• Journal of the Korean Institute of Telematics and Electronics A
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• v.30A no.8
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• pp.63-71
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• 1993

To verify validity of the steady-state Nyquist theorem and the steady-state Nyquist theorem with hot carrier effects in semiconductor devices, we calculate thermal noise in n-i-n structures using both the steady-state Nyquist theorem and the Monte-Carlo method, and compare the results from these two-methods. When the carrier temperature is not far from the lattice temperature, the results from both methods agree with each other very well, but in the hot carrier regime there are some discrepancies. Our results support the argument that for MOSFETs and MESFETs operating in the linear region, the channel thermal noise should be explained by the steady-state Nyquist theorem rather than by the existing theories.

• Korean Management Science Review
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• v.19 no.1
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• pp.117-128
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• 2002

In this Paper we investigate the performance of the five efficiency estimation methods which include the stochastic frontier model estimated by maximum likelihood (SFML), the stochastic frontier model estimated by corrected ordinary least squares (SFCOLS), the data envelopment analysis (DIA) model, the combined estimation of SFML and DEA (SFML + DEA), and the combined estimation of SFCOLS arid DIA (SFCOLS+ DEA) using Monte Carlo analysis. The results include: 1) SFML provides most accurate efficiency estimates for the sample sloe 150 or over,2) SFML+DEAor SFCOLS + DIA Perform better for the cases with sample sloe 25, 50, and low random errors, 3) SFCOLS performs better for the close with sample sloe 25, 50, and very high random errors.

• Proceedings of the KIEE Conference
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• 2001.07a
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• pp.530-532
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• 2001

This paper illustrates a method for evaluating nodal probabilistic production cost using the CMELDC. A new method for constructing CMELDC(the equivalent load duration curves of composite power system) was developed by authors. The CMELDC can be obtained by convolution integral processing between the probability distribution functions of the fictitious generators outage capacity and the load duration curves at each load point. Monte Carlo Methods are applied for the construction of CMELDC on this study. And IEEE-RTS 24 buses model is used as our case study with satisfactory results.

• Structural Engineering and Mechanics
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• v.4 no.6
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• pp.703-715
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• 1996

In stochastic analysis, the randomness of the structural parameters is taken into consideration and the response variability is obtained in addition to the conventional (mean) response. In the present paper the structural response variability of plate structure is calculated using the weighted integral method and is compared with the results obtained by different methods. The stochastic field is assumed to be normally distributed and to have the homogeneity. The decomposition of strain-displacement matrix enabled us to extend the formulation to the stochastic analysis with the quadratic elements in the weighted integral method. A new auto-correlation function is derived considering the uncertainty of plate thickness. The results obtained in the numerical examples by two different methods, i.e., weighted integral method and Monte Carlo simulation, are in a close agreement. In the case of the variable plate thickness, the obtained results are in good agreement with those of Lawrence and Monte Carlo simulation.

• Nuclear Engineering and Technology
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• v.49 no.6
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• pp.1172-1180
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• 2017

The application of Monte Carlo (MC) to large-scale fixed-source problems has recently become possible with new hybrid methods that automate generation of parameters for variance reduction techniques. Two common variance reduction techniques, weight windows and source biasing, have been automated and popularized by the consistent adjoint-driven importance sampling (CADIS) method. This method uses the adjoint solution from an inexpensive deterministic calculation to define a consistent set of weight windows and source particles for a subsequent MC calculation. One of the motivations for source consistency is to avoid the splitting or rouletting of particles at birth, which requires computational resources. However, it is not always possible or desirable to implement such consistency, which results in inconsistent source biasing. This paper develops an original framework that mathematically expresses the coupling of the weight window and source biasing techniques, allowing the authors to explore the impact of inconsistent source sampling on the variance of MC results. A numerical experiment supports this new framework and suggests that certain classes of problems may be relatively insensitive to inconsistent source sampling schemes with moderate levels of splitting and rouletting.

• 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.

