• Journal of Korean Society of Transportation
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• v.23 no.3 s.81
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• pp.95-108
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• 2005

The purpose of this study is to develop the ITS evaluation model using the Probabilistic Risk Analysis (PRA) methodology and to demonstrate the goodness-of-fit of the large ITS projects through the comparative analysis between DEA and PRA model. The results of this study are summarized below. First, the evaluation mode] using PRA with Monte-Carlo Simulation(MCS) and Latin-Hypercube Sampling(LHS) is developed and applied to one of ITS projects initiated by local government. The risk factors are categorized with cost, benefit and social-economic factors. Then, PDF(Probability Density Function) parameters of these factors are estimated. The log-normal distribution, beta distribution and triangular distribution are well fitted with the market and delivered price. The triangular and uniform distributions are valid in benefit data from the simulation analysis based on the several deployment scenarios. Second, the decision making rules for the risk analysis of projects for cost and economic feasibility study are suggested. The developed PRA model is applied for the Daejeon metropolitan ITS model deployment project to validate the model. The results of cost analysis shows that Deterministic Project Cost(DPC), Deterministic Total Project Cost(DTPC) is the biased percentile values of CDF produced by PRA model and this project need Contingency Budget(CB) because these values are turned out to be less than Target Value(TV;85% value), Also, this project has high risk of DTPC and DPC because the coefficient of variation(C.V) of DTPC and DPC are 4 and 15 which are less than that of DTPC(19-28) and DPC(22-107) in construction and transportation projects. The results of economic analysis shows that total system and subsystem of this project is in type II, which means the project is economically feasible with high risk. Third, the goodness-of-fit of PRA model is verified by comparing the differences of the results between PRA and DEA model. The difference of evaluation indices is up to 68% in maximum. Because of this, the deployment priority of ITS subsystems are changed in each mode1. In results. ITS evaluation model using PRA considering the project risk with the probability distribution is superior to DEA. It makes proper decision making and the risk factors estimated by PRA model can be controlled by risk management program suggested in this paper. Further research not only to build the database of deployment data but also to develop the methodologies estimating the ITS effects with PRA model is needed to broaden the usage of PRA model for the evaluation of ITS projects.

• Journal of the Korean Geotechnical Society
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• v.29 no.9
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• pp.5-15
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• 2013

In this paper, the unsaturated slope stability analysis considering suction stress (Lu and Godt, 2008) was introduced and the results applied for a certain sand slope were analyzed. The unsaturated slope stability analysis considering suction stress can analyze both conditions of steady infiltration and no infiltration, and it can estimate the safety factor of slope as a function of soil depth. Also, the influence of weathering phenomenon at a certain depth from the ground surface can be considered. The stability analysis considering suction stress was applied to the unsaturated infinite slope composed of sand with the relative density of 60%. The suction stress under no infiltration condition was affected by ground water table until a certain influencing depth. However, the suction stress under steady infiltration condition was affected by seepage throughout the soils. Especially, the maximum suction stress was displayed around ground surface. The factor of safety in the infinite slope under no infiltration condition rapidly increased and decreased within the influence zone of ground water table. As a result of slope stability analysis, the factor of safety is less than 1 at the depth of 2.4 m below the ground surface. It means that the probability of slope failure is too high within the range of depths. The factor of safety under steady infiltration condition is greater than that under no infiltration condition due to the change of suction stress induced by seepage. As the steady infiltration rate of precipitation was getting closer to the saturated hydraulic conductivity, the factor of safety decreased. In case of the steady infiltration rate of precipitation with $-1.8{\times}10^{-3}cm/s$, the factor of safety is less than 1 at the depths between 0.2 m and 3 m below the ground surface. It means that the probability of slope failure is too high within the range of depths, and type of slope failure is likely to be shallow landslides.

