• Proceedings of the Korean Society of Medical Physics Conference
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• 2002.09a
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• pp.116-118
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• 2002

It has been noted that Monte Carlo simulations are the most accurate method to calculate dose distributions in any material and geometry. Monte Carlo transport algorithms determine the absorbed dose by following the path of representative particles as they travel through the medium. Accurate Monte Carlo dose calculations rely on detailed modeling of the radiation source. We modeled the effects of beam modifiers such as collimators, blocks, wedges, etc. of our accelerator, Varian Clinac 600C/D to ensure accurate representation of the radiation source using the EGSnrc based BEAM code. These were used in the EGSnrc based DOSXYZ code for the simulation of particles transport through a voxel based Cartesian coordinate system. Because Monte Carlo methods use particle-by-particle methods to simulate a radiation transport, more particle histories yield the better representation of the actual dose. But the prohibitively long time required to get high resolution and accuracy calculations has prevented the use of Monte Carlo methods in the actual clinical spots. Our ultimate aim is to develop a Monte Carlo dose calculation system designed specifically for radiation therapy planning, which is distinguished from current dose calculation methods. The purpose of this study in the present phase was to get dose calculation results corresponding to measurements within practical time limit. We used parallel processing and some variance reduction techniques, therefore reduced the computational time, preserving a good agreement between calculations of depth dose distributions and measurements within 5% deviations.

• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.541-562
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• 2000

The generalized linear mixed model framework is for handling count-type categorical data as well as for clustered or overdispersed non-Gaussian data, or for non-linear model data. In this study, we review its general formulation and estimation methods, based on quasi-likelihood and Monte-Carlo techniques. The current research areas and topics for further development are also mentioned.

• Architectural research
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• v.8 no.2
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• pp.27-36
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• 2006

Structural uncertainties are generally modeled using probabilistic approaches in order to quantify uncertainties in behaviors of structures. This uncertainty results from the uncertainties of structural parameters. Monte Carlo methods have been usually carried out for analyses of uncertainty problems where no analytical expression is available for the forward relationship between data and model parameters. In such cases any direct mathematical treatment is impossible, however the forward relation materializes itself as an algorithm allowing data to be calculated for any given model. This study addresses a new method which is utilized as a basis for the uncertainty estimates of structural responses. It applies double uniform random numbers (i.e. DURN technique) to conventional Monte Carlo algorithm. In DURN method, the scenarios of uncertainties are sequentially selected and executed in its simulation. Numerical examples demonstrate the beneficial effect that the technique can increase uncertainty degree of structural properties with maintaining structural stability and safety up to the limit point of a breakdown of structural systems.

• Proceedings of the KIEE Conference
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• 2003.07a
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• pp.636-638
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• 2003

This paper presents a new approach which is able to determine the reasonable incentive levels of direct load control using sequential Monte Carlo simulation techniques. The economic analysis needs to determine the reasonable incentive level. However, the conventional methods have been based on the scenario methods because they had not considered all cases of the direct load control situations. To overcome there problems, this paper proposes a new technique using sequential Monte Carlo simulation. The Monte Carlo method is a simple and flexible tool to consider large scale systems and complex models for the components of the system. To show its effectiveness, numerical studies were performed to indicate the possible applications of the proposed technique.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.56 no.1
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• pp.1-5
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• 2007

Study on the electron transport coefficient in mixtures of CF4 and Ar, have been analyzed over a range of the reduced electric field strength between 0.1 and 350[Td] by the two-term approximation of the Boltzmann equation (BEq.) method and the Monte Carlo simulation (MCS). 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 $CF_4$ and Ar, were used. The differences of the transport coefficients of electrons in $CF_4$ mixtures of Ar, have been explained by the deduced energy distribution functions for electrons and the complete collision cross-sections for electrons. The results of the Boltzmann equation and the Monte Carlo simulation have been compared with the data presented by several workers. The deduced transport coefficients for electrons agree reasonably well with the experimental and simulation data obtained by Nakamura and Hayashi. The energy distribution function of electrons in $CF_4-Ar$ mixtures shows the Maxwellian distribution for energy. That is, $f({\varepsilon})$ has the symmetrical shape whose axis of symmetry is a most probably energy. The proposed theoretical simulation techniques in this work will be useful to predict the fundamental process of charged particles and the breakdown properties of gas mixtures. A two-term approximation of the Boltzmann equation analysis and Monte Carlo simulation have been used to study electron transport coefficients.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.53 no.2
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• pp.121-128
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• 2004

