• Journal of the Korea Society for Simulation
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• v.10 no.2
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• pp.61-74
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• 2001

The reliability evaluation of the large scale network becomes very complicate according to the growing size of network. Moreover if the reliability is not constant but follows probability distribution function, it is almost impossible to compute them in theory. This paper studies the network evaluation methods in order to overcome such difficulties. For this an efficient path set algorithm which seeks the path set connecting the start and terminal nodes efficiently is developed. Also, various variance reduction techniques are applied to compute the system reliability to enhance the simulation performance. As a numerical example, a large scale network is given. The comparisons of the path set algorithm and the variance reduction techniques are discussed.

• Journal of the Korea Society for Simulation
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• v.5 no.1
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• pp.91-106
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• 1996

Control Variate (CV) is very useful technique for variance reduction in a wide class of queueing network simulations. However, the loss in variance reduction caused by the estimation of the optimum control coefficients is an increasing function of the number of control variables. Therefore, in some situations, it is required to select an optimal set of control variables to maximize the variance reduction . In this paper, we develop the Adaptive Control Variates (ACV) method which selects an optimal set of control variates during the simulation adatively. ACV is useful to maximize the simulation efficiency when we need iterated simulations to find an optimal solution. One such an example is the Simulated Annealing (SA) because, in SA algorithm, we have to repeat in calculating the objective function values at each temperature, The ACV can also be applied to the queueing network optimization problems to find an optimal input parameters (such as service rates) to maximize the throughput rate with a certain cost constraint.

• Journal of the Korea Society of Computer and Information
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• v.23 no.12
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• pp.21-26
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• 2018

As the large amount of data can be efficiently stored, the methods extracting meaningful features from big data has become important. Especially, the techniques of converting high- to low-dimensional data are crucial for the 'Data visualization'. In this study, principal component analysis (PCA; linear dimensionality reduction technique) and Isomap (non-linear dimensionality reduction technique) are introduced and applied to neural big data obtained by the functional magnetic resonance imaging (fMRI). First, we investigate how much the physical properties of stimuli are maintained after the dimensionality reduction processes. We moreover compared the amount of residual variance to quantitatively compare the amount of information that was not explained. As result, the dimensionality reduction using Isomap contains more information than the principal component analysis. Our results demonstrate that it is necessary to consider not only linear but also nonlinear characteristics in the big data analysis.

• Journal of the Korean Operations Research and Management Science Society
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• v.21 no.3
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• pp.47-61
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• 1996

An ATM switch must deal with various kinds of input sources having different traffic characteristics and it must guarantee very small value of cel loss probability, about 10$^{8}$ -10$^{12}$ , to deal with loss-sensitive traffics. In order to estimate such a rate event probability with simulation procedure, a variance reduction technique is essential for obtaining an appropriate level of precision with reduced cost. In this paper, we propose a hybrid simulation technique to achieve reduction of variance of cell loss probability estimator, where hybrid means the combination of analytical method and simulation procedure. A discrete time queueing model with multiple input sources and a finite shared buffer is considered, where the arrival process at an input source and a finite shared buffer is considered, where the arrival process at an input source is governed by an Interrupted Bernoulli Process and the service rate is constant. We deal with heterogeneous input sources as well as homogeneous case. The performance of the proposed hybrid simulation estimator is compared with those of the raw simulation estimator and the importance sampling estimator in terms of variance reduction ratios.

• Journal of Energy Engineering
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• v.14 no.1
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• pp.46-53
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• 2005

This paper presents a new approach for determining an accurate incentive levels of Direct Load Control (DLC) program using probabilistic techniques. The economic analysis of DLC resources needs to identify the hourly-by-hourly expected energy-not-served resulting from the random outage characteristics of generators as well as to reflect the availability and duration of DLC resources, which results the computational explosion. Therefore, the conventional methods are based on the scenario approaches to reduce the computation time as well as to avoid the complexity of economic studies. In this paper, we have developed a new technique based on the sequential Monte Carlo simulation to evaluate the required expected load control amount in each hour and to decide the incentive level satisfying the economic constraints. In addition, we have applied the variance reduction technique to enhance the efficiency of the simulation. To show the efficiency and effectiveness of the suggested method, the numerical studies have been performed for the modified IEEE 24-bus reliability test system.

