• Journal of Korean Society of Transportation
• /
• v.23 no.7 s.85
• /
• pp.17-33
• /
• 2005

Highway design speed is a very important design element which determines highway design level. When determining highway design speed, one would estimate it utilizing the most likelihood of design speed and vehicle operating speed relationship. Existing operating speed prediction models only include highway geometric characteristics and their impacts on speed, which usually can not consider the impact of highway design speed on surrounding roadway environment and land use pattern. If this happens, excessive highway construction cost and huge environmental impact can occur. In this research project, a new vehicle operating speed prediction model was developed which can reflect the effect of surrounding roadway environment into vehicle speed prediction. The followings are the research findings : Firstly, highway terrain types and land use pattern on national roads were classified and integrated into drivers' visual recognition pattern. This was performed using a data management software. Secondly, the developed highway terrain types and land use pattern were related to vehicle speeds and it was found that there were significant statistical differences among vehicle speed for each different terrain and land use pattern. Thirdly. the General Linear Model analysis was employed to analyze the effects of highway geometric features, terrain types, and land use patterns. For two-lane highway and four-lane highway tested in this research project, it was found that R squares were 0.67 and 0.85, respectively. Additionally an optimal highway design speed range table, based on this research project. was proposed for practical use. This table can be reliably used on South Korean national road design, but discretion is required for applying this table to other types of highways including provincial roads and municipal roads.

• Journal of Korean Society of Transportation
• /
• v.29 no.6
• /
• pp.97-106
• /
• 2011

Mode choice model is an essential element for estimating- the demand of new means of transportation in the planning stage as well as in the establishment phase. In general, current demand analysis model developed for the mode choice analysis applies common parameters of utility function in each region which causes inaccuracy in forecasting mode choice behavior. Several critical problems from using common parameters are: a common parameter set can not reflect different distribution of coefficient for travel time and travel cost by different population. Consequently, the resulting model fails to accurately explain policy variables such as travel time and travel cost. In particular, the nonlinear logit model applied to aggregation data is vulnerable to the aggregation error. The purpose of this paper is to consider the regional characteristics by adopting the parameters fitted to each area, so as to reduce prediction errors and enhance accuracy of the resulting mode choice model. In order to estimate parameter of each area, this study used Household Travel Survey Data of Metropolitan Transportation Authority. For the verification of the model, the value of time by marginal rate of substitution is evaluated and statistical test for resulting coefficients is also carried out. In order to crosscheck the applicability and reliability of the model, changes in mode choice are analyzed when Seoul subway line 9 is newly opened and the results are compared with those from the existing model developed without considering the regional characteristics.

• Journal of Technology Innovation
• /
• v.27 no.4
• /
• pp.21-48
• /
• 2019

In the basic research field, quantitative expansion is carried out with active support from the government, but there is no research and policy data suggesting systematic investment plans or data-based financial requirements yet. Therefore, this study predicted future financial requirements of basic research support programs by using time series prediction model. In order to consider various factors including the characteristics of the basic research field, we selected the ARIMAX model which can reflect the effect of multi valuable factors rather than the ARIMA model which predicts the value of single factor over time. We compared the predictions of ARIMAX and ARIMA models for model suitability and found that the ARIMAX model improves the prediction error rate. Based on the ARIMAX model, we predicted the fiscal spending of basic research support programs for five years from 2017 to 2021. This study has significance in that it considers the financial requirements of the basic research support programs as a pilot research conducted by applying a time series model, which is a statistical approach, and multi-variate rather than single-variate. In addition, considering the policy trends that emphasize the importance of basic research investment such as 'the expansion of basic research budget twice', which is the current government's national policy task, it can be used as reference data in establishing basic research investment strategy.

