• Journal of the Korean Society for Precision Engineering
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• v.21 no.12
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• pp.109-118
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• 2004

Life cycle assessment (LCA) usually involves some uncertainty. These uncertainties are generally divided in two categories such lack of data and data inaccuracy in life cycle inventory (LCI). This paper explo.es a methodology on dealing with uncertainty due to lack of data in LCI. In order to treat uncertainty of LCI data, a model for data uncertainty is proposed. The model works with probabilistic curves as inputs and with Monte Carlo Simulation techniques to propagate uncertainty. The probabilistic curves were derived from the results of survey in expert network and Monte Carlo Simulation was performed using the derived probabilistic curves. The results of Monte Carlo Simulation were verified by statistical test. The proposed approach should serve as a guide to improve data quality and deal with uncertainty of LCI data in LCA projects.

• Structural Engineering and Mechanics
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• v.34 no.4
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• pp.417-447
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• 2010

The uncertainty often observed in experimental strengths of masonry constituents makes critical the selection of the appropriate inputs in finite element analysis of complex masonry buildings, as well as requires modelling the building ultimate load as a random variable. On the other hand, the utilization of expensive Monte Carlo simulations to estimate collapse load probability distributions may become computationally impractical when a single analysis of a complex building requires hours of computer calculations. To reduce the computational cost of Monte Carlo simulations, direct computer calculations can be replaced with inexpensive Response Surface (RS) models. This work investigates the use of RS models in Monte Carlo analysis of complex masonry buildings with random input parameters. The accuracy of the estimated RS models, as well as the good estimations of the collapse load cumulative distributions obtained via polynomial RS models, show how the proposed approach could be a useful tool in problems of technical interest.

• Nuclear Engineering and Technology
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• v.51 no.4
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• pp.954-962
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• 2019

It is well known that the variance of tally is biased in a Monte Carlo calculation based on the power iteration method. Several studies have been conducted to estimate the real variance. Among them, the batch method, which was proposed by Gelbard and Prael, has been utilized actively in many Monte Carlo codes because the method is straightforward, and it is easy to implement the method in the codes. However, there is a problem when utilizing the batch method because the estimated variance varies depending on batch size. Often, the appropriate batch size is not realized before the completion of several Monte Carlo calculations. This study recognizes this shortcoming and addresses it by permitting selection of an appropriate batch size.

• Journal of Information Processing Systems
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• v.17 no.4
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• pp.737-753
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• 2021

As the demand for high-quality rendering for mixed reality, videogame, and simulation has increased, global illumination has been actively researched. Monte Carlo path tracing can realize global illumination and produce photorealistic scenes that include critical effects such as color bleeding, caustics, multiple light, and shadows. If the sampling rate is insufficient, however, the rendered results have a large amount of noise. The most successful approach to eliminating or reducing Monte Carlo noise uses a feature-based filter. It exploits the scene characteristics such as a position within a world coordinate and a shading normal. In general, the techniques are based on the denoised pixel or sample and are computationally expensive. However, the main challenge for all of them is to find the appropriate weights for every feature while preserving the details of the scene. In this paper, we compare the recent algorithms for removing Monte Carlo noise in terms of their performance and quality. We also describe their advantages and disadvantages. As far as we know, this study is the first in the world to compare the artificial intelligence-based denoising methods for Monte Carlo rendering.

• Earthquakes and Structures
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• v.3 no.5
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• pp.719-747
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• 2012

