• Journal of the Korean Statistical Society
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• v.28 no.2
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• pp.135-150
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• 1999

Latin hypercube sampling(LHS) introduced by McKay et al. (1979) is a widely used method for Monte Carlo integration. Stratified Latin hypercube sampling(SLHS) proposed by Choi and Lee(1993) improves LHS by combining it with stratified sampling. In this article it is shown that SLHS yields an asymptotically more accurate than both stratified sampling and LHS.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.2
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• pp.248-254
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• 2018

In this paper, a Monte Carlo-based Recursive Least Square(MC-RLS) method is presented to directly identify the inverse model of the dynamical system. Although a RLS method has been used for the identification based on the deterministic data in the closed loop controlled form, it would be better for RLS to identify the model with random data. In addition, the inverse model obtained by inverting the identified forward model may not work properly. Therefore, MC-RLS can be used for the inverse model identification without proceeding a numerical inversion of an identified forward model. The performance of the proposed method is verified through experimental studies on a control moment gyroscope.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.1
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• pp.381-394
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• 2016

The several noise level estimation algorithms that have been developed for use in image processing and computer graphics generally exhibit good performance. However, there are certain special types of noisy images that such algorithms are not suitable for. It is particularly still a challenge to use the algorithms to estimate the noise levels of complex textured photographic images because of the inhomogeneity of the original scenes. Similarly, it is difficult to apply most conventional noise level estimation algorithms to images rendered by the Monte Carlo (MC) method owing to the spatial variation of the noise in such images. This paper proposes a novel noise level estimation method based on histogram modification, and which can be used for more accurate estimation of the noise levels in both complex textured images and MC-rendered images. The proposed method has good performance, is simple to implement, and can be efficiently used in various image-based and graphic applications ranging from smartphone camera noise removal to game background rendition.

• International Journal of Aeronautical and Space Sciences
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• v.16 no.4
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• pp.537-547
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• 2015

A Monte Carlo (MC) simulation was conducted to predict the reliability of a newly developed pyrotechnic pin puller. The reliability model is based on the stress-strength interference model that states that failure occurs if the stress exceeds the strength. In this study, the stress is considered to be the energy consumed by movement of a pin shaft, and the strength is considered to be the energy generated by pyrotechnic combustion for driving the pin shaft. Failure of the pin puller can thus be defined as the consumed energy being greater than the generated energy. These energies were calculated using a performance model formulated in the previous study of the present authors. The MC method was used to synthesize the probability densities of the two energies and evaluate the reliability of the pin puller. From a probabilistic perspective, the calculated reliability was compared to a deterministic safety factor. A sensitivity analysis was also conducted to determine which design parameters most affect the reliability.

• Nuclear Engineering and Technology
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• v.55 no.1
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• pp.373-377
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• 2023

The Monte Carlo (MC) method has become an indispensable part of the nuclear radiation research field. Several widely used and well-known MC packages were developed for simulation of radiation transport and interaction with matter. All these MC packages require users to prepare an input script. The input script can become lengthy for complex models. The process of preparing these input scripts is time-consuming and error-prone. In the present work, we have developed an open-source GUI computer program for modelling radiation transport and interaction in multi-segmented slab phantoms using grid-based system for the widely used PHITS MC package. The developed tools would be useful for future users of PHITS MC package and particularly inexperienced users. The present program is distributed under GPL license and all users can freely download, modify and redistribute the program without any restrictions.

• Nuclear Engineering and Technology
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• v.48 no.3
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• pp.765-772
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• 2016

In analyzing residual radiation, researchers generally use a two-step Monte Carlo (MC) simulation. The first step (MC1) simulates neutron transport, and the second step (MC2) transports the decay photons emitted from the activated materials. In this process, the stochastic uncertainty estimated by the MC2 appears only as a final result, but it is underestimated because the stochastic error generated in MC1 cannot be directly included in MC2. Hence, estimating the true stochastic uncertainty requires quantifying the propagation degree of the stochastic error in MC1. The brute force technique is a straightforward method to estimate the true uncertainty. However, it is a costly method to obtain reliable results. Another method, called the adjoint-based method, can reduce the computational time needed to evaluate the true uncertainty; however, there are limitations. To address those limitations, we propose a new strategy to estimate uncertainty propagation without any additional calculations in two-step MC simulations. To verify the proposed method, we applied it to activation benchmark problems and compared the results with those of previous methods. The results show that the proposed method increases the applicability and user-friendliness preserving accuracy in quantifying uncertainty propagation. We expect that the proposed strategy will contribute to efficient and accurate two-step MC calculations.