• Journal of Korea Water Resources Association
• /
• v.38 no.4 s.153
• /
• pp.281-292
• /
• 2005

The water balance analysis for the long-term water resources plan is a simple calculation that compares water demands with possible water supplies. For a watershed being considered the reports on the performance of the water balance analysis, however, have shown inconsistent results and thus have not earned credibility due to the uncertainty of the data acquired and models used. In this research, uncertainties in the water scarcity estimate were assessed through probability representation based on the Monte Carlo simulation using Latin Hypercube Sampling (LHS). The natural flow, municipal demand, industrial demand, agricultural demand, and return flow rate were selected as representative input variables for the water balance analysis, and their distributions were set based on the linear regression and the entropy theory. The statistical properties of the output variable samples were analyzed in comparison with a deterministic estimate of the water scarcity of an existing study. Application of LHS to three sub-basins of the Geum river basin showed the deterministic estimate could be overestimated or underestimated. The sensitivity analysis as well as the uncertainty analysis found that the return flow rate of the agricultural water is the most uncertain but is rarely sensitive to the output of the water balance analysis.

• Economic and Environmental Geology
• /
• v.45 no.6
• /
• pp.697-707
• /
• 2012

The quantitative landslide susceptibility assessment methods can be divided into statistical approaches and geomechanical approaches based on the consideration of the triggering factors and landslide models. The geomechanical approach is considered as one of the most effective approaches since this approach proposes physical slope model and considers geomorphological and geomechanical properties of slope materials. Therefore, the geomechanical approaches has been used widely in landslide susceptibility analysis using the infinite slope model as physical slope model. However, the previous studies assumed constant groundwater level for broad study area without the consideration of rainfall intensity and hydraulic properties of soil materials. Therefore, in this study, landslide susceptibility assessment was implemented using the coupled infinite slope model with hydrologic model. For the analysis, geomechanical and hydrualic properties of slope materials and rainfall intensity were measured from the soil samples which were obtained from field investigation. For the practical application, the proposed approach was applied to Jinbu area, Gangwon-Do which was experienced large amount of landslides in July 2006. In order to compare to the proposed approach, the previous approach was used to analyze the landslide susceptibility using randomly selected groundwater level. Comparison of the results shows that the accuracy of the proposed method was improved with the consideration of the hydrologic model.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.60 no.6
• /
• pp.43-54
• /
• 2018

The accurate estimation of reference crop evapotranspiration ($ET_o$) is essential in irrigation water management to assess the time-dependent status of crop water use and irrigation scheduling. The importance of $ET_o$ has resulted in many direct and indirect methods to approximate its value and include pan evaporation, meteorological-based estimations, lysimetry, soil moisture depletion, and soil water balance equations. Artificial neural networks (ANNs) have been intensively implemented for process-based hydrologic modeling due to their superior performance using nonlinear modeling, pattern recognition, and classification. This study adapted two well-known ANN algorithms, Backpropagation neural network (BPNN) and Generalized regression neural network (GRNN), to evaluate their capability to accurately predict $ET_o$ using daily meteorological data. All data were obtained from two automated weather stations (Chupungryeong and Jangsu) located in the Yeongdong-gun (2002-2017) and Jangsu-gun (1988-2017), respectively. Daily $ET_o$ was calculated using the Penman-Monteith equation as the benchmark method. These calculated values of $ET_o$ and corresponding meteorological data were separated into training, validation and test datasets. The performance of each ANN algorithm was evaluated against $ET_o$ calculated from the benchmark method and multiple linear regression (MLR) model. The overall results showed that the BPNN algorithm performed best followed by the MLR and GRNN in a statistical sense and this could contribute to provide valuable information to farmers, water managers and policy makers for effective agricultural water governance.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.8 no.4
• /
• pp.242-249
• /
• 2006

