• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.151-151
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• 2018

The present study is aimed to quantifying the uncertainty in the general circulation model (GCM) selection and its impacts on hydrology studies in the basins. For this reason, 13 GCMs was selected among the 26 GCM models of the Fifth Assessment Report (AR5) scenarios. Then, the climate data and hydrologic data with two Representative Concentration Pathways (RCPs) of the best model (INMCM4) and worst model (HadGEM2-AO) were compared to understand the uncertainty associated with GCM models. In order to project the runoff, the Precipitation-Runoff Modelling System (PRMS) was driven to simulate daily river discharge by using daily precipitation, maximum and minimum temperature as inputs of this model. For simulating the discharge, the model has been calibrated and validated for daily data. Root mean square error (RMSE) and Nash-Sutcliffe Efficiency (NSE) were applied as evaluation criteria. Then parameters of the model were applied for the periods 2011-2040, and 2070-2099 to project the future discharge the five large basins of South Korea. Then, uncertainty caused by projected temperature, precipitation and runoff changes were compared in seasonal and annual time scale for two future periods and RCPs compared to the reference period (1976-2005). The findings of this study indicated that more caution will be needed for selecting the GCMs and using the results of the climate change analysis.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.280-280
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• 2016

특정 자료의 시간의 흐름에 따른 예측치를 추정하는 방법으로 AR Model 즉, 자기회귀모형이 많이 사용되고 있다. AR Model은 변수의 현재 값을 과거 값의 함수로 나타내게 되는데, 이런 시계열 분석 모델을 사용할 때 매개변수의 추정 과정이 필수적으로 요구된다. 일반적으로 매개변수를 추정하는 방법에는 확률적근사법(stochastic approximation), 최소제곱법(method of least square), 자기상관법(method of autocorrelation method), 최우도법(method of maximum likelihood) 등이 있다. AR Model에서 가장 많이 사용되는 최우도법은 표본크기가 충분히 클 때 가장 효율적인 방법으로 평가되지만 수치적으로 해를 구하는 과정이 복잡한 경우가 많으며, 해를 구하지 못하는 어려움이 따르기도 한다. 또한 표본 크기가 작을 때 일반적으로 잘 일치하지 않은 결과를 얻게 된다. 우리나라의 강우, 유량 등의 자료는 자료의 수가 적은 경우가 많기 때문에 최우도법을 통한 매개변수 추정 시 불확실성이 내재되어있지만 그것을 정량적으로 제시하는데 한계가 있다. 본 연구에서는 AR Model의 매개변수 추정 시 Bayesian 기법으로 매개변수의 사후분포(posterior distribution)를 제공하여 매개변수의 불확실성 구간을 정량적으로 표현하게 됨으로써, 시계열 분석을 통해 보다 신뢰성 있는 예측치를 얻을 수 있으리라 판단된다.

• Journal of Auto-vehicle Safety Association
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• v.10 no.4
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• pp.54-59
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• 2018

While the advanced trffic environment systems are developed recently, the traffic systems for transportation vulnerable are still under development and their social life are limited as well. In order to the secure their mobility rights, it had been required to set up the particular system for the traffic welfare. One of the significant items is the express bus operation for wheelchair users. Thus, the research of development and operation for express buses with wheelchair users was funded by the Korean government. Before the express bus development for wheelchair users based on the current bus model, this study set up the evaluation method for the bus rollover characteristics to ensure occupant safety using the finite element method. The partial bus model was developed corresponding to the full bus model response under rollover event and the evaluation method based on two model (full bus model and partial bus model) responses is planned to apply the model development of express bus modification for wheelchair users.

• Journal of Korea Planning Association
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• v.54 no.3
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• pp.106-118
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• 2019

In order to develop pedestrian navigation service that provides optimal pedestrian routes based on pedestrian satisfaction levels, it is required to develop a prediction model that can estimate a pedestrian's satisfaction level given a certain condition. Thus, the aim of the present study is to develop a pedestrian satisfaction prediction model based on three machine learning algorithms: Logistic Regression, Random Forest, and Artificial Neural Network models. The 2009, 2012, 2013, 2014, and 2015 Pedestrian Satisfaction Survey Data in Seoul, Korea are used to train and test the machine learning models. As a result, the Random Forest model shows the best prediction performance among the three (Accuracy: 0.798, Recall: 0.906, Precision: 0.842, F1 Score: 0.873, AUC: 0.795). The performance of Artificial Neural Network is the second (Accuracy: 0.773, Recall: 0.917, Precision: 0.811, F1 Score: 0.868, AUC: 0.738) and Logistic Regression model's performance follows the second (Accuracy: 0.764, Recall: 1.000, Precision: 0.764, F1 Score: 0.868, AUC: 0.575). The precision score of the Random Forest model implies that approximately 84.2% of pedestrians may be satisfied if they walk the areas, suggested by the Random Forest model.

