• Communications for Statistical Applications and Methods
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• 제14권2호
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• pp.345-353
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• 2007

A Bayesian estimation of the Nakagami-m fading parameter is developed. Bayesian estimation is performed by Gibbs sampling, including adaptive rejection sampling. A Monte Carlo study shows that the Bayesian estimators proposed outperform any other estimators reported elsewhere in the sense of bias, variance, and root mean squared error.

• Journal of the Korean Data and Information Science Society
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• 제10권2호
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• pp.465-472
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• 1999

We considered a bootstrap method for constructing confidenc intervals for a one parameter model using multinomial sampling. The convergence rates or the proposed bootstrap method are calculated for model-based maximum likelihood estimators(MLE) using multinomial sampling. Monte Carlo simulation was used to compare the performance of bootstrap methods with normal approximations in terms of the average coverage probability criterion.

• 한국방재학회 논문집
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• 제9권5호
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• pp.93-102
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• 2009

본 연구는 미호천 유역을 대상으로 유량곡선의 세부적인 특성을 고려한 다목적함수를 적용하여 Probability Distribution Model(PDM) 모형의 유량모의성능을 검토하였다. PDM은 유역을 한 개의 단위구역으로 개념화한 집중형 강우유출모형으로 영국의 지역화 연구 및 홍수량 산정방법에 대표적으로 이용되고 있다. PDM 모형의 5개 매개변수를 Monte Carlo 방법에 기반을 둔 분석도구(MCAT, Monte Carlo Analysis Toolkit)를 활용하여 사후검정분포, 검정근거 및 민감도 분석 등을 수행하였으며, 모형의 매개변수 중 cmax와 k(q)만이 뚜렷한 검정 근거가 있고 나머지 변수들은 동등성의 영향을 확인하였다. 또한, 유량곡선의 고유량 및 저유량의 특성을 맞춘 목적함수의 Trade-off를 고려한 매개변수의 파레토 최적해를 산정한 결과, 모든 목적에 최대한 부합하는 유량 산정의 가능성을 제시하였다. 검정(calibration)기간에서 NS*E=0.035, FSB=0.161, FDBH= 0.809로 안정적이며 만족할만한 모의성능을 나타내었고, 검증(validation)기간에 대해서도 안정적인 모의성능을 나타내었다.

• 방사선산업학회지
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• 제17권4호
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• pp.345-357
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• 2023

Nuclear forensics has been understood as a mendatory component in the international society for nuclear material control and non-proliferation verification. Radiochronometry of nuclear activities for nuclear forensics are decay series characteristics of nuclear materials and the Bateman equation to estimate when nuclear materials were purified and produced. Radiochronometry values have uncertainty of measurement due to the uncertainty factors in the estimation process. These uncertainties should be calculated using appropriate evaluation methods that are representative of the accuracy and reliability. The IAEA, US, and EU have been researched on radiochronometry and uncertainty of measurement, although the uncertainty calculation method using the Bateman equation is limited by the underestimation of the decay constant and the impossibility of estimating the age of more than one generation, so it is necessary to conduct uncertainty calculation research using computer simulation such as Monte Carlo method. This highlights the need for research using computational simulations, such as the Monte Carlo method, to overcome these limitations. In this study, we have analyzed mathematical models and the LHS (Latin Hypercube Sampling) methods to enhance the reliability of radiochronometry which is to develop an uncertainty algorithm for nuclear material radiochronometry using Bateman Equation. We analyzed the LHS method, which can obtain effective statistical results with a small number of samples, and applied it to algorithms that are Monte Carlo methods for uncertainty calculation by computer simulation. This was implemented through the MATLAB computational software. The uncertainty calculation model using mathematical models demonstrated characteristics based on the relationship between sensitivity coefficients and radiative equilibrium. Computational simulation random sampling showed characteristics dependent on random sampling methods, sampling iteration counts, and the probability distribution of uncertainty factors. For validation, we compared models from various international organizations, mathematical models, and the Monte Carlo method. The developed algorithm was found to perform calculations at an equivalent level of accuracy compared to overseas institutions and mathematical model-based methods. To enhance usability, future research and comparisons·validations need to incorporate more complex decay chains and non-homogeneous conditions. The results of this study can serve as foundational technology in the nuclear forensics field, providing tools for the identification of signature nuclides and aiding in the research, development, comparison, and validation of related technologies.

• 한국통신학회논문지
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• 제38C권10호
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• pp.852-857
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• 2013

본 논문은 오류 마루 영역에서 우수한 성능을 가지는 비이진 LDPC (low-density parity-check) 부호의 설계 방법을 제안하고 성능을 검증한다. 제안된 설계 방법은 비이진 LDPC 부호의 이진 최소 거리(binary minimum distance)를 최대화하도록 패리티 검사 행렬의 비이진 원소 값들을 결정한다. BPSK (binary phase-shift keying) 변조 방식 하에서 제안된 방법으로 설계된 비이진 LDPC 부호가 오류 마루(error floor) 영역에서 우수한 성능을 가지는 것을 Monte Carlo 시뮬레이션과 중요도 표본 추출(importance sampling) 기법을 사용하여 검증한다.

