• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.8
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• pp.277-286
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• 2020

This study aimed to examine bone health awareness and knowledge and the bone mass-improving behaviors of female nursing college students. The subjects were 172 nursing students attending nursing colleges. The data were collected from March 16 to April 4, 2020, by using bone health awareness, bone health knowledge, and bone mass-promoting behavior assessment tools. Descriptive statistics are presented, and t-tests, ANOVA, Pearson's correlation, and multi-regressions were used for data analysis. Students' bone health awareness was 1.79, bone health knowledge was 8.86, and bone mass-promoting behavior level was 2.78. There were significant negative correlations between bone mass-promoting behavior level and age of menarche (r = 0.21, p = .004) and sun exposure (r = 0.44, p < .000). Also, bone mass-promoting behavior level and knowledge of bone health were negatively correlated (r = 0.21, p = .005). Regression analysis showed that knowledge of bone health (β = 0.21, p = .005), age of menarche (β = 0.20, p = .005), and sun exposure (β = 0.38, p < .000) were significant predictors of bone mass-promoting behaviors and their variance explanation power was 20.6%. Based on these results, education to improve knowledge of bone health will help to improve bone health and increase bone mass-promoting behaviors among young women.

• Korean Journal of Community Nutrition
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• v.1 no.2
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• pp.239-249
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• 1996

Despite the importance of prenatal nutrition, many studies find inadequate calcium intake among pregnant women. The purpose of this study was to investigate the value of the Theory of Planned Behavior in explaining the intentions and the actual consumption of milk among pregnant women participating in or eligible for WIC. A cross-sectional survey was conducted to collect information regarding attitudes, subjective norms, perceived control, milk allocation within the family, intentions and consumption of milk. The survey questionnaire was developed using open-ended questions and interviews with 112 pregnant women. One-hundred-eighty women recruited from prenatal clinics completed the survey questionnaire. Multiple regression was used separately to investigate the association of factors to intentions and to the consu-mption of milk, as proposed in the theory. Milk allocation within the family was used as an exploratory variable to explain milk consumption. Study findings revealed that all three factors, attitudes, subjective norms and perceived control contributed to the model in explaining intentions (explained variance : 36.2%), with perceived control being most important. For milk consumption, intentions and perceived control were related significantly to actual consumption, while milk allocation within the family was not (explained variance : 44.6%). These findings suggest that perceived control is important in understanding both intentions and milk consumption, providing empirical evidence for the Theory of Planned Behavior. With respect to the role of perceived control, more strong evidence was provided in explaining intentions. Findings suggest that educational interventions to increase milk consumption among pregnant women should incorporate strategies to enhance the perception of control, and to strengthen positive attitudes and to elicit social support from significant other. (Korean J Community Nutrition 1(2) 239-249, 1996)

• Journal of Cadastre & Land InformatiX
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• v.48 no.2
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• pp.197-211
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• 2018

We conducted this research to examine how well regional housing prices are suited to use as an alternative to conventional census-based regional deprivation indices in health and medical geography studies. To examine the relative performance of mean regional housing prices compared to conventional census-based regional deprivation indices, we compared several multilevel logistic regression models, where the first level was individuals and the second was health districts in the Seoul Metropolitan Area (SMA) in Korea, for the sake of adjusting the regional clustering tendency of unknown factors. In these models, we predicted two dichotomous variables that represented individuals' after-lunch tooth brushing behavior and use of dental floss by individual characteristics and regional indices. Then, we compared the relative predictive performance of the models using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The results from the estimations showed that mean regional housing prices and census-based deprivation indices were correlated with the two types of dental health behavior in a statistical sense. The results also revealed that the model with mean regional housing prices showed smaller AIC and BIC compared with other models with conventional census-based deprivation indices. These results imply that it is possible for housing prices summarized using aerial units to be used as an alternative to conventional census-based deprivation indices when the census variables employed cannot properly reflect the characteristics of the aerial units.

