• Journal of the Korea Institute of Military Science and Technology
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• v.15 no.6
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• pp.802-811
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• 2012

This study proposes an OMS/MP preparation methodology using a simulation method instead of a survey method. We applied our methodology to the next generation detection radar, providing reasonable peace- and war-time OMS/MP values. Based on these results, we propose the process to calculate RAM objective values. The previous survey method required to supplement its method since the method used data from a similar weapon system. In addition, the previous method didn't provide enough reliability for the future weapon system. Instead of using the previous survey method, we propose to use war game simulation, which provides a better OMS/MP values. Based on these results, we propose the logical consecutive process that prepares combat and simulation scenarios, peace- and war-time OMS/MP values and RAM objective values.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.1 s.94
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• pp.246-252
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• 1999

In this paper, the frequency response characteristics of the velocity controlled EHS system obtained by linear simulation method, nonlinear simulation method, and experimentation are compared one another, in order to verify propriety of the linearization method in case of analysis of hydraulic systems. The Bode diagrams are obtained by transforming time domain data of experimental results and nonlinear simulated ones with Fourier transform. The results of nonlinear simulation are more similar to the frequency response of the real systems than those of linear simulation. It is found that nonlinearity of hydraulic systems is mainly occurred from servo valve, and nonlinearity is increased as displacement of servo valve spool increases.

• Proceedings of the Korea Society for Simulation Conference
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• 1992.10a
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• pp.6-6
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• 1992

This work deals with nonlinear least squares method for estimating unknown universial constants C in a computer simulation code real experimental data(or database) and computer simulation data. The best linear unbiased predictor based on a spatial statistical model is fitted from the computer simulation data. Then nonlinear least squares estimation method is applied to the real data using the fitted prediction model(or simulation predictor) as if it were the true simulation model. An application to the computational nuclear fusion device is presented.

• Journal of the Korea Society for Simulation
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• v.3 no.2
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• pp.57-67
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• 1994

For many practical and industrial optimization problems where some or all of the system components are stochastic, the objective functions cannot be represented analytically. Therefore, modeling by computer simulation is one of the most effective means of studying such complex systems. In this paper, with discussion of simulation optimization techniques, a case study in machining process for application of simulation optimization is presented. Most of optimization techniques can be classified as single-or multiple-response techniques. The optimization of single-response category, these strategies are gradient based search methods, stochastic approximate method, response surface method, and heuristic search methods. In the multiple-response category, there are basically five distinct strategies for treating the responses and finding the optimum solution. These strategies are graphical method, direct search method, constrained optimization, unconstrained optimization, and goal programming methods. The choice of the procedure to employ in simulation optimization depends on the analyst and the problem to be solved.

• Structural Engineering and Mechanics
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• v.17 no.2
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• pp.267-279
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• 2004

This paper presents an improved Monte Carlo simulation for the probabilistic determination of initial cable forces of cable-stayed bridges under dead loads using the response surfaces method. A response surface (i.e. a quadratic response surface without cross-terms) is used to approximate structural response. The use of the response surface eliminates the need to perform a deterministic analysis in each simulation loop. In addition, use of the response surface requires fewer simulation loops than conventional Monte Carlo simulation. Thereby, the computation time is saved significantly. The statistics (e.g. mean value, standard deviation) of the structural response are calculated through conventional Monte Carlo simulation method. By using Monte Carlo simulation, it is possible to use the existing deterministic finite element code without modifying it. Probabilistic analysis of a truss demonstrates the proposed method' efficiency and accuracy; probabilistic determination of initial cable forces of a cable-stayed bridge under dead loads verifies the method's applicability.

• Journal of the Korea Society for Simulation
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• v.19 no.1
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• pp.133-141
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• 2010

This research suggests a unified scheme for using stratified sampling and control variates method to improve the efficiency of estimation for parameters in simulation experiments. We utilize standardized concomitant variables defined during the course of simulation runs. We first use these concomitant variables to counteract the unknown error of response by the method of control variates, then use a concomitant variable not used in the controlled response and stratify the response into appropriate strata to reduce the variation of controlled response additionally. In case that the covariance between the response and a set of control variates is known, we identify the simulation efficiency of suggested method using control variates and stratified sampling. We conjecture the simulation efficiency of this method is better than that achieved by separated application of either control variates or stratified sampling in a simulation experiments. We investigate such an efficiency gain through simulation on a selected model.

• Journal of Korean Society for Quality Management
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• v.12 no.1
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• pp.17-30
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• 1984

A computer method is developed for group technology layout and its simulation analysis. The method is composed of three phases: Phase I sorts the parts by its similar production routes and forms part families. Phase II plots the layout by machine cell and evaluates the group layout alternatives by the total process time analysis and the part travel distance evaluation analysis. Phase III also evaluates the alternatives by simulation analysis using SIMAN simulation software. All the computer programs are developed with BASIC except SIMAN simulation.

• Management Science and Financial Engineering
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• v.13 no.1
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• pp.1-24
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• 2007

I propose new combined randomized methods for global optimization problems. These methods are based on the Nested Partitions(NP) method, a useful method for simulation optimization which guarantees global optimal solution but has several shortcomings. To overcome these shortcomings I hired various statistical selection methods and combined with NP method. I first explain the NP method and statistical selection method. And after that I present a detail description of proposed new combined methods and show the results of an application. As well as, I show how these combined methods can be considered in case of computing budget limit problem.

• Proceedings of the Korea Society for Simulation Conference
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• 1993.10a
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• pp.2-2
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• 1993

Inference for discrete event simulations usually relies on either independent replications or, if each simulation run is expensive, the method of batch means applied to a single replications. We present a new method, threshold bootstrap, which equals or exceeds the performance of independent replications or batch means. The method works by resampling runs of data created when a stationary time series crosses a threshold level, such as the sample mean of series. Computational results show that the threshold bootstrap matches or exceeds the performance of these alternative methods in estimating the standard deviation of the sample mean and producing valid confidence intervals.

• Journal of Mechanical Science and Technology
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• v.15 no.8
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• pp.1090-1096
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• 2001

In this paper, new methods for efficiently solving linear acceleration equations of multibody dynamic simulation exploiting sparsity for real-time simulation are presented. The coefficient matrix of the equations tends to have a large number of zero entries according to the relative joint coordinate numbering. By adequate joint coordinate numbering, the matrix has minimum off-diagonal terms and a block pattern of non-zero entries and can be solved efficiently. The proposed methods, using sparse Cholesky method and recursive block mass matrix method, take advantages of both the special structure and the sparsity of the coefficient matrix to reduce computation time. The first method solves the η$\times$η sparse coefficient matrix for the accelerations, where η denotes the number of relative coordinates. In the second method, for vehicle dynamic simulation, simple manipulations bring the original problem of dimension η$\times$η to an equivalent problem of dimension 6$\times$6 to be solved for the accelerations of a vehicle chassis. For vehicle dynamic simulation, the proposed solution methods are proved to be more efficient than the classical approaches using reduced Lagrangian multiplier method. With the methods computation time for real-time vehicle dynamic simulation can be reduced up to 14 per cent compared to the classical approach.