In this paper a novel approach is proposed to address the problem of deriving non-stationary stochastic processes which are compatible in the mean sense with a given (target) response (uniform hazard) spectrum (UHS) as commonly desired in the aseismic structural design regulated by contemporary codes of practice. The appealing feature of the approach is that it is non-iterative and "one-step". This is accomplished by solving a standard over-determined minimization problem in conjunction with appropriate median peak factors. These factors are determined by a plethora of reported new Monte Carlo studies which on their own possess considerable stochastic dynamics merit. In the proposed approach, generation and treatment of samples of the processes individually on a deterministic basis is not required as is the case with the various "two-step" approaches found in the literature addressing the herein considered task. The applicability and usefulness of the approach is demonstrated by furnishing extensive numerical data associated with the elastic design UHS of the current European (EC8) and the Chinese (GB 50011) aseismic code provisions. Purposely, simple and thus attractive from a practical viewpoint, uniformly modulated processes assuming either the Kanai-Tajimi (K-T) or the Clough-Penzien (C-P) spectral form are employed. The Monte Carlo studies yield damping and duration dependent median peak factor spectra, given in a polynomial form, associated with the first passage problem for UHS compatible K-T and C-P uniformly modulated stochastic processes. Hopefully, the herein derived stochastic processes and median peak factor spectra can be used to facilitate the aseismic design of structures regulated by contemporary code provisions in a Monte Carlo simulation-based or stochastic dynamics-based context of analysis.

• Proceedings of the KIEE Conference
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• 2008.11a
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• pp.330-332
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• 2008

본 논문에서는 전력계통의 신뢰도를 평가하는 새로운 패러다임인 확률론에 근거한 신뢰도 평가에 대하여 살펴보았다. 확률론 신뢰도 평가 기법의 적용을 통하여 기존 결정론 접근법에서 다루지 못하였던 전력계통에서 발생하는 여러 가지 불확실성을 고려한 신뢰도 평가가 가능 하였으며 확률 신뢰도 평가 기법 중 시뮬레이션 기반 Monte-Carlo 기법을 적용하여 발전 및 부하의 블확실성까지 고려한 통합적인 신뢰도 평가 틀을 개발하였다. 더하여 개발된 신뢰도 평가 틀을 시험 계통에 적용하여 검증을 수행하였다.

• Publications of The Korean Astronomical Society
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• v.14 no.1
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• pp.23-32
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• 1999

We have developed a Monte Carlo code, which solves the problem of radiative transfer in anisotropically scattering atmosphere. The radiative code is flexible in handlings of the system geometry, the distribution of scattering particles, and the source-particle geometry. This code treats the case of highly forward throwing scattering. As performance tests, we have compared the result of Monte Carlo calculations with that of Quasi-Diffusion method for a spherically symmetric cloud model.

• Journal of the Korean Institute of Telematics and Electronics D
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• v.35D no.3
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• pp.28-38
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• 1998

In our paper is reported a new 3D(dimensional) trajectory split approach with greatly improved efficiency for the Monte Carlo simulation of the 3D profiles of implanted ionand point defect concentrations in single-crystal silicon. This approach has been successfully implemented in our TRICSI Monte Carlo code. Combined with the previously developed model for damage accumalation in our TRICSI code, this model allows phasically based dynamic simulation of 3D profiles over an subsequent process simulation such as diffusion modeling and simulation. A typical time saving of over 10 timeshas been achieved for 3D simulation. Our method ensures much better region aground the implanted area. For 1-D simulation, the optimized condition for trajectory split has set to 3,000 pseudoparticles with 2 split branches.

• Structural Engineering and Mechanics
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• v.3 no.3
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• pp.201-213
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• 1995

At present Level 2 and importance sampling methods are the main tools used to estimate reliability of structural systems. But sometimes application of these techniques to realistic problems involves certain difficulties. In order to overcome the difficulties it is suggested to use Monte Carlo simulation in combination with two other techniques-extreme value and tail entropy approximations; an appropriate Pearson's curve is fit to represent simulation results. On the basis of this approach an algorithm and computer program for structural reliability estimation are developed. A number of specially chosen numerical examples are considered with the aim of checking the accuracy of the approach and comparing it with the Level 2 and importance sampling methods. The field of application of the approach is revealed.

• Journal of the Korean Statistical Society
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• v.32 no.4
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• pp.411-423
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• 2003

In this paper we develop a method for calculating a probability that a particular generalized variance is the smallest of all the K multivariate normal generalized variances. The method gives a way of comparing K multivariate populations in terms of their dispersion or spread, because the generalized variance is a scalar measure of the overall multivariate scatter. Fully parametric frequentist approach for the probability is intractable and thus a Bayesian method is pursued using a variant of weighted Monte Carlo (WMC) sampling based approach. Necessary theory involved in the method and computation is provided.