• 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.

• The Journal of the Acoustical Society of Korea
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• v.21 no.3
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• pp.221-229
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• 2002

In thin paper, error tolerance of each array element which satisfies error tolerance of beam pattern is decided by using the Monte-Carlo method. Conventional deterministic method decides the error tolerance of each element from the acceptance pattern by testing all cases, but this method is not suitable for the analysis of large number of array elements because the computation resources increase exponentially as the number of array elements increases. To alleviate this problem, we applied new algorithm which reduces the increment of calculation time increased by the number of the array elements. We have validates the determined error tolerance region through several simulation.

• Journal of the Korean Data and Information Science Society
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• v.24 no.4
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• pp.669-677
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• 2013

Quasi-Monte Carlo method is known to have lower convergence rate than the standard Monte Carlo method. Quasi-Monte Carlo methods are using low discrepancy sequences as quasi-random numbers. They include Halton sequence, Faure sequence, and Sobol sequence. In this article, we compared standard Monte Carlo method, quasi-Monte Carlo methods and three scrambling methods of Owen, Faure-Tezuka, Owen-Faure-Tezuka in valuation of multi-asset European call option through simulations. Moro inversion method is used in generating random numbers from normal distribution. It has been shown that three scrambling methods are superior in estimating option prices regardless of the number of assets, volatility, and correlations between assets. However, there are no big differences between them.

• Progress in Medical Physics
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• v.11 no.2
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• pp.91-99
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• 2000

This is a preliminary study for developing the method of the dose reconstruction in the patients, irradiated by mega-voltage photon beams from the linear accelerator, using the transit dose distributions. In this study we present the method of three-dimensional dose reconstruction and evaluate the method by computer simulation. To acquire the dose distributions in the patients (or phantoms) we first calculate the differences between the doses at the arbitrary points in the patients and the doses at the corresponding points where the transit doses are measured. Then, we can get the dose in the patients from the measured transit dose and the calculated value of the difference. The dose differences are calculated by applying the inverse square law and using the linear attenuation coefficient. The scatter to primary dose ratios, which are calculated by the Monte Carlo program using the CT data of the patient (or phantoms), are also used in the calculations. For the evaluation of this method we used various kinds of homogeneous and inhomogeneous phantoms and calculated the transit dose distributions with the Monte Carlo program. From the distributions we reconstructed the dose distributions in the phantom. We used mono-energy Photon beam of 1.5MeV and Monte Carlo program EGS4. The comparison between the dose distributions reconstructed using the method and the distributions calculated by the Monte Carlo program was done. They agreed within errors of -4%∼+2%. This method can be used to predict the dose distributions in the patient

• 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).

• Water for future
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• v.18 no.4
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• pp.335-345
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• 1985

The purpose of stochastic models for synthetic generation of river flows based on the short-term observed data is to provide abundant input data to the water resources systems of which the system performance and operation policy are to be determined beforehand. Among many of such models the Monte Carlo Method of synthetic generation, which is usually known to be appropriate for annual data generation, is employed to check if it can be applied for the generation of monthly flows. For the purpose of comparisons the statistical parameters of the generated monthly flows by Monte Carlo model based on the appropriate probability distribution for each month were compared with those of the generated flows by Thoms-Fiering multiseason model and with those of the observed monthly flows. On the other hand, the statistical parameters of the annual river flows obtained by adding the generated monthly flows year by year based on the Monte Carlo and Thomas-Fiering models were compared with those of the annual flows generated directly by annual Monte Carlo model with reference to those for the observed annual river flows. Based on the above comparative studies, the discussions are made and conclusions derived.

• Proceedings of the Korean Vacuum Society Conference
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• 2013.08a
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• pp.105.2-105.2
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• 2013

배기속도 2500 L/s, 최고진공도 10-10 mbar의 구현을 목표로 하는 대용량 복합 분자펌프 설계를 위한 3차원 유동해석을 실시하였다. 진공도가 10-5 mbar 이상이 되는 고진공도에서는 Knudsen 수가 102 이상이 되어러 분자간 충돌을 거의 무시할 수 있게 되며, 이때의 유체해석 방법으로서는 통상 희박기체 해석법으로 많이 쓰이는 Direct simulation Monte Carlo 방법보다, 충돌이 없는 분자의 자유운동을 모사하는 Monte Carlo 방법이 더 적합할 수 있다. 본 연구에서는 다단계 rotor와 stator로 구성되는 복합분자 내 유동장에 Monte Carlo 해석법을 적용하여 유동해석을 실시하였다. 다양한 변수의 조합에 대한 수치적 해석에서, 복합분자펌프의 성능에 영향을 미치는 중요한 설계변수는 rotor-stator의 날개각, 유동방향 회전축의 두께 변화 등, 진행방향 분자의 모멘텀에 직접적인 영향을 미치는 변수들임이 확인되었다.

• Journal of the Korean Institute of Gas
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• v.19 no.5
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• pp.81-86
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• 2015

A deterministic stress-based methodology has traditionally been applied in pipeline design. Meanwhile, reliability based design and assessment (RBDA) methodology has been extensively applied in offshore or nuclear structures. Lately, the release of ISO standard on reliability based limit state methods for pipelines ISO16708 indicates that the RBDA methodology is one of the newest directions of natural gas pipeline design method. This paper presents a case study of the RBDA procedure for predicting the time-dependent failure probability of pipelines with corrosion defects, where separable Monte Carlo (SMC) method is applied in the reliability estimation for corroded pipeline instead of traditional, crude Monte Carlo(CMC) Method. The result shows the SMC method take advantage of improving accuracy in reliability calculation.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.2
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• pp.92-99
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• 2004

The errors can cause the serious loss of the performance of a precision machine system. In this paper, we proposed the method of allocating the alignment tolerances of the parts and applied this method to get the optimal tolerances of a Confocal Scanning Microscope. In general, tight tolerances are required to maintain the performance of a system, but a high cost of manufacturing and assembling is required to preserve the tight tolerances. The purpose of allocating the optimal tolerances is minimizing the cost while keeping the high performance of the system. In the optimal problem, we maximized the tolerances while maintaining the performance requirements. The Monte Carlo Method, a statistical simulation method, is used in tolerance analysis. Alignment tolerances of optical components of the confocal scanning microscope are optimized to minimize the cost and to maintain the observation performance of the microscope. We can also apply this method to the other precision machine system.

• Proceedings of the Korean Radioactive Waste Society Conference
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• 2004.06a
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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.