• Journal of Korean Association for Spatial Structures
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• v.22 no.4
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• pp.89-98
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• 2022

The recent earthquake in Korea caused a lot of damage to reinforced concrete (RC) columns with non-seismic details. The nonlinear analysis enables predicting the hysteresis behavior of RC columns under earthquakes, but the analytical model used for the columns must be accurate and practical. This paper studied the nonlinear analysis models built into a commercial structural analysis program for the existing RC columns. The load-displacement relationships, maximum strength, initial stiffness, and energy dissipation predicted by the three analysis models were compared and analyzed. The results were similar to those tested in the order of the fiber, Pivot, and Takeda models, whereas the fiber model took the most time to build. For columns subjected to axial load, the Pivot model could predict the behavior at a similar level to that of the fiber model. Based on the above, it is expected that the Pivot model can be applied most practically for existing RC columns.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.117-117
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• 2020

The main objective of this study was to setup model and evaluate the model performance for streamflow simulation in Burundi using Soil and Water Assessment Tool (SWAT) model. The total area of Burundi is 27,834 ㎢. The elevation of Burundi ranges from 780 m to 2,700m. The West and East are low lands, while the Central part is high land. The topographic data (30 meters Digital Elevation Model) and land use and land cover data of Burundi were obtained respectively from Shuttle Radar Topography Mission (SRTM) and the Regional Centre for Mapping of Resources for Development (RCMRD). The soil data used was obtained from Food and Agriculture Organization (FAO). The local weather data and discharge data were provided by Burundi Hydro meteorological Service (IGEBU). Mean Areal Precipitation (MAP) and Mean Areal Temperature (MAT) were estimated. The streamflow simulation was done for the period 1980-2017. The calibration and validation of river discharge was performed at a daily time step from 2005 through 2011 as the calibration period and 2012 up to 2017 as the validation period. The findings show that streamflow decreases during Jun to September and increases during March to May and October to December.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.4
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• pp.1145-1149
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• 2012

Aim: To clarify any association between the hOGG1 Ser326Cys polymorphism and susceptibility to gastric cancer. Methods: A meta-analysis based on 11 eligible case-control studies involving 5,107 subjects was carried out to summarize the data on the association between hOGG1 Ser326Cys polymorphism and gastric cancer risk. Results: No association was found between hOGG1 Ser326Cys polymorphism and gastric cancer risk (dominant model: OR = 0.95, 95% CI: 0.83-1.09, p = 0.486, ph (p values for heterogeneity) = 0.419; additive model: OR = 1.02, 95% CI: 0.81-1.30, p = 0.850, ph = 0.181; recessive model: OR = 1.09, 95% CI: 0.80-1.48, p = 0.586, ph = 0.053). Subgroup analysis based on ethnicity (Asian and Caucasian) and smoking status (ever smoker and never smoker) did did notpresent any significant association. Sensitivity analysis did not perturb the results. Conclusions: This study strongly suggested there might be no association between the hOGG1 Ser326Cys polymorphism and gastric cancer risk. However, larger scale studies are needed for confirmation.

• Journal of Korea Water Resources Association
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• v.33 no.2
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• pp.159-167
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• 2000

The purpose of this study is to estimate the stochastic monthly runoff model for the Kunwi south station of Wi-stream basin in Nakdong river system. This model was based on the theory of Box-Jenkins multiplicative ARlMA and the state-space model to simulate changes of monthly runoff. The forecasting monthly runoff from the pair of estimated effective rainfall and observed value of runoff in the uniform interval was given less standard error then the analysis only by runoff, so this study was more rational forecasting by the use of effective rainfall and runoff. This paper analyzed the records of monthly runoff and effective rainfall, and applied the multiplicative ARlMA model and state-space model. For the P value of V AR(P) model to establish state-space theory, it used Ale value by lag time and VARMA model were established that it was findings to the constituent unit of state-space model using canonical correction coefficients. Therefore this paper confirms that state space model is very significant related with optimization factors of VARMA model.

• The Journal of the Korea Contents Association
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• v.10 no.5
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• pp.351-359
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• 2010

Recently, interesting of U-city with ubiquitous computing technologies has increased and u-city services can improve people's quality of life. Among the u-city services, traffice service is actively developed in our lives. In this paper, we propose the business model and business model process in u-intelligence traffic service. To propose the research purpose, we examined the prior business model and investigated u-intelligence traffic information and control services. And also, we draw scenario and used it to identify business model. To efficiently understand proposed business model, we built business model process of u-intelligence traffic information and control services. The results of study, we draw 4 representative U-intelligence traffic information and control service. Based on representative services, we proposed business model and business model process with stakeholder, benefiter and value model. This study concludes with implications of the study results as well as limitations and future research directions.

• Journal of Korea Water Resources Association
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• v.33 no.3
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• pp.341-350
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• 2000

The objectives of this study are to develop a state-space form of stochastic dynamic storage function routing model and to test the model performance for real-time flow forecast. The selected study area is the main Han River starting from Paldang Dam site to Indogyo station and the 13 flood events occurred from 1987 to 1998 are selected for computing model parameters and testing the model performance. It was shown that the optimal model parameters are quite different depending on Hood events, but the values used on field work also give reasonable results in this study area. It is also obvious that the model performance from the stochastic-dynamic model developed in this study gives more accurate and reliable results than that from the existing deterministic model. Analysis for allowable forecast lead time leads that under the current time step the reasonable predicted downstream flows in 5 hours time advance are obtained from the stochastic dynamic model on relatively less lateral inflow event in the study area.