeds estimated by the proposed model were statistically identical to the ones from the field at 95% confidence level at each three different locations described above. The root mean square errors at the starting and the middle of curve section were 6.68 and 10.06, and the R-squares at these points were 0.77 and 0.65, respectively. It was concluded from the study that the proposed model would be one of the potential tools introducing the effects of highway design elements to free-flow speeds in simulation.

• Spatial Information Research
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• v.18 no.4
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• pp.1-11
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• 2010

Unlike the previous studies which have focused on 2-dimensional urban characteristics, this paper presents statistical models explaining urban heat island(UHI) effect by 3-dimensional urban morphologic information and addresses its policy implications. 3~dimensional informations of Columbus, Ohio arc captured from LiDAR data and building boundary informations are extracted from a building digital map, Finally NDV[ and temperature data are calculated by manipulating band 3, band 4, and thermal hand of LandSat images. Through complicated data processing, 6 independent variables(building surface area, building volume, height to width ratio, porosity, plan surface area) are introduced in simple and multiple linear regression models. The regression models are specified by Box-Tidwell method, finding the power to which the independent variable needs to raised to be in a linearity. Porosity, NDVI, and building surface area are carefully chosen as explanatory variables in the final multiple regression model, which explaining about 57% of the variability in temperatures. On reducing UHI, various implications of the results give guidelines to policy-making in open space, roof garden, and vertical garden management.

• Journal of the Korean Data and Information Science Society
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• v.26 no.1
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• pp.65-75
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• 2015

This paper analyzes the determinants of wage for the college and university graduates utilizing both individual-level and industry-level variables. We note that wage determination has multi-level structure in the sense that individual wage is influenced by individual-level variables (level-1) and industry-level (level-2) variables. Then, the assumption that individual wage is independent in the classical regression is violated. Therefore, this paper utilizes the hierarchical linear model (HLM). The major results are the followings. First, the multiple correspondence analysis including level-1 and 2 variables reveals that both level 1 and level 2 variables affects individual wages judging from the fact that the values of level 1 and level 2 variables differ across the different level of individual wage groups. Second, the decision tree analysis including level-1 and 2 variables shows that the most influential variable in wage determination is industry-level wage and the next is industry-level working hour, ages and sex in the decling order in. This suggests that the utilization of the HLM is appropriate since the characteristics of industry is important in determining the individual wage. Third, it is shown that the HLM model is the best compared to the other models which do not take level-1 and level-2 variables simultaneously into account.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.5
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• pp.713-719
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• 2009

In this paper, we develope a water demand forecasting algorithm using AR(Auto-regressive) and MLP(Multi-layer perceptron). To show effectiveness of the proposed method, we analyzed characteristics of time-series data collected in "A" purification plant at Jeon-Buk province during 2007-2008, and then performed the proposed method with various input factors selected through various analyses. As noted in experimental results, the performance of three types model such as multi-regressive, AR(Auto-regressive), and AR+MLP(Auto-regressive + Multi-layer perceptron) show 5.1%, 3.8%, and 3.6% with respect to MAPE(Mean Absolute Percentage Error), respectively. Thus, it is noted that the proposed method can be used to predict short-term water demand for the efficient operation of a water purification plant.

• International Journal of Highway Engineering
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• v.6 no.4 s.22
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• pp.109-121
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• 2004

This study was performed to investigate the characteristics of drying shrinkage for concrete slabs as a project for Korean pavement design procedure. According to the volume-surface ratios and aggregate types, the experiments have been executed for 252 days. In order to simulate the volume-surface ratio of a real concrete pavement slab, three-layer epoxy coating and wrapping were used to prevent the evaporation at the part of specimen surfaces. As a result of preliminary test, coating and wrapping method was identified as reliable for three months. According to the volume-surface ratio, the drying shrinkage of the concrete specimen using sandstone was measured 1.32 to 1.8 times higher than that of the limestone specimen. Comparing to the measured drying shrinkage strains and established ACI and CEB-FIP model equations, it turned out that those model equations were underestimated. Finally, considering the age and volume-surface ratios, the prediction equations of the drying shrinkage of concrete specimen were proposed through a multiple nonlinear regression analysis.

• Tuberculosis and Respiratory Diseases
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• v.43 no.5
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• pp.755-762
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• 1996

Background : Continuous positive airway pressure(CPAP) is doubtlessly using as a medical treatment of choice for patients with obstructive sleep apnea (OSA) syndrome. CPAP is effective in OSA patients as a physical "pneumatic pressure splint" mechanism. We have done this study for two purposes, first to seek for the factors to determine the optimal CPAP titer, second to predict the minimal CPAP titer using the determined factors. Methods: We studied a 72 OSA patients who were treated with CPAP. All of them were studied by using a two nights polysomnographic rests in hospital. We compared the patients requiring CPAP over $10cmH_2O$ with those who required CPAP under 5cm $H_2O$ to determine the factors affecting the minimal CPAP titer. Results : The high CPAP group is characterized by a significantly higher body mass index(BMI), apnea index(AI) and apnea and hyponea index(AHI) and significantly lower lowest $SaO_2$. Regression analysis using the optimal four variables resulted in the following prediction equation for CPAP titer. CPAPtiter=8.382 + 0.064 ${\times}$ BMI + 0.077 ${\times}$ AI - 0.004 ${\times}$ AHI - 0.077 ${\times}$ lowest $SaO_2$ When this regression equation was applied to the 72 patients, the mean CPAP titer as predicted by the above equation was $7.80{\pm}2.96$ mmHg. Compared this value with actually determined CPAPtiter, $7.93{\pm}4.00$mmHg, there was no significant difference between the two values. Conclusion: Obesity, apnea severity and lowest Sa02 were strongly correlated with CPAP titer. Linear regression equation for CPAP titer using these indices predicted very closely the actually measured values in the sleep laboratory.

