• Journal of Korean Institute of Industrial Engineers
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• v.27 no.4
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• pp.337-344
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• 2001

As cost invested in developing the specified technology is increasing, investors are paying more attention to cost to benefit analysis (CBA). One of the basic elements of CBA for new technological development is the diffusion pattern of demand of such technology. Many studies of technology evaluation have adopted a single generation model to simulate the diffusion pattern of demand. This approach, however, considers the diffusion of the new technology itself, not taking into account a newer generation that can replace the one just invented. In this paper, we show how a multi-generation technology diffusion model can be applied for more accurate CBA for information technology. Monte Carlo simulation is performed to find influential factors on the CBA of a Cybernetic Building System.

• 유체기계공업학회:학술대회논문집
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• 2000.12a
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• pp.82-87
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• 2000

The direct simulation Monte Carlo (DSMC) method is applied to investigate the flow field of a disk-type drag pump. The pumping channels are cut on both sides of a rotating disk. The rotor has 10 Archimedes' spiral blades. In the present DSMC method, the variable hard sphere model is used as a molecular model, and the no time counter method is employed as a collision sampling technique. For simulation of diatomic gas flows, the Larsen-Borgnakke phenomenological model is adopted to redistribute the translational and internal energies.

• Journal of the Semiconductor & Display Technology
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• v.6 no.3
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• pp.25-33
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• 2007

A computationally efficient and accurate Monte Carlo (MC) simulator of electron beam lithography process, which is named SCNU-EBL, has been developed for semiconductor nanometer pattern design and fabrication. The simulator is composed of a MC simulation model of electron trajectory into solid targets, an Gaussian-beam exposure simulation model, and a development simulation model of photoresist using a string model. Especially for the trajectories of incident electrons into the solid targets, the inner-shell electron scattering of an target atom and its discrete energy loss with an incident electron is efficiently modeled for multi-layer resists and heterogeneous multi-layer targets. The simulator was newly applied to the development profile simulation of ZEP520 positive photoresist for NGL(Next-Generation Lithography). The simulation of ZEP520 for electron-beam nanolithography gave a reasonable agreement with the SEM experiments of ZEP520 photoresist.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.1
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• pp.41-48
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• 2014

This paper analyzes the Monte Carlo localization (MCL) method, which estimates the pose of an indoor mobile robot. A mobile robot must know where it is to navigate in an indoor environment. The MCL technique is one of the most influential and popular techniques for estimation of robot position and orientation using a particle filter. For the analysis, we perform experiments in an indoor environment with a differential drive robot and ultrasonic range sensor system. The analysis uses MATLAB for implementation of the MCL and investigates the effects of the control parameters on the MCL performance. The control parameters are the uncertainty of the motion model of the mobile robot and the noise level of the measurement model of the range sensor.

• Nuclear Engineering and Technology
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• v.43 no.6
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• pp.523-530
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• 2011

In a previous study, the stochastic space-dependent kinetics model (SSKM) based on the forward stochastic model in stochastic kinetics theory and the Ito stochastic differential equations was proposed for treating monoenergetic space-time nuclear reactor kinetics in one dimension. The SSKM was tested against analog Monte Carlo calculations, however, for exemplary cases of homogeneous slab reactors with only one delayed-neutron precursor group. In this paper, the SSKM is improved and evaluated with more realistic and complicated cases regarding several delayed-neutron precursor groups and heterogeneous slab reactors in which the extraneous source or reactivity can be introduced locally. Furthermore, the source level and the initial conditions will also be adjusted to investigate the trends in the variances of the neutron population and fission product levels across the reactor. The results indicate that the improved SSKM is in good agreement with the Monte Carlo method and show how the variances in population dynamics can be controlled.

• Communications for Statistical Applications and Methods
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• v.29 no.5
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• pp.513-532
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• 2022

In this paper, the use of Bayes factors and the deviance information criterion for model selection are compared in a Bayesian accelerated life testing setup. In Bayesian accelerated life testing, the most used tool for model comparison is the deviance information criterion. An alternative and more formal approach is to use Bayes factors to compare models. However, Bayesian accelerated life testing models with more than one stressor often have mathematically intractable posterior distributions and Markov chain Monte Carlo methods are employed to obtain posterior samples to base inference on. The computation of the marginal likelihood is challenging when working with such complex models. In this paper, methods for approximating the marginal likelihood and the application thereof in the accelerated life testing paradigm are explored for dual-stress models. A simulation study is also included, where Bayes factors using the different approximation methods and the deviance information are compared.

• Smart Structures and Systems
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• v.26 no.2
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• pp.175-184
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• 2020

Conventional Monte Carlo simulation-based methods for seismic risk assessment of water networks often require excessive computational time costs due to the hydraulic analysis. In this study, an Artificial Neural Network-based surrogate model was proposed to efficiently evaluate the flow-based system reliability of water distribution networks. The surrogate model was constructed with appropriate training parameters through trial-and-error procedures. Furthermore, a deep neural network with hidden layers and neurons was composed for the high-dimensional network. For network training, the input of the neural network was defined as the damage states of the k-dimensional network facilities, and the output was defined as the network system performance. To generate training data, random sampling was performed between earthquake magnitudes of 5.0 and 7.5, and hydraulic analyses were conducted to evaluate network performance. For a hydraulic simulation, EPANET-based MATLAB code was developed, and a pressure-driven analysis approach was adopted to represent an unsteady-state network. To demonstrate the constructed surrogate model, the actual water distribution network of A-city, South Korea, was adopted, and the network map was reconstructed from the geographic information system data. The surrogate model was able to predict network performance within a 3% relative error at trained epicenters in drastically reduced time. In addition, the accuracy of the surrogate model was estimated to within 3% relative error (5% for network performance lower than 0.2) at different epicenters to verify the robustness of the epicenter location. Therefore, it is concluded that ANN-based surrogate model can be utilized as an alternative model for efficient seismic risk assessment to within 5% of relative error.

• Proceedings of the Korean Nuclear Society Conference
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• 2004.10a
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• pp.67-68
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• 2004

• 한국지구물리탐사학회:학술대회논문집
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• 2011.10a
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• pp.127-130
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• 2011

• The Korean Journal of Applied Statistics
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• v.18 no.1
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• pp.1-13
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• 2005

Latent Class model has been considered recently by many researchers and practitioners as a tool for identifying heterogeneous segments or groups in a population, and grouping objects into the segments. In this paper we consider data on prostate cancer patients from Korean National Cancer Institute and propose a method for grouping prostate cancer patients by using latent class Poisson model. A Bayesian approach equipped with a Markov chain Monte Carlo method is used to overcome the limit of classical likelihood approaches. Advantages of the proposed Bayesian method are easy estimation of parameters with their standard errors, segmentation of objects into groups, and provision of uncertainty measures for the segmentation. In addition, we provide a method to determine an appropriate number of segments for the given data so that the method automatically chooses the number of segments and partitions objects into heterogeneous segments.