• Journal of Korean Institute of Industrial Engineers
• /
• v.19 no.1
• /
• pp.61-71
• /
• 1993

We focus on a way of combining the Monte Calro methods of antithetic variates and control variates to reduce the variance of the estimator of the mean response in a simulation experiment. Combined Method applies antithetic variates (partially) for driving approiate stochastic model components to reduce the vaiance of estimator and utilizes the correlations between the response and control variates. We obtain the variance of the estimator for the response analytically and compare Combined Method with control variates method. We explore the efficiency of this method in reducing the variance of the estimator through the port operations model. Combined Method shows a better performance in reducing the variance of estimator than methods of antithetic variates and control variates in the range from 6% to 8%. The marginal efficiency gain of this method is modest for the example considered. When the effective set of control variates is small, the marginal efficiency gain may increase. Though these results are from the limited experiments, Combined Method could profitably be applied to large-scale simulation models.

• Journal of the Korean Data and Information Science Society
• /
• v.15 no.4
• /
• pp.945-955
• /
• 2004

'Complete-case analysis' is easy to carry out and it may be fine with small amount of missing data. However, this method is not recommended in general because the estimates are usually biased and not efficient. There are numerous alternatives to complete-case analysis. One alternative is the single imputation. Some of the most common single imputation methods are reviewed and the performances are compared by simulation studies.

• KIISE Transactions on Computing Practices
• /
• v.20 no.10
• /
• pp.534-542
• /
• 2014

Multiscale modeling is a new simulation approach which can manage different spatial and temporal scales of system. In this study, as part of multiscale modeling research, we propose the way of combining two different simulation methods, molecular dynamics(MD) and stochastic rotation dynamics(SRD). Our conceptual implementations are based on LAMMPS, one of the well-known molecular dynamics programs. Our prototype of multiscale modeling follows the form of the third party implementation of LAMMPS. It added MD to SRD in order to simulate the boundary area of the simulation box. Because it is important to guarantee the seamless simulation, we also designed the overlap zones and communication zones. The preliminary experimental results showed that our proposed scheme is properly worked out and the execution time is also reduced.

• Proceedings of the Korea Society for Simulation Conference
• /
• 1998.10a
• /
• pp.65-65
• /
• 1998

Manufacturing systems like a motor production process are analyzed using simulations than numerical analyses and/or heuristic methods due to their stochastic properties. The SME(small and medium enterprise) producing automotive motors that develop CIM systems to improve production performance is focused as an application site. We analyze and understand the system exactly using layout based simulation, and then we will suggest the initial feashible production-plan dependent on the layout to overcome weak-points of the current system(i.e., high WIPs, bottle-neck processes, due-date delays and etc.). And, solutions are suggested to increase performances of SMEs producing automotive motors in this paper. The simulation model built in this study is moedlled and analyzed with fully object-oriented methodology using SiMPLE++TM according to properties of production processes of the automotive motor. And, we will introduce ways to verify the model with developed templates for reusability when new needs will be occurred such as designing a new ship, extension or rearrangement of the system, change of production-plans, receiving urgent orders, and so on.

• The Korean Journal of Applied Statistics
• /
• v.36 no.5
• /
• pp.399-413
• /
• 2023

Recently, monitoring and detecting anomalies in social networks have become an interesting research topic. In this study, we investigate the detection of abnormal changes in a network modeled by the DCSBM (degree corrected stochastic block model), which reflects the propensity of both individuals and communities. To this end, we propose three methods for anomaly detection in the DCSBM networks: One method for monitoring the entire network, and two methods for dividing and monitoring the network in consideration of communities. To compare these anomaly detection methods, we design and perform simulations. The simulation results show that the method for monitoring networks divided by communities has good performance.

• Journal of the Korea Society for Simulation
• /
• v.3 no.2
• /
• pp.27-36
• /
• 1994

In many real-world simulation studies the several measures of performance are of interest simultaneously. There exist very limited number of studies that explain and suggest the methods or procedures of inferencing the system performances at the same time. This study presents a procedure for determining the number of simulation observations required to achieve the prespecified confidence level for several measures of system performance. Mean values are selected as the measures, for instance, expected ordering cost, expected holding cost, and expected shortage cost for a given period of time in the study of inventory problems. Basically, the batch means approach is applied and extended to develop an algorithm to carry out the procedure handling more than single parameter. The efficacy of the presented method is assessed through the experiments. The empirical results based on some stochastic systems such as queues and inventory problems show that the suggested method produces as excellent result in terms of the precision of estimated means and the number of observations required.

