• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.9
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• pp.6120-6129
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• 2015

The purpose of this study was to investigate the factors affecting the health promoting behaviors(HPB) of health-related and health-unrelated department university students. Data were collected by questionnaires from 189 health-related and 204 health-unrelated department university students. Data were analyzed using descriptive statistics, Pearson's correlation coefficient, and multiple regression. The quality of life and self-esteem showed a significantly positive correlation with HPB, but life stress showed a significantly negative correlation with HPB. The result of multiple regression analysis showed that three variables affected the HPB significantly(p<.001) and made a 27% prediction for health-related department university students and 34% for health-unrelated department university students. It is necessary to investigate the various influencing factors on HPB of university students and research the difference of HPB of health-related and health-unrelated department university students.

• Journal of the Korean Society of Hazard Mitigation
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• v.11 no.2
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• pp.59-66
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• 2011

In this study, the vibration serviceability of pedestrian by travelling vehicles on the cable-stayed bridge with concrete tower was studied. Experiment variables were considered travelling speed of vehicles, pavement state of asphalt on the deck and weight of vehicles, preferentially. Especially, pavement grade states were considered by A and C grades by BMS (Bridge Management System) standard. The incremental ratio extent of vibration acceleration responses, asphalt pavement grade C over A, was construed to 1.23~1.43. Only, these results are valid within extent of the Scaled-Weight 228.0~1161.9 km/h kN. The vibration equations for acceleration responses prediction of bridge deck were proposed into three types, reliability 50%, 90%, 95% respectively. These equations can consider asphalt pavement grade, and the vehicle's weight and travelling velocity, which are the source of vibration, are combined into the term called, 'Scaled Weight'.

• Journal of the Korea Convergence Society
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• v.10 no.3
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• pp.23-30
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• 2019

The purpose of this study is to find out the structure of the substance that affects antidiabetic using the candidate drug information for diabetes treatment. A quantitative structure activity relationship model based on machine learning method was constructed and major molecular descriptors were determined for each experimental data variables from coefficient values using a partial least squares algorithm. The results of the analysis of the molecular access system fingerprint data reflecting the candidate drug structure information were higher than those of the in vitro data analysis in terms of goodness-of-fit, and the major molecular expression factors affecting the antidiabetic effect were also variously derived. If the proposed method is applied to the new drug development environment, it is possible to reduce the cost for conducting candidate screening experiment and to shorten the search time for new drug development.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.23 no.6
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• pp.125-135
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• 2020

The uncontrolled urban expansion causes various social, economic problems and natural/environmental problems. Therefore, it is necessary to forecast urban expansion by identifying various factors related to urban expansion. This study aims to forecast it using a decision tree that is widely used in various areas. The study used geographic data such as the area of use, geographical data like elevation and slope, the environmental conservation value assessment map, and population density data for 2006 and 2018. It extracted the new urban expansion areas by comparing the residential, industrial, and commercial zones of the zoning in 2006 and 2018 and derived a decision tree using the 2006 data as independent variables. It is intended to forecast urban expansion in 2030 by applying the data for 2018 to the derived decision tree. The analysis result confirmed that the distance from the green area, the elevation, the grade of the environmental conservation value assessment map, and the distance from the industrial area were important factors in forecasting the urban area expansion. The AUC of 0.95051 showed excellent explanatory power in the ROC analysis performed to verify the accuracy. However, the forecast of the urban area expansion for 2018 using the decision tree was 15,459.98㎢, which was significantly different from the actual urban area of 4,144.93㎢ for 2018. Since many regions use decision tree to forecast urban expansion, they can be useful for identifying which factors affect urban expansion, although they are not suitable for forecasting the expansion of urban region in detail. Identifying such important factors for urban expansion is expected to provide information that can be used in future land, urban, and environmental planning.

• The Journal of Information Systems
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• v.30 no.2
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• pp.147-163
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• 2021

Purpose The study aims to predict real estate prices by utilizing regional characteristics. Since real estate has the characteristic of immobility, the characteristics of a region have a great influence on the price of real estate. In addition, real estate prices are closely related to economic development and are a major concern for policy makers and investors. Accurate house price forecasting is necessary to prepare for the impact of house price fluctuations. To improve the performance of our predictive models, we applied LSTM, a widely used deep learning technique for predicting time series data. Design/methodology/approach This study used time series data on real estate prices provided by the Ministry of Land, Infrastructure and Transport. For time series data preprocessing, HP filters were applied to decompose trends and SOM was used to cluster regions with similar price directions. To build a real estate price prediction model, SVR and LSTM were applied, and the prices of regions classified into similar clusters by SOM were used as input variables. Findings The clustering results showed that the region of the same cluster was geographically close, and it was possible to confirm the characteristics of being classified as the same cluster even if there was a price level and a similar industry group. As a result of predicting real estate prices in 1, 2, and 3 months, LSTM showed better predictive performance than SVR, and LSTM showed better predictive performance in long-term forecasting 3 months later than in 1-month short-term forecasting.