This paper presents a new approach for determining an accurate incentive levels of Direct Load Control (DLC) program using sequential Monte Carlo Simulation (MCS) techniques. The economic analysis of DLC resources needs to identify the hourly-by-hourly expected energy-not-served resulting from the random outage characteristics of generators as well as to reflect the availability and duration of DLC resources, which results the computational explosion. Therefore, the conventional methods are based on the scenario approaches to reduce the computation time as well as to avoid the complexity of economic studies. In this paper, we have developed a new technique based on the sequential MCS to evaluate the required expected load control amount in each hour and to decide the incentive level satisfying the economic constraints. In addition, the mathematical formulation for DLC programs' economic evaluations are developed. To show the efficiency and effectiveness of the suggested method, the numerical studies have been performed for the modified IEEE reliability test system.

• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1114-1116
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• 1999

This paper presents a new algorithm which evaluates production cost and reliability indices under various constraints of the thermal generation system. In order to consider the operational constraints of thermal units effectively, the proposed algorithm is based on Monte Carlo techniques instead of analytical ones which have difficulty in modelling the units with additional constraints. At that point, generating units are modelled into two types, base load units and peaking units. These generating unit models are used in state duration sampling simulation for which approach can readily consider the peaking unit operating cycles and easily calculates frequency-duration indices. The proposed production simulation algorithm is applied to the IEEE Reliability Test System, and performs the production simulation under the given constraints. The results show that the proposed algorithm is accurate, reliable and useful.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.122-128
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• 2013

Computer simulations designed to predict technical and financial returns of wind turbine installations are used to make informed investment decisions. These simulations used fixed values to represent real-world variables, while the actual projects can be highly uncertain, resulting in predictions that are less accurate and less useful. In this article, by modifying a popular wind power simulation sourced from the American Wind Energy Association to use Monte Carlo techniques in its calculations, the authors have proposed a way to improve simulation usability by producing probability distributions of likely outcomes, which can be used to draw broader, more useful conclusions about the simulated project.

• Journal of Soil and Groundwater Environment
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• v.20 no.3
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• pp.51-64
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• 2015

In the present study, we develop two history matching techniques based on Markov chain Monte Carlo method where radial basis function and Gaussian distribution generated by unconditional geostatistical simulation are employed as the random walk transition kernels. The Bayesian inverse methods for aquifer characterization as the developed models can be effectively applied to the condition even when the targeted information such as hydraulic conductivity is absent and there are transient hydraulic head records due to imposed stress at observation wells. The model which uses unconditional simulation as random walk transition kernel has advantage in that spatial statistics can be directly associated with the predictions. The model using radial basis function network shares the same advantages as the model with unconditional simulation, yet the radial basis function network based the model does not require external geostatistical techniques. Also, by employing radial basis function as transition kernel, multi-scale nested structures can be rigorously addressed. In the validations of the developed models, the overall predictabilities of both models are sound by showing high correlation coefficient between the reference and the predicted. In terms of the model performance, the model with radial basis function network has higher error reduction rate and computational efficiency than with unconditional geostatistical simulation.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.887-889
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• 1996

This paper presents Variance Reduction Techniques of the Monte Carlo Simulation considering Non-Exponential Distribution for Power System Reliability Evaluation. Generally, the components consisting of power system are assumed to be exponentially distributed in their state residence time. Sometimes, however, this assumption may cause a lot of errors in the reliability index evaluation. Non-exponential distribution can be approximated by a sum of several Erlangian distributions, whose inverse transform is easily calculated by using composition method. This paper proposes a new approach to deal with the non-exponential distribution and to reduce the simulation time by virtue of Variance Reduction Techniques such as Control Variate and Antithetic Variate.