• Title, Summary, Keyword: Monte Carlo

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CHALLENGES AND PROSPECTS FOR WHOLE-CORE MONTE CARLO ANALYSIS

  • Martin, William R.
    • Nuclear Engineering and Technology
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    • v.44 no.2
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    • pp.151-160
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    • 2012
  • The advantages for using Monte Carlo methods to analyze full-core reactor configurations include essentially exact representation of geometry and physical phenomena that are important for reactor analysis. But this substantial advantage comes at a substantial cost because of the computational burden, both in terms of memory demand and computational time. This paper focuses on the challenges facing full-core Monte Carlo for keff calculations and the prospects for Monte Carlo becoming a routine tool for reactor analysis.

A Sequential Monte Carlo inference for longitudinal data with luespotted mud hopper data (짱뚱어 자료로 살펴본 장기 시계열 자료의 순차적 몬테 칼로 추론)

  • Choi, Il-Su
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.9 no.6
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    • pp.1341-1345
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    • 2005
  • Sequential Monte Carlo techniques are a set of powerful and versatile simulation-based methods to perform optimal state estimation in nonlinear non-Gaussian state-space models. We can use Monte Carlo particle filters adaptively, i.e. so that they simultaneously estimate the parameters and the signal. However, Sequential Monte Carlo techniques require the use of special panicle filtering techniques which suffer from several drawbacks. We consider here an alternative approach combining particle filtering and Sequential Hybrid Monte Carlo. We give some examples of applications in fisheries(luespotted mud hopper data).

Application of Fuzzy Math Simulation to Quantitative Risk Assessment in Pork Production (돈육 생산공정에서의 정량적 위해 평가에 fuzzy 연산의 적용)

  • Im, Myung-Nam;Lee, Seung-Ju
    • Korean Journal of Food Science and Technology
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    • v.38 no.4
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    • pp.589-593
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    • 2006
  • The objective of this study was to evaluate the use of fuzzy math strategy to calculate variability and uncertainty in quantitative risk assessment. We compared the propagation of uncertainty using fuzzy math simulation with Monte Carlo simulation. The risk far Listeria monocytogenes contamination was estimated for carcass and processed pork by fuzzy math and Monte Carlo simulations, respectively. The data used in these simulations were taken from a recent report on pork production. In carcass, the mean values for the risk from fuzzy math and Monte Carlo simulations were -4.393 log $CFU/cm^2$ and -4.589 log $CFU/cm^2$, respectively; in processed pork, they were -4.185 log $CFU/cm^2$ and -4.466 log $CFU/cm^2$ respectively. The distribution of values obtained using the fuzzy math simulation included all of the results obtained using the Monte Carlo simulation. Consequently, fuzzy math simulation was found to be a good alternative to Monte Carlo simulation in quantitative risk assessment of pork production.

Approximating Exact Test of Mutual Independence in Multiway Contingency Tables via Stochastic Approximation Monte Carlo

  • Cheon, Soo-Young
    • The Korean Journal of Applied Statistics
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    • v.25 no.5
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    • pp.837-846
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    • 2012
  • Monte Carlo methods have been used in exact inference for contingency tables for a long time; however, they suffer from ergodicity and the ability to achieve a desired proportion of valid tables. In this paper, we apply the stochastic approximation Monte Carlo(SAMC; Liang et al., 2007) algorithm, as an adaptive Markov chain Monte Carlo, to the exact test of mutual independence in a multiway contingency table. The performance of SAMC has been investigated on real datasets compared to with existing Markov chain Monte Carlo methods. The numerical results are in favor of the new method in terms of the quality of estimates.

Bayesian Estimation of State-Space Model Using the Hybrid Monte Carlo within Gibbs Sampler

  • Park, Ilsu
    • Communications for Statistical Applications and Methods
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    • v.10 no.1
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    • pp.203-210
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    • 2003
  • In a standard Metropolis-type Monte Carlo simulation, the proposal distribution cannot be easily adapted to "local dynamics" of the target distribution. To overcome some of these difficulties, Duane et al. (1987) introduced the method of hybrid Monte Carlo(HMC) which combines the basic idea of molecular dynamics and the Metropolis acceptance-rejection rule to produce Monte Carlo samples from a given target distribution. In this paper, using the HMC within Gibbs sampler, an asymptotical estimate of the smoothing mean and a general solution to state space modeling in Bayesian framework is obtaineds obtained.

