• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.24 no.1
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• pp.79-84
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• 2020

In this article, we implement a recently developed fast Monte Carlo simulation (MCS) for pricing equity-linked securities (ELS), which is most commonly issued autocallable structured financial derivative in South Korea, on the mobile platform. The fast MCS is based on Brownian bridge technique. Although mobile platform devices are easy to carry around, mobile platform devices are slow in computation compared to desktop computers. Therefore, it is essential to use a fast algorithm for pricing ELS on the mobile platform. The computational results demonstrate the practicability of Android application implementation for pricing ELS.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2005.07a
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• pp.578-580
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• 2005

Energy distribution function for electrons in SF6-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 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.

• 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.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.10
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• pp.847-850
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• 2002

Stochastic process analysis is often based on Monte Carlo simulations. As a more rigorous alternative, a deterministic algorithm based on numerical integration is proposed in this paper. which calculates the probability distributions of dependent random variables using the results of simulation with grid points of independent random variables. For performance evaluation, the proposed algorithm is applied to an example problem which can be analytically solved. and the result is compared with that of Monte Carlo simulation. The proposed algorithm is suitable for general process simulation problems with a few independent random variables, and expected to be applicable to areas such as safety analysis and quality control.

• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.704-710
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• 2018

For the optimal design of electromagnetic device involving uncertainties in design variables, this paper proposes a new reliability-based optimal design algorithm for multiple constraints problems. Through optimizing the nominal objective function and maximizing the minimum reliability, a set of global optimal reliable solutions representing different reliability levels are obtained by the multi-objective particle swarm optimization algorithm. Applying the sensitivity-assisted Monte Carlo simulation method, the numerical efficiency of optimization procedure is guaranteed. The proposed reliability-based algorithm supplying multi-reliable solutions is investigated through applications to analytic examples and the optimal design of two electromagnetic problems.

• Science of Emotion and Sensibility
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• v.7 no.3
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• pp.1-6
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• 2004

The algorithm developed in this paper allows us to generate or synthesize a large amount of data sets using only a small amount of signal features obtained from the original data set. Because the simulated density profiles of yarns retain the original features without a significant loss of information on the location of imperfections, the resulting fabric images are likely to resemble the original images. The data expansion system developed could generate a large area of fabric images by combining the Monte Carlo simulation and the wavelet sub-band exchange algorithm developed. The system has proven effective for simulating realistic fabric images by retaining the location of imperfections such as neps, thin and thick places.

• Nuclear Engineering and Technology
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• v.35 no.1
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• pp.55-63
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• 2003

Threshold porosity above which fission gas release channels would be formed in the rim egion of high burnup UO$_2$ fuel was estimated by the Monte Carlo method and Hoshen-Kopelman algorithm. With the assumption that both rim pore and rim grain can be represented by cube, pore distribution in the rim was simulated 3-dimensionally by the Monte Carlo method according to porosity and pore size distribution. Then, using the Hoshen-Kopelman algorithm, the fraction of open rim pores interlinked to the outer surface of a fuel pellet was derived as a function of rim porosity. The simulation showed that porosity of 24-25% is the threshold above which the number of rim pores forming release channels increases very rapidly. On the other hand, channels would not be formed if the porosity is less than about 23.5%. This is consistent with the observation that, for porosity less than 23.5%, almost no fission gas is released in the rim. However, once the rim porosity reaches beyond 25%, extensive open paths would be developed and considerable fission gas release would start in the rim.

• Journal of Satellite, Information and Communications
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• v.9 no.4
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• pp.104-110
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• 2014

This paper considers two reconstruction schemes of structured-sparse signals, turbo inference and Markov chain Monte Carlo (MCMC) inference, in compressed sensing(CS) technique that is recently getting an important issue for an efficient video wireless transmission system using multi-copter as an unmanned aerial vehicle. Proposed reconstruction algorithms are setting importance on reduction of image data sizes, fast reconstruction speed and errorless reconstruction. As a result of experimentation with twenty kinds of images, we can find turbo reconstruction algorithm based on loopy belief propagation(BP) has more excellent performances than MCMC algorithm based on Gibbs sampling as aspects of average reconstruction computation time, normalized mean squared error(NMSE) values.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.10
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• pp.3355-3372
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• 2022

Composing music is an inspired yet challenging task, in that the process involves many considerations such as assigning pitches, determining rhythm, and arranging accompaniment. Algorithmic composition aims to develop algorithms for music composition. Recently, algorithmic composition using artificial intelligence technologies received considerable attention. In particular, computational intelligence is widely used and achieves promising results in the creation of music. This paper attempts to provide a survey on the music generation based on the Monte Carlo (MC) algorithm. First, transform the MIDI music format files to digital data. Among these data, use the logistic fitting method to fit the time series, obtain the time distribution regular pattern. Except for time series, the converted data also includes duration, pitch, and velocity. Second, using MC simulation to deal with them summed up their distribution law respectively. The two main control parameters are the value of discrete sampling and standard deviation. Processing the above parameters and converting the data to MIDI file, then compared with the output generated by LSTM neural network, evaluate the music comprehensively.

• Journal of the Korea Computer Graphics Society
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• v.30 no.2
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• pp.1-9
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• 2024

In this paper, we propose a new method to visualize different vector field patterns from density data. We use moving least squares (MLS), which is used in physics-based simulations and geometric processing. However, typical MLS does not take into account the nature of density, as it is interpolated to a higher order through vector-based constraints. In this paper, we design an algorithm that incorporates Monte Carlo-based weights into the MLS to efficiently account for the density characteristics implicit in the input data, allowing the algorithm to represent different forms of white noise. As a result, we experimentally demonstrate detailed vector fields that are difficult to represent using existing techniques such as naive MLS and divergence-constrained MLS.