• Journal of the Korea Society for Simulation
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• v.14 no.3
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• pp.129-136
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• 2005

We develop a variance reduction technique applicable in one simulation experiment whose purpose is to estimate the parameters of a first order linear model. This method utilizes the control variates obtained during the course of simulation run under Schruben and Margolin's method (S-M method). The performance of this method is shown to be similar in estimating the main effects, and to be superior to S-M method in estimating the overall mean response in a given model. We consider that a proposed method may yield a better result than S-M method if selected control variates are highly correlated with the response at each design point.

• Proceedings of the KIEE Conference
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• summer
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• pp.666-670
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• 2004

This paper presents a new approach for determining an accurate incentive levels of Direct Load Control (DLC) program using sequential Monte Carlo Simulation (MCS) techniques. The economic analysis of DLC resources needs to identify the hourly-by-hourly expected energy-not-served resulting from the random outage characteristics of generators as well as to reflect the availability and duration of DLC resources, which results the computational explosion. Therefore, the conventional methods are based on the scenario approaches to reduce the computation time as well as to avoid the complexity of economic studies. In this paper, we have developed a new technique based on the sequential MCS to evaluate the required expected load control amount in each hour and to decide the incentive level satisfying the economic constraints. And also the proposed approach has been considered multi-state as well as two-state of the generating units. In addition, we have applied the variance reduction technique to enhance the efficiency of the simulation. To show the efficiency and effectiveness of the suggested method the numerical studies have been performed for the modified IEEE reliability test system.

• Journal of the Korea Society for Simulation
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• v.4 no.1
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• pp.25-36
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• 1995

We develop a variance reduction technique in one simulation experiment whose purpose is to estimate the parameters of a first-order linear model. This method utilizes the control variates obtained during the course of simulation run under Schruben and Margolin's method (S-M method). The performance of this method is shown to be similar in estimating the main effects, and to be superior to S-M method in estimating the overall mean response in the hospital simulation experiment. For the general case, we consider that a proposed method may yield a better result than S-M method if selected control variates are highly correlated with the response at each design point.

• Korean Management Science Review
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• v.31 no.1
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• pp.49-61
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• 2014

We investigate the hedging effectiveness of incorporating single-stock futures into the corresponding stocks. Investing in only stocks frequently causes too much risk when market volatility suddenly rises. We found that single-stock futures help reduce the variance and risk levels of the corresponding stocks invested. We use daily prices of Korean stocks and their corresponding futures for the time period from December 2009 to August 2013 to test the hedging effect. We also use system trading technique that uses automatic trading program which also has several simulation functions. Moving average strategy, Stochastic's strategy, Larry William's %R strategy have been considered for hedging strategy of the futures. Hedging effectiveness of each strategy was analyzed by percent reduction in the variance between the hedged and the unhedged variance. The results clearly showed that examined hedging strategies reduce price volatility risk compared to unhedged portfolio.

• Journal of Korean Society for Quality Management
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• v.22 no.4
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• pp.1-12
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• 1994

For quality improvement, Taguchi emphasizes the reduction of variation of the quality characteristic. Taguchi has used the signal to noise ratios for achieving minimum dispersion of the quality characteristic with its location adjusted to some desired target value. At each setting of design factors, the variance of the quality characteristic could be affected by the mean. In most cases, as the mean get larger, the variance tends to increase, The Taguchi's SN ratio corresponds to the case that the variance is proportional to the square of the mean. But the variance can increase faster or slower than the square of the mean. We propose to infer a linking relationship of the variance and mean through simple data analysis technique, and then use a reasonable SN ratio.