• 한국ITS학회 논문지
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• 제22권2호
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• pp.64-79
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• 2023

본 연구는 Monte Carlo 기법을 교통카드기반의 수도권 지하철의 통행배정 문제에 적용하는 과정을 검토하였다. 연구는 우선 교통카드에서 역 간 표본의 통행에서 나타나는 통행시간에 대하여 프로빗 모형의 기반이 되는 정규분포의 가정을 적용하였다. Monte Carlo 통행배정은 역 간 통행에 대하여 평균과 표준편차를 산정하고 이를 개별 링크의 차내시간과 환승의 보행 및 배차간격의 가중치로 적용하는 방안을 제안하였다. 샘플 수가 50 이하로 낮게 나타나는 장거리 통행은 유사 통행의 특성을 이전하는 방안으로 적용하였다. 수도권 지하철 네트워크에 대하여 두 가지 방향에서 연구 결과를 검토하였다. 하나는 선릉-성수의 단일 역 간 통행에 대하여 차내시간 및 환승시간에 랜덤샘플링을 적용하는 방안으로 검증하였다. 다음으로 수도권 지하철 전체에 대해서는 역 간 통행 샘플수에 따라서 50 이상은 역 간 정규분포의 가정을 그대로 수용하였다. 샘플수가 50 이하의 장거리 통행은 역 간 최소거리가 122 (Km)에서 표본의 균등성이 확보되는 상황으로 판단하고 이 거리에서 나타나는 카드자료의 역 간 평균과 표준편차를 적용하였다. 사례연구로서 교통카드자료로 구축된 수도권 지하철을 네트워크를 대상으로 단일OD 및 전체 OD의 통행배정의 결과를 도출하였다. 한편 통행에 대한 샘플링이 부족한 상황에서 추가적인 연구가 필요한 것으로 나타났다.

• Wind and Structures
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• 제22권1호
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• pp.17-41
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• 2016

To address the uncertainty of the flight trajectories caused by the turbulence and gustiness of the wind field over the roof and in the wake of a building, a 3-D probabilistic trajectory model of flat-type wind-borne debris is developed in this study. The core of this methodology is a 6 degree-of-freedom deterministic model, derived from the governing equations of motion of the debris, and a Monte Carlo simulation engine used to account for the uncertainty resulting from vertical and lateral gust wind velocity components. The influence of several parameters, including initial wind speed, time step, gust sampling frequency, number of Monte Carlo simulations, and the extreme gust factor, on the accuracy of the proposed model is examined. For the purpose of validation and calibration, the simulated results from the 3-D probabilistic trajectory model are compared against the available wind tunnel test data. Results show that the maximum relative error between the simulated and wind tunnel test results of the average longitudinal position is about 20%, implying that the probabilistic model provides a reliable and effective means to predict the 3-D flight of the plate-type wind-borne debris.

• 전기학회논문지P
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• 제55권2호
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• pp.57-61
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• 2006

This paper calculates and gives the analysis of mean energy in pure $SiH_4,\;Ar-SiH_4$ mixture gases ($SiH_4-0.5[%],\;5[%]$) over the range of $E/N =0.01{\sim}300[Td]$, p = 0.1, 1, 5.0 [Torr] by Monte Carlo the Backward prolongation method of the Boltzmann equation using computer simulation without using expensive equipment. The results have been obtained by using the electron collision cross sections by TOF, PT, SST sampling, compared with the experimental data determined by the other author. It also proved the reliability of the electron collision cross sections and shows the practical values of computer simulation. The calculations of electron swarm parameters require the knowledge of several collision cross-sections of electron beam. Thus, published momentum transfer, ionization, vibration, attachment, electronic excitation, and dissociation cross-sections of electrons for $SiH_4$ and Ar, were used. The differences of the transport coefficients of electrons in $SiH_4$, mixtures of $SiH_4$ and Ar, have been explained by the deduced energy distribution functions for electrons and the complete collision cross-sections for electrons. A two-term approximation of the Boltzmann equation analysis and Monte Carlo simulation have been used to study electron transport coefficients.

• Nuclear Engineering and Technology
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• 제50권3호
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• pp.439-445
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• 2018

Experimental measurements of the response of $BF_3$ detector to a 3 Ci Am-Be neutron source for three different reflecting materials, i.e., aluminum, wood, and Perspex of varying thicknesses have been carried out. The varying contribution of wall effect to the response due to change in active volume of the detector has also been determined experimentally. Then, a Monte Carlo code has been developed for the calculation of the neutron response function of the $BF_3$ detector using source biasing and importance sampling. This code simulates the $BF_3$ detector response exposed to the neutron field in a three-dimensional source, detector, and reflecting medium configurations. The results of simulation have been compared with the corresponding experimental measurements and are found to be in good agreement. The experimental neutron albedo measurements for various values of Perspex thickness show saturating behavior, and results agree very well with the data obtained by Monte Carlo simulation.

• Journal of Information Processing Systems
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• 제15권2호
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• pp.331-343
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• 2019

Dynamic thermal rating of the overhead transmission lines is affected by many uncertain factors. The ambient temperature, wind speed and wind direction are the main sources of uncertainty. Measurement uncertainty is an important parameter to evaluate the reliability of measurement results. This paper presents the uncertainty analysis based on Monte Carlo. On the basis of establishing the mathematical model and setting the probability density function of the input parameter value, the probability density function of the output value is determined by probability distribution random sampling. Through the calculation and analysis of the transient thermal balance equation and the steady- state thermal balance equation, the steady-state current carrying capacity, the transient current carrying capacity, the standard uncertainty and the probability distribution of the minimum and maximum values of the conductor under 95% confidence interval are obtained. The simulation results indicate that Monte Carlo method can decrease the computational complexity, speed up the calculation, and increase the validity and reliability of the uncertainty evaluation.