• Journal of Korea Water Resources Association
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• v.30 no.6
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• pp.679-690
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• 1997

The risk assessment model for hydrlolgic safety analysis of dam and levee in developed by using Monte-Carlo and AFOSM (Advanced First-Order Second-Moment) method. The fault tree analysis and four phases approach are presented for the safety eveluation of risk of dam and levee. The risk model consists of rainfall-runoff analysis, reservoir routing and channel routing considering the variations in the model parameter. For the rainfall-runoff analysis, KRRL method is adopted with 200-year precipitation and PMP (Probable Maximum Precipitation). Reservoir routing is performed by fourth order Runge-Kutta method and channel routing by standard step method. The suggested model will contribute to safety evaluation of dam and levee and their rehabilitation decision problem.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.485-490
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• 2003

Nowadays the rapid change in construction environment getting more globalized and complicated has caused lots of unexpected risks from inside and out of the country, so more sophisticated construction management strategies are being strongly needed. This paper suggests a risk management model with which we can estimate the appropriate contingency by quantifying the amount of probable risks immanent in large construction projects, which have a high degree of uncertainty in the anticipation of the total construction cost. To develop the model, the risk factors that make cost variations are elicited based on the real data of the contingencies assigned to the past projects. Furthermore, the influential relationship of risk factors is structured by applying the CRM(Cost Risk Model) which is the synthetic model of Monte Carlo Simulation, Influence Diagram and Decision Tree. The ultimate outcome of this research can by validated by tile case study with a large construction project performed.

• The Journal of the Korea institute of electronic communication sciences
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• v.6 no.2
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• pp.288-295
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• 2011

The paper proposes a localization method of an underwater robot using Monte Carlo Localization(MCL) approach. Localization is one of the fundamental basics for autonomous navigation of an underwater robot. The proposed method resolves the problem of accumulation of position error which is fatal to dead reckoning method. It deals with uncertainty of the robot motion and uncertainty of sensor data in probabilistic approach. Especially, it can model the nonlinear motion transition and non Gaussian probabilistic sensor characteristics. In the paper, motion model is described using Euler angles to utilize the MCL algorithm for position estimation of an underwater robot. Motion model and sensor model are implemented and the performance of the proposed method is verified through simulation.

• Communications for Statistical Applications and Methods
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• v.5 no.2
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• pp.559-570
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• 1998

This paper introduces doubly Winsorized Poisson auto-model by truncating the support of a Poisson random variable both from above and below, and shows that this model has a same form of negpotential function as regular Poisson auto-model and one-way Winsorized Poisson auto-model. Strategies for maximum likelihood estimation of parameters are discussed. In addition to exact maximum likelihood estimation, Monte Carlo maximum likelihood estimation may be applied to this model.

• Korean Journal of Construction Engineering and Management
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• v.2 no.2 s.6
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• pp.98-108
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• 2001

This research proposes a new risk analysis model in order to guarantee successful performance of construction projects. The risk analysis model, called Construction Risk Analysis System(CRAS), is introduced to help contractors Identify project risks through RBS and through the procedures in risk analysis model. The proposed CRAS model consists of three phases. First step, CRAS model can help contractors decide whether or not they bid for a project by analysing risks involved in the project. Second step, the influence diagraming, decision tree and Monte Carlo simulation are used as tools to analyze and evaluate project risks quantitatively. Third step, Monte Carlo simulation is used to assess risk for groups of activities with probabilistic branching and calendars. Consequently, it will help contractors identify risk elements in their projects and quantify the impact of risk on project time and cost.