• Korean Journal of Psychosomatic Medicine
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• v.31 no.2
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• pp.155-164
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• 2023

Objectives : The aim of this study was to identify the factors affecting ideas of persecution in post-traumatic stress disorder (PTSD) patients who underwent Clinician-Administered PTSD Scale (CAPS) and Minnesota Multiphasic Personality Inventory-2 (MMPI-2). Methods : We retrospectively reviewed 116 patients who underwent CAPS and MMPI 2 between May 2013 and April 2020 at Veteran Health Service Medical Center. Based on the CAPS score, the patients were divided into the PTSD group (n=63, age: 58.16±17.84) and the trauma exposed without PTSD group (n=53, age: 67.34±12.05). After checking the correlation between Ideas of persecution, CAPS, and MMPI-2 scales, linear regression analysis was performed to identify the risk factors for clinically relevant symptoms. Results : The PTSD group showed significant differences in Schizophrenia, Ideas of persecution, Dysfunctional negative emotions, Aberrant Experiences, Psychoticism, Negative Emotionality/Neuroticism, Anxiety, Depression, and Anger scales compared to the trauma-exposed without PTSD group. When analyzing the correlation between Idea of persecution, CAPS and MMPI-2 scales, there was a strong association with most of the scales in MMPI-2 and Idea of persecution except Disconstraint. Multiple linear regression analysis performed in PTSD group identified that risk factors for Idea of persecution were Dysfunctional negative emotions and Anger scale. Conclusions : The PTSD group had increased idea of persecution compared to the trauma exposed without PTSD group. Dysfunctional negative emotions and anger may be risk factors for idea of persecution in trauma exposed population.

• Journal of the Korean Institute of Landscape Architecture
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• v.42 no.3
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• pp.1-12
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• 2014

Urban Green space takes charge of the important role for the physical activity and promotion of health to the residents. Therefore, this study is trying to examine the relationship between the various characteristics of green space and green space usage for physical activity and health promotion. A questionnaire survey was conducted to obtain the information about patterns of green space usage and perceived neighborhood environments for the residents living in Changwon-si, Gyeongsangnam-do(n=541). Geographic Information System(GIS) was used to construct spatial data about green space accessibility and physical neighborhood environments. A Multiple Linear Regression model was used to examine the association between the characteristics of green space and physical activity, perceived health status and BMI(Body Mass Index). The study results revealed that the residents' physical activities are positively and directly influenced by the number of available public parks and green spaces in the vicinity(${\leq}200m$). The frequency at which residents witness others exercising nearby or the perceived abundance of low-cost gym facilities also factor as positive influences. The closer to the park, the higher the number of parks and area of green spaces, the more comfortable the walk thereto and the denser the neighboring residential area distribution, the perceived health level was found to be the more positively influenced. Further, it was verified that BMI is correlated with the number of public parks and green spaces within 400 m of the resident's home as well as the safety of walkways, the density of neighboring residential areas, the ratio of road, and the density of crosswalk. The significant multiple regression models between the characteristics of green spaces and physical activities and perceived health level were extracted within the significance level of 10%. This study will contribute to provide better understanding the ways in which green space and neighborhood characteristics are associated with physical activity and health. The result of this research will be available in the landscape architecture plan aimed at improving the use of green space for physical activity and reducing obesity.

• Journal of Korean Ophthalmic Optics Society
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• v.15 no.3
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• pp.293-297
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• 2010

Purpose: The aim of the current study was to compare body mass index (BMI) with waist circumference (WC) as a predictor of elevated intraocular pressure (IOP). Methods: The subjects were consisted of 458 adults, aged 20 year or above, of one community in Kyunggi-do. Mean IOPs were stratified jointly for BMI and WC tertiles. Multi-variate linear regression analysis was also used to compare between BMI and WC. Results: Although any BMI tertiles were not associated with IOP within each tertile of WC, WC tertiles was significantly related to elevation of IOP within the third BMI tertile (${\geq}24.9kg/m^2$). After adjusting for age and sex, only WC showed significant association with IOP. In additional adjustment for lifestyle variables, both BMI and WC were significantly associated with elevation of IOP. However, the results showed the stronger association of IOP with WC than BMI, whether they were adjusted by age and sex or additionally lifestyle variables. Conclusions: These data showed that BMI and WC were positively associated with IOP. However, WC appeared to be a better indicator for higher IOP than BMI.

• Journal of the Korea Society of Computer and Information
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• v.27 no.3
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• pp.1-12
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• 2022

In the event of a maritime distress accident, rapid search and rescue operations using rescue assets are very important to ensure the safety and life of drowning person's at sea. In this paper, we analyzed the surface layer current in the northwest sea area of Ulleungdo by applying machine learning such as multiple linear regression, decision tree, support vector machine, vector autoregression, and LSTM to the meteorological information collected from the maritime observation buoy. And we predicted the drowning person's route at sea based on the predicted current direction and speed information by constructing each prediction model. Comparing the various machine learning models applied in this paper through the performance evaluation measures of MAE and RMSE, the LSTM model is the best. In addition, LSTM model showed superior performance compared to the other models in the view of the difference distance between the actual and predicted movement point of drowning person.