• Journal of Radiation Protection and Research
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• v.43 no.1
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• pp.10-19
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• 2018

Background: Monte Carlo (MC) simulation is the most accurate for calculating radiation dose distribution and determining patient dose. In MC simulations of the therapeutic accelerator, the characteristics of the initial electron must be precisely determined in order to achieve accurate simulations. However, It has been computation-, labor-, and time-intensive to predict the beam characteristics through predominantly empirical approach. The aim of this study was to analyze the relationships between electron beam parameters and dose distribution, with the goal of simplifying the MC commissioning process. Materials and Methods: The Varian Clinac 2300 IX machine was modeled with the Geant4 MC-toolkit. The percent depth dose (PDD) and lateral beam profiles were assessed according to initial electron beam parameters of mean energy, radial intensity distribution, and energy distribution. Results and Discussion: The PDD values increased on average by 4.36% when the mean energy increased from 5.6 MeV to 6.4 MeV. The PDD was also increased by 2.77% when the energy spread increased from 0 MeV to 1.019 MeV. In the lateral dose profile, increasing the beam radial width from 0 mm to 4 mm at the full width at half maximum resulted in a dose decrease of 8.42% on the average. The profile also decreased by 4.81% when the mean energy was increased from 5.6 MeV to 6.4 MeV. Of all tested parameters, electron mean energy had the greatest influence on dose distribution. The PDD and profile were calculated using parameters optimized and compared with the golden beam data. The maximum dose difference was assessed as less than 2%. Conclusion: The relationship between the initial electron and treatment beam quality investigated in this study can be used in Monte Carlo commissioning of medical linear accelerator model.

• Nuclear Engineering and Technology
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• v.55 no.7
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• pp.2474-2482
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• 2023

The development of fast reactors with complex designs and operation status requires more accurate and effective simulation. The Monte-Carlo method can generate multi-group cross-sections in arbitrary geometry without approximation on resonances treatment and leads to good results in combination with diffusion codes. However, in previous studies, the coupling of Monte-Carlo generated multi-group cross-sections (MC-MGXS) and transport solvers has shown relatively large biases in fast reactor problems. In this paper, the main contribution to the biases is proved to be the neglect of the angle-dependence of the total cross-sections. The flux-moment homogenization technique (MHT) is proposed to take into account this dependence. In this method, the angular dependence is attributed to the transfer cross-sections, keeping an independent form for the total sections. For the MET-1000 benchmark, the multi-group transport simulation results with MC-MGXS generated with MHT are improved by 700 pcm and an additional 120 pcm with higher order scattering. The factors that cause the residual bias are discussed. The core power distribution bias is also significantly reduced when MHT is used. It proves that the MCMGXS with MHT can be applicable with transport solvers in fast reactor analysis.

• Nuclear Engineering and Technology
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• v.48 no.3
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• pp.635-641
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• 2016

Because of prohibitive data storage requirements in large-scale simulations, the memory problem is an obstacle for Monte Carlo (MC) codes in accomplishing pin-wise three-dimensional (3D) full-core calculations, particularly for whole-core depletion analyses. Various kinds of data are evaluated and quantificational total memory requirements are analyzed based on the Reactor Monte Carlo (RMC) code, showing that tally data, material data, and isotope densities in depletion are three major parts of memory storage. The domain decomposition method is investigated as a means of saving memory, by dividing spatial geometry into domains that are simulated separately by parallel processors. For the validity of particle tracking during transport simulations, particles need to be communicated between domains. In consideration of efficiency, an asynchronous particle communication algorithm is designed and implemented. Furthermore, we couple the domain decomposition method with MC burnup process, under a strategy of utilizing consistent domain partition in both transport and depletion modules. A numerical test of 3D full-core burnup calculations is carried out, indicating that the RMC code, with the domain decomposition method, is capable of pin-wise full-core burnup calculations with millions of depletion regions.

• Nuclear Engineering and Technology
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• v.53 no.12
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• pp.4098-4105
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• 2021

The aim of this study is to calculate the JO-IMRT dose distributions based on the AAPM TG-119 using Monte Carlo (MC) simulation and Prowess Panther treatment planning system (TPS) (Panther, Prowess Inc., Chico, CA). JO-IMRT dose distributions of AAPM TG-119 were calculated by the TPS and were recalculated by MC simulation. The DVHs and 3D gamma index using global methods implemented in the PTW-VeriSoft with 3%/3 mm were used for evaluation. JO-IMRT dose distributions calculated by TPS and MC were matched the TG-119 goals. The gamma index passing rates with 3%/3 mm were 98.7% for multi-target, 96.0% for mock prostate, 95.4% for mock head-and-neck, and 96.6% for C-shape. The dose in the planning target volumes (PTV) for TPS was larger than that for the MC. The relative dose differences in D99 between TPS and MC for multi-target are 1.52%, 0.17% and 1.40%, for the center, superior and inferior, respectively. The differences in D95 are 0.16% for C-shape; and 0.06% for mock prostate. Mock head-and-neck difference is 0.40% in D99. In contrast, the organ curve for TPS tended to be smaller than MC values. JO-IMRT dose distributions for the AAPM TG-119 calculated by the TPS agreed well with the MC.