• International Journal of Highway Engineering
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• v.13 no.1
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• pp.69-77
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• 2011

This study is to suggest a methodology to overcome the uncertainty and lack of reliability of data. The fuzzy reasoning model and the neural network model were developed in order to overcome the potential lack of reliability which may occur during the process of data collection. According to the result of comparison with the Poisson regression model, the suggested models showed better performance in the accuracy of the accident frequency prediction. It means that the more accurate accident frequency prediction model can be developed by the process of the uncertainty of raw data and the adjustment of errors in data by learning. Among the suggested models, the performance of the neural network model was better than that of the fuzzy reasoning model. The suggested models can evaluate the safety of signalized intersections in operation and/or planning, and ultimately contribute the reduction of accidents.

• Journal of Korean Society of Forest Science
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• v.112 no.2
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• pp.209-216
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• 2023

This study aimed to use national forest inventory data to develop a forest productivity index and yield prediction model of a Tilia amurensis stand. The site index displaying the forest productivity of the Tilia amurensis stand was developed as a Schumacher model, and the site index classification curve was generated from the model results; its distribution growth in Korea ranged from 8-16. The growth model using age as an independent variable for breast height and height diameter estimation was derived from the Chapman-Richards and Weibull model. The Fitness Indices of the estimation models were 0.32 and 0.11, respectively, which were generally low values, but the estimation-equation residuals were evenly distributed around 0, so we judged that there would be no issue in applying the equation. The stand basal area and site index of the Tilia amurensis stand had the greatest effect on the stand-volume change. These two factors were used to derive the Tilia amurensis stand yield model, and the model's determination coefficient was approximately 94%. After verifying the residual normality of the equation and autocorrelation of the growth factors in the yield model, no particular problems were observed. Finally, the growth and yield models of the Tilia amurensis stand were used to produce the makeshift stand yield table. According to this table, when the Tilia amurensis stand is 70 years old, the estimated stand-volume per hectare would be approximately 208 m³ . It is expected that these study results will be helpful for decision-making of Tilia amurensis stands management, which have high value as a forest resource for honey and timber.

• Journal of Korean Society of Transportation
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• v.33 no.2
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• pp.145-158
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• 2015

A network connectivity has been regarded as a key element to strengthen a business competitive power in the aviation industry, so many airport authorities try to attract the new airlines and scheme out new air routes. With this trend, a study for an induced travel demand estimation methodology is needed. This study introduces a demand estimation method, especially for a new air route to a promising destination. With the results of previous studies, the derived demand is classified into four types - Local, Beyond, Behind and Bridge. The explanatory variables are established for each type of demand and the main independent variables are composed of distance, ratio of detour, and relative capacity compared with other airports. The equations using such variables and statistically significant coefficients are suggested as the model to make an estimation of derived demand for a new route. Therefore this study will be expected to take an initial step for all related parties to be involved more deeply into developing new air routes to enhance network connectivity.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.3D
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• pp.435-444
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• 2006

This paper investigates the effect of 3-dimensional FE models to evaluation results of jointed concrete pavements which is back-calculated by AREA method. Sensitivity of 3-dimensional FE models developed to simulate the behavior of real jointed concrete pavement are analyzed after compared with 2-dimensional FE models using ILLISLAB. In comparison with 2-dimensional models, influence of concrete contraction under loading plate and base layer on surface deflections is more than that of loading configuration. Deflections at 3-dimensional model between linear and nonlinear temperature distribution under same temperature difference are similar, but noticeable differences are investigated in low elastic modulus of foundations. Dynamic deflections under loading plate are larger than static deflections in high elastic modulus of foundation, but smaller in low elastic modulus. Lower dynamic modulus of subgrade reactions are backcalculated by dynamic deflections than by static deflections. But reverse trend is investigated in the backcalculated elastic modulus of concrete which describes trends of the field backcalculation values calculated from AREA method.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.69-88
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• 2022

