• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.1
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• pp.83-89
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• 2015

Selection of efficient supplier is a very important process as risk or uncertainty of a supply chain and its environment are increasing. Previous deterministic DEA and probabilistic DEAs are very limited to handle various types of risk and uncertainty. In this paper, I propose an improved probabilistic DEA which consists of two steps; Monte Carlo simulation and statistical decision making. The simulation results show that the proposed method is proper to distinguish supplier's performance and provide statistical decision background.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.12 no.1
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• pp.88-93
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• 1999

This paper describes the electron transport characteristics in $SiH_4$ gas calculated for the range of E/n:0.5~300(Td) and Pressure:0.5, 1, 2.5(Torr) by the Monte carlo simulation and Boltzmann equation method using a set of electron collision cross sections determined by the reported results. The motion has been calculated to give swarm parameters for the electron drift velocity, longitudinal and transverse diffusion coefficients, the electron ionization coefficients, characteristics energy and the electron energy distribution function. The electron energy distributions function has been analysed in $SiH_4$ at E/N: 30, 50(Td)for a case of the equilibrium region in the mean electron energy and respective set of electron collision cross sections. The results of Monte carlo simulation and Boltzmann equation have been compared with experimental data by ohmori ad Pollock.

• 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.

• 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 the Korea Academia-Industrial cooperation Society
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• v.12 no.6
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• pp.2770-2776
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• 2011

Mutual Interference scenarios between Wireless Broadband (WiBro) and Wireless LAN (WLAN) in DTV bands are assumed. Co-channel interference and adjacent channel interference are respectively evaluated in terms of carrier to interference ratio (C/I) by using Spectrum Engineering Advanced Monte Carlo Analysis Tool (SEAMCAT) based on the Monte-Carlo simulation method. For the simulation, three frequencies such as 185 MHz, 481 MHz and 687 MHz are chosen. Analysis results indicate that interference situation of using frequency of 185 MHz is the worst case, which requires longer protection distance between WiBro MS and WLAN User Equipment (UE), lower transmit power of WiBro Mobile Station (MS) and WiBro Base Station (BS) and WLAN UE and larger guard band. Comparing to cases of using frequency of 185 MHz and 481 MHz, interference situation of using frequency of 687 MHz is slighter. Therefore, using frequency of 687 MHz is easier for coexistence between WiBro and WLAN. Analysis results can be used as reference and guideline when planning the deployment of WiBro and WLAN in DTV bands.

• Tunnel and Underground Space
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• v.12 no.3
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• pp.189-197
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• 2002

In recent years, the probabilistic analysis has been used in rock slope engineering. This is because uncertainty is pervasive in rock slope engineering and most geometric and geotechnical parameters of discontinuity and rock masses are involved with uncertainty. Whilst the traditional deterministic analysis method fails to properly deal with uncertainty, the probabilistic analysis has advantages quantifying the uncertainty in parameters. As a probabilistic analysis method, the Monte Carlo simulation has been used commonly. However, the Monte Carlo simulation requires many repeated calculations and therefore, needs much effort and time to calculate the probability of failure. In contrast, the point estimate method involves a simple calculation with moments for random variables. In this study the probability of failure in rock slope is evaluated by the point estimate method and the results are compared to the probability of failure obtained by Monte Carlo simulation method.

• Journal of Digital Convergence
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• v.19 no.12
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• pp.339-345
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• 2021

In this paper, estimation angle performance analysis of amplitude-comparison monopulse radar under additive noise effect is dealt with. When uncorrelated white noises are added to the squinted beams, the angle estimation performance is analyzed through the mean square error(MSE). The numerical integration-based mean square error result completely overlaps the Monte Carlo-based mean square error result, which corresponds to 99.8% of the Monte Carlo-based mean square error result. In addition, the mean square error analysis method based on numerical integration has a much faster operation time than the mean square error method based on Monte Carlo. the angle estimation performance of the amplitude comparison monopulse radar can be efficiently analyzed in various noise environments through the proposed numerical integration-based mean square error method.

• Structural Engineering and Mechanics
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• v.63 no.1
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• pp.47-53
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• 2017

The fundamental goal of this study is to minimize the uncertainty of the median fragility curve and to assess the structural vulnerability under earthquake excitation. Bayesian Inference with Markov Chain Monte Carlo (MCMC) simulation has been presented for efficient collapse response assessment of the independent intake water tower. The intake tower is significantly used as a diversion type of the hydropower station for maintaining power plant, reservoir and spillway tunnel. Therefore, the seismic fragility assessment of the intake tower is a pivotal component for estimating total system risk of the reservoir. In this investigation, an asymmetrical independent slender reinforced concrete structure is considered. The Bayesian Inference method provides the flexibility to integrate the prior information of collapse response data with the numerical analysis results. The preliminary information of risk data can be obtained from various sources like experiments, existing studies, and simplified linear dynamic analysis or nonlinear static analysis. The conventional lognormal model is used for plotting the fragility curve using the data from time history simulation and nonlinear static pushover analysis respectively. The Bayesian Inference approach is applied for integrating the data from both analyses with the help of MCMC simulation. The method achieves meaningful improvement of uncertainty associated with the fragility curve, and provides significant statistical and computational efficiency.

• Journal of the Korea Society for Simulation
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• v.25 no.2
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• pp.83-92
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• 2016

Tolerances are defined as the allowable variations in the geometry and positioning of parts in a mechanical assembly for assuring its proper functionality. Tolerance analysis is the activity related to estimating the potential accumulated variation in assemblies. If the estimated variances go out of the specified ranges, it causes the quality problem. Thus, we should adjust the tolerances and this activity is called as tolerance design. In this paper, a case study on the accumulated tolerance analysis and design using Monte Carlo simulation is introduced, which is applied for developing a portable medical device. Using the simulation study, we can improve the assemblability and functionality of the product.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.33 no.4
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• pp.346-352
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• 2009

In the present study, the Weibull statistical analysis using the Monte-Carlo simulation has been performed to investigate the micro-Vickers hardness measurement reliability considering the variability. Experimental indentation test were performed with a micro-Vickers hardness tester for as-received and quenching and tempering specimens in SCM440 steels. The distribution of micro-Vickers hardness is found to be 2-parameter Weibull distribution function. The mean values and coefficients of variation (COV) for both data set are compared with results based on Weibull statistical analysis. Finally, Monte-Carlo simulation was performed in order to evaluate the effect of sample size on the micro-Vickers hardness measurement reliability. For the parent distribution with shape parameter 30.0 and scale parameter 200.0 (COV=0.040), the number of sample data required to obtain the true Weibull parameters was founded by 20. For the parent distribution with shape parameter 10.0 and scale parameter 200.0 (COV=0.1240), the number of sample data required to obtain the true Weibull parameters was founded by 30.