• The Korean Journal of Applied Statistics
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• v.19 no.3
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• pp.505-519
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• 2006

We consider zero-inflated count data, which is discrete count data but has too many zeroes compared to the Poisson distribution. Zero-inflated data can be found in various areas. Despite its increasing importance in practice, appropriate statistical inference on zero-inflated data is limited. Classical inference based on a large number theory does not fit unless the sample size is very large. And regular Poisson model shows lack of St due to many zeroes. To handle the difficulties, a mixture of distributions are considered for the zero-inflated data. Specifically, a mixture of a point mass at zero and a Poisson distribution is employed for the data. In addition, when there exist meaningful covariates selected to the response variable, loglinear link is used between the mean of the response and the covariates in the Poisson distribution part. We propose a Bayesian inference for the zero-inflated Poisson regression model by using a Markov Chain Monte Carlo method. We applied the proposed method to a Korean oral hygienic data and compared the inference results with other models. We found that the proposed method is superior in that it gives small parameter estimation error and more accurate predictions.

• Wind and Structures
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• v.31 no.2
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• pp.85-102
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• 2020

Accurate load evaluation is essential in any performance-based design. Design wind speeds and associated wind loads are well defined for synoptic boundary layer winds but not for thunderstorms. The method presented in the current study represents a new approach to obtain design wind speeds associated with thunderstorms and their gust fronts using historical data and Monte Carlo simulations. The method consists of the following steps (i) developing a numerical model for thunderstorm downdrafts (i.e. downbursts) to account for storm translation and outflow dissipation, (ii) utilizing the model to characterize previous events and (iii) extrapolating the limited wind speed data to cover life-span of structures. The numerical model relies on a previously generated CFD wind field, which is validated using six documented thunderstorm events. The model suggests that 10 parameters are required to describe the characteristics of an event. The model is then utilized to analyze wind records obtained at Lubbock Preston Smith International Airport (KLBB) meteorological station to identify the thunderstorm parameters for this location, obtain their probability distributions, and utilized in the Monte Carlo simulation of thunderstorm gust front events for many thousands of years for the purpose of estimating design wind speeds. The analysis suggests a potential underestimation of design wind speeds when neglecting thunderstorm gust fronts, which is common practice in analyzing historical wind records. When compared to the design wind speed for a 700-year MRI in ASCE 7-10 and ASCE 7-16, the estimated wind speeds from the simulation were 10% and 11.5% higher, respectively.

• Journal of The Korean Society of Agricultural Engineers
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• v.49 no.4
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• pp.13-22
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• 2007

The uncertainty in water quality model predictions is inevitably high due to natural stochasticity, model uncertainty, and parameter uncertainty. An integrated modeling system under uncertainty was described and demonstrated for use in watershed management and receiving-water quality prediction. A watershed model (HSPF), a receiving water quality model (WASP), and a wetland model (NPS-WET) were incorporated into an integrated modeling system (modified-BASINS) and applied to the Hwaseong Reservoir watershed. Reservoir water quality was predicted using the calibrated integrated modeling system, and the deterministic integrated modeling output was useful for estimating mean water quality given future watershed conditions and assessing the spatial distribution of pollutant loads. A Monte Carlo simulation was used to investigate the effect of various uncertainties on output prediction. Without pollution control measures in the watershed, the concentrations of total nitrogen (T-N) and total phosphorous (T-P) in the Hwaseong Reservoir, considering uncertainty, would be less than about 4.8 and 0.26 mg 4.8 and 0.26 mg $L^{-1}$, respectively, with 95% confidence. The effects of two watershed management practices, a wastewater treatment plant (WWTP) and a constructed wetland (WETLAND), were evaluated. The combined scenario (WWTP + WETLAND) was the most effective at improving reservoir water quality, bringing concentrations of T-N and T-P in the Hwaseong Reservoir to less than 3.54 and 0.15 mg ${L^{-1}$, 26.7 and 42.9% improvements, respectively, with 95% confidence. Overall, the Monte Carlo simulation in the integrated modeling system was practical for estimating uncertainty and reliable in water quality prediction. The approach described here may allow decisions to be made based on probability and level of risk, and its application is recommended.

