• Journal of Astronomy and Space Sciences
• /
• v.14 no.1
• /
• pp.1-17
• /
• 1997

The results of a restricted numerical simulation for the color gradients within globular clusters have been presented. The standard luminosity function of M3 and Salperter's initial mass functions were used to generate model clusters as a fundamental population. Color gradients with the sample clusters for both King and power law cusp models of surface brightness distributions are discussed in the case of using the standard luminosity function. The dependence of color gradients on several parameters for the simulations with Salpter's initial mass functions, such as slope of initial mass functions, cluster ages, metallicities, concentration parameters of King model, and slopes of power law, are also discussed. No significant radial color gradients are shown to the sample clusters which are regenerated by a random number generation technique with various parameters in both of King and power law cusp models of surface brightness distributions. Dynamical mass segregation and stellar evolution of horizontal branch stars and blue stragglers should be included for the general case of model simulations to show the observed radial color gradients within globular clusters.

• Proceedings of the Korea Society for Simulation Conference
• /
• 1999.04a
• /
• pp.70-79
• /
• 1999

Importance Sampling (IS) has been applied to accelerate the occurrence of rare events. However, it has a drawback of effective biasing scheme to make the estimator from IS unbiased. Adaptive Importance Sampling (AIS) employs an estimated sampling distribution of IS to the systems of interest during the course of simulation. We propose Nonparametric Adaptive Importance Sampling (NAIS) technique which is nonparametrically modified version of AIS and test it to estimate a probability of rare event in M/M/1 queueing model. Comparing with classical Monte Carlo simulation, the computational efficiency and variance reductions gained via NAIS are substantial. A possible extension of NAIS regarding with random number generation is also discussed.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.34 no.2
• /
• pp.9-18
• /
• 2011

Vender prioritization process is one of the most critical tasks of production and logistics management for many companies. Determining the most critical criteria for vender prioritization process is a vital means for a purchasing company to improve its supply chain productivity. This study discuss the use of a Fuzzy analytic network process (Fuzzy ANP) model which is an efficient tool to handle the fuzziness of the data involved in deciding the preferences of different criteria which are not independent. Also, the comparison of classical ANP and Fuzzy ANP is described using simulation with triangular distribution random number generation. It is shown that Fuzzy ANP model possesses some attractive properties and could be used as an alternative to the known vender prioritization methods.

• Journal of the Semiconductor & Display Technology
• /
• v.19 no.2
• /
• pp.37-44
• /
• 2020

In improving laser cutting of optical plastic films for mass production of optoelectronics display units, it is important to understand particle contamination over optical film surface due to fume particle generation and dispersion. This numerical study investigates the effects of downward and upward air flow motions on fume particle dispersion around laser cut line. The simulations employ random particle sampling of up to one million fume particles by probabilistic distributions of particle size, ejection velocity and angle, and fume particle dispersion and surface landing are predicted using Basset-Boussinesq-Oseen model of low Reynolds number flows. The numerical results show that downward air flow scatters fume particles of a certain size range farther away from laser cut line and aggravate surface contamination. However, upward air flow pushes fume particles of this size range back toward laser cut line or sucks them up with rising air motion, thus significantly alleviating surface contamination.

• Journal of the Korea Society for Simulation
• /
• v.8 no.3
• /
• pp.77-89
• /
• 1999

Simulating rare events, such as probability of cell loss in ATM networks, machine failure in highly reliable systems, requires huge simulation efforts due to the low chance of occurrence. Importance Sampling (IS) has been applied to accelerate the occurrence of rare events. However, it has a drawback of effective biasing scheme to make the estimator of IS unbiased. Adaptive Importance Sampling (AIS) employs an estimated sampling distribution of IS to the system of interest during the course of simulation. We propose Nonparametric Adaptive Importance Sampling (NAIS) technique which is nonparametrical version of AIS. We test NAIS to estimate a probability of rare event in M/M/1 queueing model. Comparing with classical Monte Carlo simulation, the computational efficiency and variance reductions gained via NAIS are substantial. A possible extension of NAIS regarding with random number generation is also discussed.