• Tunnel and Underground Space
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• v.22 no.2
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• pp.86-92
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• 2012

Point estimate method has a less accuracy than Monte Carlo simulation that is usually considered as an exact probabilistic method, but this method still remains popular in probability-based reliability assessment in geotechnical and rock engineering, because it significantly reduce the number of sampling points and produces the statistical moments of a performance function in a reasonable accuracy. In the present study, we investigated the accuracy and applicability of point estimate methods proposed by Rosenblueth and Zhou & Nowak by comparing the results of these two methods with those of Monte Carlo simulations. The comparison was carried out for the problem of a lined circular tunnel in an elastic medium where an closed-form analytical solution is given. The comparison results showed that despite the non-linearity of the analytical solution, the statistical moments calculated by the point estimate methods and the Monte Carlo simulations agreed well with an average error of roughly 1-2%. This average error demonstrates the applicability of the two point estimate methods for the probabilistic reliability assessment of underground structures in combination with numerical analysis.

• Nuclear Engineering and Technology
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• v.56 no.7
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• pp.2748-2755
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• 2024

This paper presents two new methods for variance reduction for shielding calculation in Monte Carlo radiation transport. One method is CADIS-NEE, which combines Consistent Adjoint Driven Importance Sampling (CADIS) and next-event estimator (NEE) methods to increase the calculation efficiency of tallies at points. The other is CADIS-deterministic transport (DXTRAN), which combines CADIS and DXTRAN to obtain higher performance than using CADIS and DXTRAN separately. The combination processes are derived and implemented in the hybrid Monte-Carlo-Deterministic particle-transport code NECP-MCX. Various problems are tested to demonstrate the effectiveness of the two methods. According to the results, the two combination methods have higher efficiency than using CADIS, NEE or DXTRAN separately. In a long-distance photon-transport problem, CADIS-NEE converges faster than NEE and the figure of merit (FOM) of CADIS-NEE is 75.6 times of NEE. In a labyrinthine problem, CADIS-DXTRAN's FOM surpasses that of DXTRAN and CADIS by a factor of 45.3 and 17.7, respectively. Therefore, it is advisable to employ these two novel methods selectively in appropriate scenarios to reduce variance.

• Communications for Statistical Applications and Methods
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• v.9 no.1
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• pp.115-128
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• 2002

Hierarchical models are widely used for inference on correlated parameters as a compromise between underfitting and overfilling problems. In this paper, we take a Bayesian approach to analyzing hierarchical models and suggest a Markov chain Monte Carlo methods to get around computational difficulties in Bayesian analysis of the hierarchical models. We apply the method to a real data on smoking and lung cancer which are collected from cities in China.

• Journal of Preventive Medicine and Public Health
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• v.55 no.1
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• pp.28-36
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• 2022

The current multimorbidity correction method in the Global Burden of Disease studies assumes the independent occurrence of diseases. Those studies use Monte-Carlo simulations to adjust for the presence of multiple disease conditions for all diseases. The present study investigated whether the above-mentioned assumption is reasonable based on the prevalence confirmed from actual data. This study compared multimorbidity-adjusted years of lived with disability (YLD) obtained by Monte-Carlo simulations and multimorbidity-adjusted YLD using multimorbidity prevalence derived from National Health Insurance Service data. The 5 most common diseases by sex and age groups were selected as diseases of interest. No significant differences were found between YLD estimations made using actual data and Monte-Carlo simulations, even though assumptions about the independent occurrence of diseases should be carefully applied. The prevalence was not well reflected according to disease characteristics in those under the age of 30, among whom there was a difference in YLD between the 2 methods. Therefore, when calculating the burden of diseases for Koreans over the age of 30, it is possible to calculate the YLD with correction for multimorbidity through Monte-Carlo simulation, but care should be taken with under-30s. It is useful to apply the efficiency and suitability of calibration for multiplicative methods using Monte-Carlo simulations in research on the domestic disease burden, especially in adults in their 30s and older. Further research should be carried out on multimorbidity correction methodology according to the characteristics of multiple diseases by sex and age.