• Journal of Radiation Protection and Research
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• v.29 no.4
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• pp.269-273
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• 2004

The hybrid dead time model adopting paralyzable (or extendable) and non-paralyzable (or non-extendable) dead times has been introduced to extend the usable range of G-M counters in high counting rate environment and the relationship between true and observed counting rates is more accurately expressed in the hybrid model. GMSIM, dead time effects simulator, has been developed to analyze the counting statistics of G-M counters using Monte Carlo simulations. GMSIM accurately described the counting statistics of the paralyzable and non-paralyzable models. For G-M counters that follow the hybrid model, the counting statistics behaved in between two idealized models. In the future, GMSIM may be used in predicting counting statistics of three G-M dead time models, which are paralyzable, non-paralyzable and hybrid models.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.2058-2062
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• 2009

1992년 한강수계법이 제정되면서 우리나라에 도입된 오염총량관리제는 현재 2차총량관리 시행계획수립 단계에 이르렀다. 오염총량제에서 수질모델은 수계구간별로 설정된 기준유량과 목표수질 조건을 달성하는 지를 판단할 수 있는 도구로 사용되며, 다양한 모델들이 사용되고 있다. 그 중 하천수질모형으로는 주로 QUAL2E, QUALKO, QUALKO2 모형으로 압축할 수 있다. QUAL2E 모형은 1980년대에 개발되어 국내외로 널리 이용하고 있으나 SOD를 0차나 일정량으로 처리하였고, 부착조류에 의한 용존산소 변화와 부유 조류 사멸시 발생하는 유기물이 고려되지 않았다. 또한 용존산소가 부족한 상태에서 반응이 활발한 탈질화과정이 포함되지 않아 이들 반응에 의해 수질이 영향을 받는 하천에 적용하기에는 한계가 있었다. 그리고 QUAL2E 모델은 여러 개의 지류를 가진 대형 하천에는 적용하기 어려운 단점이 있다. 국내에서는 1999년 QUAL2E 모델에 WASP5의 장점을 접목시켜 QUALKO 모델을 개발하였다. 이 모델은 QUAL2E에 부유성 조류의 사멸로 인한 유기물의 내부증가, 탈질화 반응 및 부착식물의 광합성 호흡 과정을 추가한 것이다. 또한 QUAL2E 모델에서 BOD는 CBOD로 입력되고 모의되므로 bottle BOD의 개념이 결여되어 있으므로 이러한 문제점을 보완하고, 조류의 생산 및 사멸에 의한 내부생산 유기물 증가와 탈질화 반응 과정을 추가한 것이다. 우리나라에서 진행되고 있는 총량관리 대상물질은 2010년까지는 $BOD_5$이며, 2011년부터는 일부 지역에 총인이 포함될 예정이다. 2007년에 실험실에서 측정하는 BOD5나 유기성 질소 또는 유기성 인을 그대로 입력하여 계산되고 출력할 수 있으며, 향후 오염총량제의 관리대상항목으로 논의되고 있는 TOC를 모의할 수 있는 QUALKO2가 개발되었다. 이에 본 연구에서는 향후 활용도가 클 것으로 기대되는 QUALKO2 모형에 기존 QUAL2E-UNCAS 모형에 서 수행할 수 있는 불확실성 해석 기법인 Monte Carlo 모의를 가능하도록 모형을 수정하고자 한다. 실제 하천에서의 수질해석에 대한 단순한 표현인 수학적 모형은 불확실성을 내포하고 있으며, Monte Carlo 해석을 사용하여 모형의 불확실도 정량화와 매개변수의 불확실성을 통계학적으로 기술할 수 있다. 또한 각 지점에 대한 계산결과치들에 대해 빈도 및 누가빈도분포 값을 제시함으로서 모형 예측치들의 전반적인 분포경향을 평가할 수 있으며, 하천수질에 대해서 환경기준치를 위배할 가능성을 산정하는데 활용할 수 있다. 우리나라 실정에 맞는 QUALKO2 모형에 Monte Carlo 모의를 통해 신뢰도 기반의 수질해석을 수행하게 된다면 수질정책의 기초자료 제공에 기여할 것으로 판단된다.