• Journal of Information Processing Systems
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• v.17 no.1
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• pp.28-36
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• 2021

This paper proposes two novel methods to model and simulate a mobile Internet of Things (IoT) system using the discrete event system specification (DEVS) formalism. In traditional simulation methods, it is advantageous to partition the simulation area hierarchically to reduce simulation time; however, in this case, the structure of the model may change as the IoT nodes to be modeled move. The proposed methods reduce the simulation time while maintaining the model structure, even when the IoT nodes move. To evaluate the performance of the proposed methods, a prototype mobile IoT system was modeled and simulated. The simulation results show that the proposed methods achieve good performance, even if the number of IoT nodes or the movement of IoT nodes increases.

• 한국전산유체공학회:학술대회논문집
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• 2009.11a
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• pp.1-1
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• 2009

In this presentation, I talk about various fluid simulation methods that have been developed for computer graphics special effects since 1996. They are all based on CFD but sacrifice physical reality for visual plausability and time. But as the speed of computer increases rapidly and the capability of GPU (graphics processing unit) improves, methods for more physical realism have been tried. In this talk, I will focus on four aspects of fluid simulation methods for computer graphics: (1) particle level-set methods, (2) particle-based simulation, (3) methods for exact satisfaction of incompressibility constraint, and (4) GPU-based simulation. (1) Particle level-set methods evolve the surface of fluid by means of the zero-level set and a band of massless marker particles on both sides of it. The evolution of the zero-level set captures the surface in an approximate manner and the evolution of marker particles captures the fine details of the surface, and the zero-level set is modified based on the particle positions in each step of evolution. (2) Recently the particle-based Lagrangian approach to fluid simulation gains some popularity, because it automatically respects mass conservation and the difficulty of tracking the surface geometry has been somewhat addressed. (3) Until recently fluid simulation algorithm was dominated by approximate fractional step methods. They split the Navier-Stoke equation into two, so that the first one solves the equation without considering the incompressibility constraint and the second finds the pressure which satisfies the constraint. In this approach, the first step introduces error inevitably, producing numerical diffusion in solution. But recently exact fractional step methods without error have been developed by fluid mechanics scholars), and another method was introduced which satisfies the incompressibility constraint by formulating fluid in terms of vorticity field rather than velocity field (by computer graphics scholars). (4) Finally, I want to mention GPU implementation of fluid simulation, which takes advantage of the fact that discrete fluid equations can be solved in parallel.

• 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.

• Journal of the Korea Society for Simulation
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• v.7 no.1
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• pp.27-37
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• 1998

Simulation output is generally stochastic and autocorrelated, and includes the initial condition bias. To exclude the bias, the determination of truncation point has been one of important issues for the steady-state simulation output analysis. In this paper, two methods are presented for detection of truncation point in order to estimate efficiently the steady-state measure of simulation output. They are based on the Euclidean distance equation, and the backpropagation algorithm in Neural Networks. The experimental results obtained by M/M/1 and M/M/2 show that the proposed methods are very promising with respect to coverage and relative bias. The methods could be used for the on-line analysis of simulation outputs.

• 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.

• The Korean Journal of Emergency Medical Services
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• v.25 no.3
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• pp.111-125
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• 2021

Purpose: This study aimed to analyze the impact of rapid cycle deliberate practice (RCDP) simulation education on advanced cardiac life support knowledge, confidence, satisfaction, and performance ability among paramedic students, and provide basic data on the appropriate methods of educational instruction. Methods: The 48 subjects to be instructed were divided into the traditional simulation education group and the RCDP simulation education group. Six participants were randomly assigned to each group and pre-surveyed. They were then exposed to a lecture about advanced cardiac life support related theories for 60 min and post-surveyed through questionnaires with the same learning goals and scenarios. Results: The advanced cardiac life support knowledge (t=-4.813, p=.000) and performance ability (t=-2.903, p=.006) were significantly different between the traditional simulation education and RCDP simulation education groups The results also showed a significant difference in attach monitor (z=6.857, p=.009), analyze EKG rhythm (z=11.111, p=.001), and defibrillation (z=12.632, p=.000), indicating differences in performance capabilities between the two groups. Conclusion: To improve advanced cardiac life support knowledge, performance ability, and confidence in the paramedic students who receive RCDP simulation education, simulation education methods that are appropriate for the subjects being taught, and detailed learning goals and feedback are necessary.

• 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.

• Journal of the Korea Safety Management & Science
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• v.3 no.3
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• pp.221-230
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• 2001

The assembly tolerance design methods have applied linear or nonlinear programming methods and used simulation method and search algorithms to optimize the tolerance allocation of each part in an assembly. However, those methods are only considered to the relationship between tolerance and manufacturing cost, which do not consider a quality loss cost for each part tolerance. In this paper, the integrated simulation model used genetic algorithm and the Monte-Carlo simulation method was developed for the allocation of the optimal tolerance considering the manufacturing cost and quality loss cost.

• 한국디지털정책학회:학술대회논문집
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• 2006.06a
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• pp.87-109
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• 2006

The purpose of this paper is to examine the present performance evaluation methods and to make Monte Carlo Simulation Model for the IT-based Government innovation project. It is suggested the proper ways in applying of Monte Carlo Simulation Model by integration of present evaluation methods. It develops the theoretical framework for this paper, examining the existing literature on proposing an approach to the key concepts of the economic impact analysis methods. It examines the actual conditions of performance evaluation focusing on the It-based Government Innovation project. It considers how the simulation model is applied to the performance management in the public innovation project focusing on the framework, process and procedure of performance management.

• Journal of the Korea Society for Simulation
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• v.3 no.1
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• pp.65-74
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• 1994

In the steady-state simulation, it is important to identify initialization bias for the correct estimates of the simulation model under study. In this paper, the methods from chaos theory are applied to the determination of truncation points in the simulation data for controlling the initial bias. Two methods are proposed and evaluated based on their effectiveness for estimation the average waiting time in M/M/1($\infty$) queueing model.