Potential impacts of the future climate change on crop production can be inferred by crop simulations at a landscape scale, if the climate data may be provided at appropriate spatial scales. Northern Gyunggi Province is one of the few prospective regions in South Korea for growing quality soybeans. Any geographical shift of production areas under the changing climate may influence the current land planning policy in this region. A soybean growth simulation was performed at 342 land units in northern Gyunggi province to test the potential geographical shift of the current production areas for quality soybeans in the near future (form 2011 to 2100). The land units for soybean cultivation were selected by the land use, the soil characteristics, and the minimum arable land area. Daily maximum and minimum temperature, precipitation, the number of rain days and solar radiation were extracted for each land unit from the future digital climate models (DCM, 2011-2040, 2041-2070, 2071-2100). Daily weather data for 30 years were randomly generated for each land unit for each normal year by using a well-known statistical method. They were used to run CROPGRO-Soybean model to simulate the growth, phonology, and yields of 3 cultivars representing different maturity groups grown at 342 land units. According to the model calculations, the warming trend in this region will accelerate the flowering and physiological maturity of all cultivars, resulting in a 7 to 9 days reduction in overall growing season and a 1 to 15% reduction in grain yield of early to medium maturity cultivars. There was a slight increase in grain yield of the late maturing cultivar under the projected climate by 2070, but a decreasing tend was dominant by the year 2100.

• The Journal of Engineering Geology
• /
• v.20 no.2
• /
• pp.121-126
• /
• 2010

Current technology for radioactive waste disposal facility is operated as part of KURT site characterization in terms of reliability assessment is conducted to expand. In this study, a geological model of KURT surrounding area on the basis of flow characteristics of the site-scale hydrogeological study was about. Distributed in the study area into four boreholes were plotted using the stereo net NS, NW, EW, Low-angle fracture group was able to identify the components of geological models and include top soil layer, belt of weathering, Low-angle fracture zone, fracture zone was divided into. Separated by fracture of the hydraulic test of through the groundwater aquifer that provides the flow hydraulic conductivity and insulation hydraulic affecting the slope of the normal distribution for the size and direction by performing statistical analysis of fracture in the direction of local ns The advantage was confirmed. In addition, Low-angle fracture hydraulic conductivity of the value of 3.61e-07 m/s has a value greater than the major fracture, the fracture zones exist in the base rock and base rock and the hydraulic characteristics of the different methods applied and had to have a different interpretation judged by was.

• Journal of Broadcast Engineering
• /
• v.17 no.3
• /
• pp.519-528
• /
• 2012

Voice activity detection (VAD) is generally conducted by extracting features from the acoustic signal and a decision rule. The performance of such VAD algorithms driven by the input acoustic signal highly depends on the acoustic noise. When video signals are available as well, the performance of VAD can be enhanced by using the visual information which is not affected by the acoustic noise. Previous visual VAD algorithms usually use single visual feature to detect the lip activity, such as active appearance models, optical flow or intensity variation. Based on the analysis of the weakness of each feature, we propose to combine intensity change measure and the optical flow in the mouth region, which can compensate for each other's weakness. In order to minimize the computational complexity, we develop simple measures that avoid statistical estimation or modeling. Specifically, the optical flow is the averaged motion vector of some grid regions and the intensity variation is detected by simple thresholding. To extract the mouth region, we propose a simple algorithm which first detects two eyes and uses the profile of intensity to detect the center of mouth. Experiments show that the proposed combination of two simple measures show higher detection rates for the given false positive rate than the methods that use a single feature.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.15 no.12
• /
• pp.7134-7147
• /
• 2014

This study examined one of the contemporary issues that may be interesting to academics and practitioners regarding the driving force of the growth rate for the firms belonging to the chaebols in the Korean capital markets. With respect to the empirical results obtained from two hypothesis tests, the first hypothesis was to identify any financial determinants on the growth rate by applying both dynamic panel data and static panel data models. The debt ratios relevant to the book- and market-value showed their positive relationships with the DV of GROWTH1, along with other significant IDVs such as one-period lagged DV of GROWTH_1, SIZE1 and FOS with statistical significance. Second, by employing conditional quantile regression (CQR) analysis, the control variables, such as ROA, SMARKET, time dummy variable of F2010 and F2011, and the industry dummies of IND3 and IND10, provided evidence of their significant influences on DV of GROWTH1.