• Journal of radiological science and technology
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• v.45 no.6
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• pp.491-502
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• 2022

The purpose of this study was to retrospectively investigate the upper and lower control limits of treatment planning parameters using EBT film based delivery quality assurance (DQA) results and to analyze the results of statistical process control (SPC) in helical tomotherapy (HT). A total of 152 patients who passed or failed DQA results were retrospectively included in this study. Prostate (n = 66), rectal (n = 51), and large-field cancer patients, including lymph nodes (n = 35), were randomly selected. The absolute point dose difference (DD) and global gamma passing rate (GPR) were analyzed for all patients. Control charts were used to evaluate the upper and lower control limits (UCL and LCL) for all the assessed treatment planning parameters. Treatment planning parameters such as gantry period, leaf open time (LOT), pitch, field width, actual and planning modulation factor, treatment time, couch speed, and couch travel were analyzed to provide the optimal range using the DQA results. The classification and regression tree (CART) was used to predict the relative importance of variables in the DQA results from various treatment planning parameters. We confirmed that the proportion of patients with an LOT below 100 ms in the failure group was relatively higher than that in the passing group. SPC can detect QA failure prior to over dosimetric QA tolerance levels. The acceptable tolerance range of each planning parameter may assist in the prediction of DQA failures using the SPC tool in the future.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.5
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• pp.611-617
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• 2010

A complex system involves a large number of design variables, and its operation is non-linear. To explore the characteristics in its design space, a Kriging meta-model can be utilized; this model has replaced expensive computational analysis that was performed in traditional parametric design optimization. In this study, a Kriging meta-model with a computational orthogonal array for the design of experiments was developed to optimize the fatigue life of a turbine blade whose behavior under cyclic rotational loads is significantly non-linear. The results not only show that the maximum fatigue life is improved but also indicate that the accuracy of computational analysis is achieved. In addition, the robustness of the results obtained by six-sigma optimization can be verified by comparison with the results obtained by performing Monte Carlo simulations.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.36 no.2
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• pp.97-106
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• 1991

Two dimensional non-parametric model using daily mean temperature and daylength as predictor variables was established and daily developmental rates (DVR) for the period of seedling emergence to heading were estimated for 26 rice cultivars by using data from field direct seeding dates and short-day treatments experiment carried out at experimental farm of Seoul National University in 1990. Three existing parametric models were tested for the comparision of predictability with non-parametric model. The non-parametric model was found to be superior to parametric models in predicting heading date. The developmetal indice(DVI) at heading date, cummulative DVR's from seedling emergence showed 0.5 to 2.2 percent of coefficient of variations. The non-parametric model revealed errors of 0 to three days in 11 varieties when applied to data independent of those used in estimating DVR.

• Information Systems Review
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• v.7 no.2
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• pp.23-40
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• 2005

Discovering customers' behavioral patterns from large data set and providing them with corresponding services or products are critical components in managing a current business. However, the diversity of customer needs coupled with the limited resources suggests that companies should make more efforts on understanding and managing specific groups of customers, not the whole customers. The key issue of this paper is based on the fact that the behavioral patterns extracted from the specific groups of customers shall be different from those from the whole customers. This paper proposes the idea of pre-segmentation before developing customer classification models. We collected three customers' demographic and transactional data sets from a credit card, a tele-communication, and an insurance company in Korea, and then segmented customers by major variables. Different churn prediction models were developed from each segments and the whole data set, respectively, using the decision tree induction approach, and compared in terms of the hit ratio and the simplicity of generated rules.

• Journal of the Korean Society of Hazard Mitigation
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• v.2 no.2 s.5
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• pp.95-104
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• 2002

This paper presents the pull-out failure mode on Headed Bars and prediction of tensile capacity, as governed by concrete cone failure. 17 different plate types, three different concrete strengths and three different welding types of specimens were simulated. Test variables are the reinforcing bar diameters connected to headed plate (e.g., 16mm, 19mm and 22mm), the head plate shapes (e.g., circular, square, rectangular), the dimensions of head plates (e.g., area and thickness), the types of welding scheme for connection of reinforcing bars and head plates (e.g., general welding and friction welding). Headed Bars were manufactured in different areas, which shape and thickness are based on ASTM 970-98. Calculation of Embedment length in concrete is based on CSA 23.3-94, and static tensile load was applied. Pullout capacities tested were compared to the values determined using current design methods such as ACI-349 and CCD method. If compare experiment results and existings, Headed bar expressed high strength and bigger breakdown radious than standard by wide plate area and anomaly reinforcing rod unlike anchor.