One of the characteristics of South Korea's economic structure is that it is highly dependent on exports. Thus, many businesses are closely related to the global economy and diplomatic situation. In addition, small and medium-sized enterprises(SMEs) specialized in exporting are struggling due to the spread of COVID-19. Therefore, this study aimed to develop a model to forecast exports for next year to support SMEs' export strategy and decision making. Also, this study proposed a strategy to recommend promising export countries of each item based on the forecasting model. We analyzed important variables used in previous studies such as country-specific, item-specific, and macro-economic variables and collected those variables to train our prediction model. Next, through the exploratory data analysis(EDA) it was found that exports, which is a target variable, have a highly skewed distribution. To deal with this issue and improve predictive performance, we suggest a separated learning method. In a separated learning method, the whole dataset is divided into homogeneous subgroups and a prediction algorithm is applied to each group. Thus, characteristics of each group can be more precisely trained using different input variables and algorithms. In this study, we divided the dataset into five subgroups based on the exports to decrease skewness of the target variable. After the separation, we found that each group has different characteristics in countries and goods. For example, In Group 1, most of the exporting countries are developing countries and the majority of exporting goods are low value products such as glass and prints. On the other hand, major exporting countries of South Korea such as China, USA, and Vietnam are included in Group 4 and Group 5 and most exporting goods in these groups are high value products. Then we used LightGBM(LGBM) and Exponential Moving Average(EMA) for prediction. Considering the characteristics of each group, models were built using LGBM for Group 1 to 4 and EMA for Group 5. To evaluate the performance of the model, we compare different model structures and algorithms. As a result, it was found that the separated learning model had best performance compared to other models. After the model was built, we also provided variable importance of each group using SHAP-value to add explainability of our model. Based on the prediction model, we proposed a second-stage recommendation strategy for potential export countries. In the first phase, BCG matrix was used to find Star and Question Mark markets that are expected to grow rapidly. In the second phase, we calculated scores for each country and recommendations were made according to ranking. Using this recommendation framework, potential export countries were selected and information about those countries for each item was presented. There are several implications of this study. First of all, most of the preceding studies have conducted research on the specific situation or country. However, this study use various variables and develops a machine learning model for a wide range of countries and items. Second, as to our knowledge, it is the first attempt to adopt a separated learning method for exports prediction. By separating the dataset into 5 homogeneous subgroups, we could enhance the predictive performance of the model. Also, more detailed explanation of models by group is provided using SHAP values. Lastly, this study has several practical implications. There are some platforms which serve trade information including KOTRA, but most of them are based on past data. Therefore, it is not easy for companies to predict future trends. By utilizing the model and recommendation strategy in this research, trade related services in each platform can be improved so that companies including SMEs can fully utilize the service when making strategies and decisions for exports.

• Journal of Navigation and Port Research
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• v.43 no.6
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• pp.335-343
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• 2019

As the regulations of ship exhaust gas have been strengthened recently, many measures are under consideration to reduce fuel consumption. Among them, research has been performed actively to develop a machine-learning model that predicts fuel consumption by using data collected from ships. However, many studies have not considered the methodology of the main parameter selection for the model or the processing of the collected data sufficiently, and the reckless use of data may cause problems such as multicollinearity between variables. In this study, we propose a method to predict the fuel consumption of the ship by using the principal component analysis to solve these problems. The principal component analysis was performed on the operational data of the 13K TEU container ship and the fuel consumption prediction model was implemented by regression analysis with extracted components. As the R-squared value of the model for the test data was 82.99%, this model would be expected to support the decision-making of operators in the voyage planning and contribute to the monitoring of energy-efficient operation of ships during voyages.

• Spatial Information Research
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• v.10 no.2
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• pp.215-232
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• 2002

Variables representing neighborhood quality should be included in hedonic price models to control lfor the influences of negative or positive externalities from the quality of neighborhood on urban housing prices. This study proposes a GIS-based method to effectively measure the neighborhood quality variable when data on the neighborhood quality are aggregated by census sub area. This study also tests the superiority of the proposed neighborhood quality variable created by intensive use of GIS operations to a neighborhood variable not based on GIS operations in explaining the housing price variations by using Seoul＇s apartment sales data. The results from this study show that the neighborhood quality variable based on GIS-based operations shows better performance in explaining the urban housing price variations in Seoul＇s housing market. The implication from the results is that the potentials of GIS-based spatial operations in creating neighborhood quality variables should be well acknowledged by the researchers in the area of urban housing market study and GIS-based spatial operations should be more actively applied to generate better neighborhood quality variables for hedonic price models.