• Journal of the Korean Society of Radiology
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• v.7 no.6
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• pp.403-408
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• 2013

This study made a tentative estimation of the shielding rate of barium compound by thickness through monte carlo simulation to apply medical radiation shielding products that can replace existing lead. Barium sulfate($BaSO_4$) was used for the shielding material, and thickness of the shielding material specimen was simulated from 0.1 mm to 5 mm by applying $15{\times}15cm^2$ of specimen area, $4.5g/cm^3$ of density of barium sulfate, and $11.34g/cm^3$ density of lead. Entered source was simulated with 10kVp Step in consecutive X-ray energy spectrum(40 kVp ~ 120 kVp). Absorption probability in 40 kVp ~ 60 kVp showed same shielding rate with lead in 3 mm ~ 5 mm of thickness, but it was identified that under 2 mm, the shielding rate was a bit lower than the existing lead shielding material. Also, the shielding rate in 70 kVp ~ 120 kVp energy band showed similar performance as the existing lead shielding material, but it was tentatively estimated as fairly low shielding rate below 0.5 mm. This study estimated the shielding rate of barium compound as the thickness function of x-ray energy band for medical radiation through monte carlo simulation, and made comparative analysis with existing lead. Also, this study intended to verify application validity of the x-ray shielding material for medical radiation of pure barium sulfate. As a result, it was estimated that the shielding effect was 95% higher than the existing lead 1.5 mm in at least 2 mm thickness of barium compound in medical radiation energy band 70 kVp ~ 120 kVp, and this result is considered valid to be provided as a base data in weight lightening production of radiation shielding product for medical radiation.

• Journal of the Korean Institute of Landscape Architecture
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• v.49 no.2
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• pp.89-100
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• 2021

This study evaluated the degree of interaction between the people and the environment using perceived naturalness measure. The seventh-grade index of Hemeroby was divided into subclasses of land cover according to degrees of human influence. The grade was standardized for each indicator to evaluate the current state of urban parks in Seoul by applying probability density function and weight. User evaluation was conducted on six distinctive parks selected. In the results, three implications were found between spatial evaluation according to the perceived naturalness. First, park users evaluated highly for the spaces such as broad-leaved forest, coniferous forest and mixed forest evaluated highly in the Hemeroby grade index. Park users generally recognized that various types of trees in the area had high naturalness. The density of trees is one of the factors in perceived naturalness. Second, water spaces were highly evaluated for naturalness in the Hemeroby grade index. However, the perceived naturalness of water spaces such as inland wetlands, pond and reservoir evaluated in various ways depending on environmental conditions around the park. Third, perceived naturalness is easily evaluated through vertical landscape elements such as trees rather than horizontal landscapes such as grassland. The perceived naturalness is similar to the naturalness evaluation using land cover. However the study found the perceived naturalness for a specific space was different from the Hemeroby index. Perceived naturalness by the user includes the content that the individual sees, hears, and experiences. Park users are usually structuring naturalness through evaluating the value of urban green spaces based on personal perception. Therefore there is no absolute standard criterion for evaluating the naturalness of urban green spaces. A deeper study is needed that considers user bundles or user groups with conflicting interests on the perceived naturalness in urban parks. These studies will be essential data on the direction of naturalness urban park service should provide.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.3
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• pp.149-155
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• 2021

Early warning systems for weather risk management in the agricultural sector have been developed to predict potential wind damage to crops. These systems take into account the daily maximum wind speed to determine the critical wind speed that causes fruit drops and provide the weather risk information to farmers. In an effort to increase the accuracy of wind risk predictions, an artificial neural network for binary classification was implemented. In the present study, the daily wind speed and other weather data, which were measured at weather stations at sites of interest in Jeollabuk-do and Jeollanam-do as well as Gyeongsangbuk- do and part of Gyeongsangnam- do provinces in 2019, were used for training the neural network. These weather stations include 210 synoptic and automated weather stations operated by the Korean Meteorological Administration (KMA). The wind speed data collected at the same locations between January 1 and December 12, 2020 were used to validate the neural network model. The data collected from December 13, 2020 to February 18, 2021 were used to evaluate the wind risk prediction performance before and after the use of the artificial neural network. The critical wind speed of damage risk was determined to be 11 m/s, which is the wind speed reported to cause fruit drops and damages. Furthermore, the maximum wind speeds were expressed using Weibull distribution probability density function for warning of wind damage. It was found that the accuracy of wind damage risk prediction was improved from 65.36% to 93.62% after re-classification using the artificial neural network. Nevertheless, the error rate also increased from 13.46% to 37.64%, as well. It is likely that the machine learning approach used in the present study would benefit case studies where no prediction by risk warning systems becomes a relatively serious issue.