• Communications for Statistical Applications and Methods
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• v.31 no.1
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• pp.65-85
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• 2024

The tail probability of a function of a multivariate random variable is not easy to estimate by the crude Monte Carlo simulation. When the occurrence of the function value over a threshold is rare, the accurate estimation of the corresponding probability requires a huge number of samples. When the explicit form of the cumulative distribution function of each component of the variable is known, the inverse transform likelihood ratio method is directly applicable scheme to estimate the tail probability efficiently. The method is a type of the importance sampling and its efficiency depends on the selection of the importance sampling distribution. When the cumulative distribution of the multivariate random variable is represented by a copula and its marginal distributions, we develop an iterative algorithm to find the optimal importance sampling distribution, and show the convergence of the algorithm. The performance of the proposed scheme is compared with the crude Monte Carlo simulation numerically.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.26 no.9
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• pp.416-423
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• 2014

To verify different boundary conditions on the borehole wall, which are commonly used for generating g-function, the well-known TRNSYS simulation model, DST (Duct STorage), is employed. By letting the fluid circulation determine the borehole wall conditions, a DST-based g-function is induced with numerical processes proposed in this work. A new TRNSYS module is also developed to accommodate g-function data and predict dynamic outlet fluid temperatures. Results showed that the modified g-function, which is different from Eskilson's original g-function, is closer to the DST-based g-function. This implies that the uniform heat transfer rates over the height can be used for good approximation. In fact, simulations with the modified g-function showed similar results as the DST model, while Eskilson g-function case deviated from the DST model as time progressed.

• Water for future
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• v.13 no.4
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• pp.41-50
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• 1980

The prupose of this study are the theoretical examination of Gamma distribution function and its application to hydraulic engineering, that is studying the simulation of monthly streamflow by the Gamma distribtution function model(Gamma Model) based on Monte Carlo technique. In the analysis, monthly streamflow data in the Nak Dong River, the Han River, and the Keum River were used and the data were changed to modular coefficient in order to make the analysis convenient. At first, the fitness of monthly streamflow to 2-Parameter Gamma distribution was tested by Chi-square and Kolmogrov-Smironov test, by which it was found the monthly streamflow were fit well to this Gamma distribution function. Then, the Gamma Model based on the Gamma distribution and Monte Carlo Method was used in the simulation of monthly streamflow, and simulateddata showed that all their stastical characteristics were preserved well in the simulation. Consequently, it can be concluded that the Gamma Model is suitable for the simulation of monthly streamflow series directly by using the Mote Carlo technique.

• The KSFM Journal of Fluid Machinery
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• v.19 no.3
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• pp.33-36
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• 2016

The investigation on the verification of availability simulation for small-scale plant has been carried out. This study focuses on the availability variation induced by number of equipment and iteration with failure density function. The equipment classification of small-scale plant and failure type and the methodologies on Monte-Carlo simulation are established. The availability deviation with programs showed under Max. 1.7% for the case of normal function. This method could be used to availability evaluation of small-scale plant, but calibration of the failure density function is necessary for general application.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.52 no.1
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• pp.9-13
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• 2003

Energy Distribution Function in pure $CH_4$, $CF_4$ and mixtures of $CF_4$ 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 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 measured results and the calculated results have been compared each other.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.12 no.2
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• pp.97-108
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• 2008

In this paper Bayes estimator of the parameter and reliability function of the zero-truncated Poisson distribution are obtained. Furthermore, recurrence relations for the estimator of the parameter are also derived. Monte Carlo simulation technique has been made for comparing the Bayes estimator and reliability function with the corresponding maximum likelihood estimator (MLE) of zero-truncated Poisson distribution.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.456-459
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• 1993

CADET is used to analyze the performance of the missile. Miss distance is calculated for a given lateral fin force saturation level due to the aerodynamic characteristics, target acceleration, and glint and fading noises which is assumed as Gaussian noises. As .alpha.-.betha. filter is studied to attenuate the noises, the results are compared with those of without filter. For the easy simulation, the transfer function of a discrete .alpha.-.betha. filter is converted into the continuous model. Simulation results show that the results of CADET simulation is similar to those of Monte-Carlo simulation. Moreover CADET is the better in computing time demand.

• Proceedings of the KSR Conference
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• 2000.05a
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• pp.116-122
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• 2000

This paper presents a simulation method with constant power model for the train load. In power system simulation loads could be modeled as a constant power, constant current, constant Impedance or a function of voltage and frequency. At this time, however, representing a train load as the function is difficult because of the lack of data. Therefore as a first step, simulation method with a constant power model fer a train is studied, and the test result is compared with the simulation result using the constant Impedance model.

• Structural Engineering and Mechanics
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• v.75 no.4
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• pp.425-434
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• 2020

Practical non-synoptic fluctuating wind often exhibits nonstationary features and should be modeled as nonstationary random processes. Generally, the coherence function of the fluctuating wind field has time-varying characteristics. Some studies have shown that there is a big difference between the fluctuating wind field of the coherent function model with and without time variability. Therefore, it is of significance to simulate nonstationary fluctuating wind field with time-varying coherent function. However, current studies on the numerical simulation of nonstationary fluctuating wind field with time-varying coherence are very limited, and the proposed approaches are usually based on the traditional spectral representation method with low simulation efficiency. Especially, for the simulation of multi-variable wind field of large span structures such as transmission tower-line, not only the simulation is inefficient but also the matrix decomposition may have singularity problem. In this paper, it is proposed to conduct the numerical simulation of nonstationary fluctuating wind field in one-spatial dimension with time-varying coherence based on the wavenumber-frequency spectrum. The simulated multivariable nonstationary wind field with time-varying coherence is transformed into one-dimensional nonstationary random waves in the simulated spatial domain, and the simulation by wavenumber frequency spectrum is derived. So, the proposed simulation method can avoid the complicated Cholesky decomposition. Then, the proper orthogonal decomposition is employed to decompose the time-space dependent evolutionary power spectral density and the Fourier transform of time-varying coherent function, simultaneously, so that the two-dimensional Fast Fourier transform can be applied to further improve the simulation efficiency. Finally, the proposed method is applied to simulate the longitudinal nonstationary fluctuating wind velocity field along the transmission line to illustrate its performances.

• 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.