• Journal of Institute of Control, Robotics and Systems
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• v.17 no.1
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• pp.6-11
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• 2011

This paper proposes a new collision risk assessment system for pedestrians's safety. Monte Carlo Simulation (MCS) method is a one of the most popular method that rely on repeated random sampling to compute their result, and this method is also proper to get the results when it is unfeasible or impossible to compute an exact result. Nevertheless its advantages, it spends much time to calculate the result of some situation, we apply not only MCS but also Neural Networks in this problem. By Monte carlo method, we make some sample data for input of neural networks and by using this data, neural networks can be trained for computing collision probability of whole area where can be measured by sensors. By using this trained networks, we can estimate the collision probability at each positions and velocities with high speed and low error rate. Computer simulations will be shown the validity of our proposed method.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2002.07b
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• pp.878-881
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• 2002

Energy distribution function for electrons in $SF_6+Ar$ mixtures gas used by MCS-BEq algorithm has been analysed over the E/N range 30 ~ 300[Td] by a two term Boltzmann equation and by a Monte Carlo Simulation using a set of electron cross sections determined by other authors, experimentally the electron swarm parameters for 0.2[%] and 0.5[%] $SF_6+Ar$ mixtures were measured by time-of-flight(TOF) method, The results show that the deduced electron drift velocities, the electron ionization or attachment coefficients, longitudinal and transverse diffusion coefficients and mean energy agree reasonably well with theoretical for a rang of E/N values. The results obtained from Boltzmann equation method and Monte Carlo simulation have been compared with present and previously obtained data and respective set of electron collision cross sections of the molecules.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.07b
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• pp.1150-1153
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• 2004

Diffusion coefficients Of electrons in $SF_6$-Ar mixtures gas used by MCS- BEq algorithm has been analysed over the E/N range $30\sim300$(Td) by a two term Boltzmann equation and by a Monte Carlo Simulation using a set of electron cross sections determined by other authors, experimentally the electron swarm parameters for 0.2[%] and 0.5[%] $SF_6$-Ar mixtures were measured by time-of-flight(TOF) method, The results show that the deduced electron drift velocities, the electron ionization or attachment coefficients, longitudinal and transverse diffusion coefficients and mean energy agree reasonably well with theoretical for a rang of E/N values The results obtained from Boltzmann equation method and Monte Carlo simulation have been compared with present and previously obtained data and respective set of electron collision cross sections of the molecules.

• Journal of Electrical Engineering and Technology
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• v.8 no.2
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• pp.331-338
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• 2013

A new reliability calculation method is proposed based on design sensitivity analysis by the finite element method for nonlinear performance constraints in the optimal design of electromagnetic devices. In the proposed method, the reliability of a given design is calculated by using the Monte Carlo simulation (MCS) method after approximating a constraint function to a linear one in the confidence interval with the help of its sensitivity information. The validity and numerical efficiency of the proposed sensitivity-assisted MCS method are investigated by comparing its numerical results with those obtained by using the conventional MCS method and the first-order reliability method for analytic functions and the TEAM Workshop Problem 22.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 1999.05a
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• pp.696-699
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• 1999

The electron transport coefficients in $SF_6+N_2$ gas is analysed in range of E/N values from 100~900(Td) by a Monte Carlo simulation and Boltzmann method, using a set of electron collision cross sections determined by the authors. The result of the Monte Carlo simulation such as electron drift velocity, ionization and electron attachment coefficients, longitudinal and transverse diffusion coefficients in nearly agreement with the respective experimental and theoretical for a range of E/N.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.61 no.1
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• pp.13-17
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• 2012

Ionization and Attachment Coefficients 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 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 $CH_4$, $CF_4$ and Ar, were used. 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.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.64 no.4
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• pp.241-245
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• 2015

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 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 $CH_4$, $CF_4$ and Ar, were used. The differences of the transport coefficients of electrons in $CH_4$, mixtures of $CH_4$ and 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.

• Journal of Mechanical Science and Technology
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• v.20 no.12
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• pp.2124-2135
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• 2006

In this paper, the reliability estimation of pipelines is performed by employing the probabilistic method, which accounts for the uncertainties in the load and resistance parameters of the limit state function. The FORM (first order reliability method) and the SORM (second order reliability method) are carried out to estimate the failure probability of pipeline utilizing the FAD (failure assessment diagram). And the reliability of pipeline is assessed by using this failure probability and analyzed in accordance with a target safety level. Furthermore, the MCS (Monte Carlo Simulation) is used to verify the results of the FORM and the SORM. It is noted that the failure probability increases with the increase of dent depth, gouge depth, operating pressure, outside radius, and the decrease of wall thickness. It is found that the FORM utilizing the FAD is a useful and is an efficient method to estimate the failure probability in the reliability assessment of a pipeline. Furthermore, the pipeline safety assessment technique with the deterministic procedure utilizing the FAD only is turned out more conservative than those obtained by using the probability theory together with the FAD. The probabilistic method such as the FORM, the SORM and the MCS can be used by most plant designers regarding the operating condition and design parameters.

• Journal of the Korean Geotechnical Society
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• v.32 no.11
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• pp.31-42
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• 2016

Usually the seismic stability analysis of slope uses the pseudostatic analysis considering the inertial force by the earthquake as a static load. Geostructures such as slope include the uncertainty of soil properties. Therefore, it is necessary to consider probabilistic method for stability analysis. In this study, the probabilistic stability analysis of slope considering the uncertainty of soil properties has been performed. The fragility curve that represents the probability of exceeding limit state of slope as a function of the ground motion has been established. The Monte Carlo Simulation (MCS) has been implemented to perform the probabilistic stability analysis of slope with pseudostatic analysis. A procedure to develop the fragility curve by the pseudostatic horizontal acceleration has been presented by calculating the probability of failure based on the Artificial Neural Network (ANN) based response surface technique that reduces the required time of MCS. The results showed that the proposed method can get the fragility curve that is similar to the direct MCS-based fragility curve, and can be efficiently used to reduce the analysis time.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1027-1028
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• 2011

In this paper, three reliability calculation algorithms: Monte Carlo Simulation (MCS), Reliability Index Approach (RIA), and Sensitivity-based Monte Carlo Simulation (SMCS) are studied. Their efficiency and accuracy are validated by analytic test functions.