• Wind and Structures
• /
• v.31 no.6
• /
• pp.549-560
• /
• 2020

Methods for stochastic simulation of non-Gaussian wind pressure have increasingly addressed the efficiency and accuracy contents to offer an accurate description of the extreme value estimation of the long-span and high-rise structures. This paper presents a linear prediction and z-transform (LPZ) based Cumulative distribution function (CDF) mapping algorithm for the simulation of multivariate non-Gaussian fluctuating wind pressure. The new algorithm generates realizations of non-Gaussian with prescribed marginal probability distribution function (PDF) and prescribed spectral density function (PSD). The inverse linear prediction and z-transform function (ILPZ) is deduced. LPZ is improved and applied to non-Gaussian wind pressure simulation for the first time. The new algorithm is demonstrated to be efficient, flexible, and more accurate in comparison with the FFT-based method and Hermite polynomial model method in two examples for transverse softening and longitudinal hardening non-Gaussian wind pressures.

• Nuclear Engineering and Technology
• /
• v.48 no.3
• /
• pp.765-772
• /
• 2016

In analyzing residual radiation, researchers generally use a two-step Monte Carlo (MC) simulation. The first step (MC1) simulates neutron transport, and the second step (MC2) transports the decay photons emitted from the activated materials. In this process, the stochastic uncertainty estimated by the MC2 appears only as a final result, but it is underestimated because the stochastic error generated in MC1 cannot be directly included in MC2. Hence, estimating the true stochastic uncertainty requires quantifying the propagation degree of the stochastic error in MC1. The brute force technique is a straightforward method to estimate the true uncertainty. However, it is a costly method to obtain reliable results. Another method, called the adjoint-based method, can reduce the computational time needed to evaluate the true uncertainty; however, there are limitations. To address those limitations, we propose a new strategy to estimate uncertainty propagation without any additional calculations in two-step MC simulations. To verify the proposed method, we applied it to activation benchmark problems and compared the results with those of previous methods. The results show that the proposed method increases the applicability and user-friendliness preserving accuracy in quantifying uncertainty propagation. We expect that the proposed strategy will contribute to efficient and accurate two-step MC calculations.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 1996.04a
• /
• pp.213-216
• /
• 1996

R'||'&'||'D project management is a process of decisions concerned with the achievement of goals of objectives. Especially, defense R'||'&'||'D project planning is the key in the successfull management of defense development. The defense project managers are constantly having to perform "what if\ulcorner" exercise, such as what if the project is extended out for an additional cost\ulcorner In this reserch, we developed a schedule-cost analysis model based upon Critical Path Method(CPM) and Venture Evaluation and Review Technique(VERT) for schedule-cost trade off analysis defense R'||'&'||'D projects. In the first step, a deterministic model is developed as a heuristic which deterministic model is developed as a heuristic which determines the schedule extension and reduction cost as a function desired schedule. In the second step, a stochastic network simulation model is developed to analyse the project risk (sucess and failure). The expected time and cost can be determined for desired schedule under the assumptions of stochastic arc data (time and cost) with a various precedence relationships. This model provides the defense R'||'&'||'D managers with an estimated and expected cost for curtailing or extending a project a given amount of time. The effectiveness and efficiency of the proposed methods, a heuristic and stochastic networks simulations, have been demonstrated through examples.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 1998.04a
• /
• pp.399-406
• /
• 1998

An investigation into the stochastic bifurcation and response statistits of an autoparameteric system under broad-band random excitation is made. The specific system examined is a spring-pendulum system with internal resonance, which is known to be a good model for a variety of engineering systems, including ship motions with nonlinear coupling between pitching and rolling motions. The Fokker-Planck equations is used to generate a general first-order differential equation in the dynamic moment of response coordinates. By means of the Gaussian and non-Gaussian closure methods the dynamic moment equations for the random responses of the system are reduced to a system of autonomous ordinary differential equations. In view of equilibrium solutions of this system and their stability we examine the stochastic bifurcation and response statistics. The analytical results are compared with results obtained by Monte Carlo simulation.