• ETRI Journal
• /
• v.41 no.3
• /
• pp.348-357
• /
• 2019

Fog radio access network (F-RAN) architectures provide markedly improved performance compared to conventional approaches. In this paper, an efficient genetic algorithm-based content distribution scheme is proposed that improves the throughput and reduces the transmission delay of a F-RAN. First, an F-RAN system model is presented that includes a certain number of randomly distributed fog access points (F-APs) that cache popular content from cloud and other sources. Second, the problem of efficient content distribution in F-RANs is described. Third, the details of the proposed optimal genetic algorithm-based content distribution scheme are presented. Finally, simulation results are presented that show the performance of the proposed algorithm rapidly approaches the optimal throughput. When compared with the performance of existing random and exhaustive algorithms, that of the proposed method is demonstrably superior.

• Journal of the Semiconductor & Display Technology
• /
• v.20 no.3
• /
• pp.77-82
• /
• 2021

This numerical study focuses on the analysis of fume particle dispersion characteristics over the surface of target workpiece in laser micro-hole machining process. The effects of oblique stagnation flow over fume generating machining point are examined by carrying out a series of three-dimensional random particle simulations along with probabilistic particle generation model and particle drag correlation of low Reynolds number. Present computational model of fume particle dispersion is found to be capable of assessing and quantifying the fume particle contamination in precision hole machining which may influenced by different types of air flow patterns and their flow intensity. The particle size dependence on dispersion distance of fume particles from laser machining point is significant and the effects of increasing flow oblique angle are shown quite differently when slot blowing or slot suction flows are applied in micro-hole machining.

• Journal of the Semiconductor & Display Technology
• /
• v.21 no.2
• /
• pp.1-6
• /
• 2022

This computational investigation of micro-sized particle dispersion concerns the fume particle contamination over target surface in high-precision laser line machining process of semiconductor and display device materials. Employing the random sampling based on probabilistic fume particle generation distributions, the effects of sphericity for nonspherical fume particles are analyzed for the fume particle dispersion and contamination near the laser machining line. The drag coefficient correlation for nonspherical particles in a low Reynolds number regime is selected and utilized for particle trajectory simulations after drag model validation. When compared to the corresponding results by the assumption of spherical fume particles, the sphericity of nonspherical fume particles show much less dispersion and contamination characteristics and it also significantly affects the particle removal rate in a suction air flow patterns.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2023.11a
• /
• pp.214-215
• /
• 2023

오늘날 운영체제나 응용프로그램에서 레이스 컨디션으로 인한 문제가 발생하여 공격에 이용하거나 레이스 컨디션을 기반으로 한 공격을 막기 위한 연구가 진행되고 있다. 그러나 레이스 컨디션이 발생할 때 스레드가 자원에 접근하는 매커니즘을 응용한 보호기법과 관련된 연구는 미흡하다. 이에 본 논문에서는 레이스 컨디션이 발생할 때 스레드가 무작위 순서로 자원에 접근하는 점을 이용해 새로운 난수 생성 방식을 제안한다. 또한 이를 난수 생성 알고리즘을 사용하는 랜덤 모듈과 비교하여 더 안정적인 난수 생성 모듈을 개발할 수 있는 가능성에 대해 알아봤다.

• Journal of KIISE:Software and Applications
• /
• v.36 no.6
• /
• pp.451-461
• /
• 2009

The Adaptive Random Testing(ART) aims to improve the performance of traditional Random Testing(RT) by reducing the number of test cases to find the failure region which is located in the input domain. Such enhancement can be obtained by efficient selection algorithms of test cases. The ART through Iterative Partitioning(IP-ART) is one of ART techniques and it uses an iterative input domain partitioning method to improve the performance of early-versions of ART which have significant drawbacks in computation time. And the IP-ART with Enlarged Input Domain(EIP-ART), an improved version of IP-ART, is known to make additional performance improvement with scalability by expanding to virtual test space beyond real input domain of IP-ART. The EIP-ART algorithm, however, have the drawback of heavy cost of computation time to generate test cases mainly due to the virtual input domain enlargement. For this reason, two algorithms are proposed in this paper to mitigate the computation overhead of the EIP-ART. In the experiments by simulations, the tiling technique of input domain, one of two proposed algorithms, showed significant improvements in terms of computation time and testing performance.