MONTE CARLO DEPLETION UNDER LEAKAGE-CORRECTED CRITICAL SPECTRUM VIA ALBEDO SEARCH

  • Yun, Sung-Hwan;Cho, Nam-Zin
    • Nuclear Engineering and Technology
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    • v.42 no.3
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    • pp.271-278
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    • 2010
  • While the deterministic lattice physics/depletion codes use leakage-corrected critical spectrum (although approximate due to the B1 buckling search employed), Monte Carlo depletion codes currently in use do not have such a feature in spite of their heterogeneity and continuous-energy modeling capability. This paper describes an approach to Monte Carlo depletion with leakage-corrected critical spectrum derived from first principles. This is based on the concept of albedo eigenvalue treated as weight of the reflected neutron in Monte Carlo simulation.

EFFICIENT MONTE CARLO ALGORITHM FOR PRICING BARRIER OPTIONS

  • Moon, Kyoung-Sook
    • Communications of the Korean Mathematical Society
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    • v.23 no.2
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    • pp.285-294
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    • 2008
  • A new Monte Carlo method is presented to compute the prices of barrier options on stocks. The key idea of the new method is to use an exit probability and uniformly distributed random numbers in order to efficiently estimate the first hitting time of barriers. It is numerically shown that the first hitting time error of the new Monte Carlo method decreases much faster than that of standard Monte Carlo methods.

A Study on the Radioactivity Analysis of Decommissioning Concrete Using Monte Carlo Simulation (Monte Carlo 모사기법을 이용한 해체 콘크리트의 방사능 분석법 연구)

  • 서범경;김계홍;정운수;이근우;오원진;박진호
    • Proceedings of the Korean Radioactive Waste Society Conference
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    • pp.43-51
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    • 2004
  • In order to decommission the shielding concrete of KRR(Korea Research Reactor) -1&2, it must be exactly determined activated level and range by neutron irradiation during operation. To determine the activated level and range, it must be sampled and analyzed the core sample. But, there are difficulties in sample preparation and determination of the measurement efficiency by self-absorption. In the study, the full energy efficiency of the HPGe detector was compared with the measured value using standard source and the calculated one using Monte Carlo simulation. Also. self-absorption effects due to the density and component change of the concrete were calculated using the Monte Carlo method. Its results will be used radioactivity analysis of the real concrete core sample in the future.

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A Study on Generation of Stochastic Rainfall Variation using Multivariate Monte Carlo method (다변량 Monte Carlo 기법을 이용한 추계학적 강우 변동 생성기법에 관한 연구)

  • Ahn, Ki-Hong;Han, Kun-Yeun
    • Journal of Korean Society of Hazard Mitigation
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    • v.9 no.3
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    • pp.127-133
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    • 2009
  • In this study, dimensionless-cumulative rainfall curves were generated by multivariate Monte Carlo method. For generation of rainfall curve rainfall storms were divided and made into dimensionless type since it was required to remove the spatial and temporal variances as well as differences in rainfall data. The dimensionless rainfall curves were divided into 4 types, and log-ratio method was introduced to overcome the limitations that elements of dimensionless-cumulative rainfall curve should always be more than zero and the sum total should be one. Orthogonal transformation by Johnson system and the constrained non-normal multivariate Monte Carlo simulation were introduced to analyse the rainfall characteristics. The generative technique in stochastic rainfall variation using multivariate Monte Carlo method will contribute to the design and evaluation of hydrosystems and can use the establishment of the flood disaster prevention system.

Photon Beam Commissioning for Monte Carlo Dose Calculation

  • Cho, Byung-Chul;Park, Hee-Chul;Hoonsik Bae
    • Proceedings of the Korean Society of Medical Physics Conference
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    • pp.106-108
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    • 2002
  • Recent advances in radiation transport algorithms, computer hardware performance, and parallel computing make the clinical use of Monte Carlo based dose calculations possible. Monte Carlo treatment planning requires accurate beam information as input to generate accurate dose distributions. The procedures to obtain this accurate beam information are called "commissioning", which includes accelerator head modeling. In this study, we would like to investigate how much accurately Monte Carlo based dose calculations can predict the measured beam data in various conditions. The Siemens 6MV photon beam and the BEAM Monte Carlo code were used. The comparisons including the percentage depth doses and off-axis profiles of open fields and wedges, output factors will be presented.

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