• Journal of the Korea Society for Simulation
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• v.11 no.2
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• pp.67-75
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• 2002

This paper proposes a parallel simulation algorithm for bounded Petri nets in a single processor, which exploits the SIMD(Single Instruction Multiple Data)-type parallelism. The proposed algorithm is based on a data packing scheme which packs multiple bytes data in a single register, thereby being manipulated simultaneously. The parallelism can reduce simulation time of bounded Petri nets in a single processor environment. The effectiveness of the algorithm is demonstrated by presenting speed-up of simulation time for two bounded Petri nets.

• 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 Environmental Science International
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• v.26 no.12
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• pp.1307-1319
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• 2017

In this study, we investigated the impact of different initial data on atmospheric modeling results using the Weather Research and Forecast (WRF) model. Four WRF simulations were conducted with different initialization in March 2015, which showed the highest monthly mean $PM_{10}$ concentration in the recent ten years (2006-2015). The results of WRF simulations using NCEP-FNL and ERA-Interim were compared with observed surface temperature and wind speed data, and the difference of grid nudging effect on WRF simulation between the two data were also analyzed. The FNL simulation showed better accuracy in the simulated temperature and wind speed than the Interim simulation, and the difference was clear in the coastal area. The grid nudging effect on the Interim simulation was larger than that of the FNL simulation. Despite of the higher spatial resolution of ERA-Interim data compared to NCEP-FNL data, the Interim simulation showed slightly worse accuracy than those of the FNL simulation. It was due to uncertainties associated with the Sea Surface Temperature (SST) field in the ERA-Interim data. The results from the Interim simulation with different SST data showed significantly improved accuracy than the standard Interim simulation. It means that the SST field in the ERA-Interim data need to be optimized for the better WRF simulation. In conclusion, although the WRF simulation with ERA-Interim data does not show reasonable accuracy compared to those with NCEP-FNL data, it would be able to be Improved by optimizing the SST variable.

• Journal of Digital Convergence
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• v.4 no.1
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• pp.17-30
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• 2006

Recently, Modeling and Simulation, which have been received attention in various in dustries such as national defence, eduction and entertainment, have been researched, and related applications have been developed actively. Especially, it is focused on the to chnology of a virtual reality and a virtual simulation which represents the implementation technology for the simulation education related to the e-Learning industry. However, a solution is needed to fulfill the lack of technology and research about standardize d data elements which could be applied to virtual simulation technologies in common. Therefore, this article suggests the virtual simulation data elements to increase the educational effect of a virtual simulation and interoperability of data among LMS through reference to korean and international standards and the result of related area analysis. In other words, this article aims to define the expression of data element and to propose the guideline elements in the virtual simulation scope.

• Korean Journal of Computational Design and Engineering
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• v.15 no.2
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• pp.85-93
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• 2010

This paper proposes a CAE data translation and visualization technique that can verify time-varying continuous analysis simulation in a virtual reality (VR) environment. In previous research, the use of CAE analysis data has been problematic because of the lack of any interactive simulation controls for visualizing continuous simulation data. Moreover, the research on post-processing methods for real-time verification of CAE analysis data has not been sufficient. We therefore propose a scene graph based visualization method and a post-processing method for supporting interoperability of continuous CAE analysis data. These methods can continuously visualize static analysis data independently of any timeline; it can also continuously visualize dynamic analysis data that varies in relation to the timeline. The visualization system for continuous simulation data, which includes a CAE middleware that interfaces with various formats of CAE analysis data as well as functions for visualizing continuous simulation data and operational functions, enables users to verify simulation results with more realistic scenes. We also use the system to do a performance evaluation with regard to the visualization of continuous simulation data.

• Journal of the Korea Society for Simulation
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• v.2 no.1
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• pp.125-133
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• 1993

A statistical method is described for estimation of the unknown constants in a theory using both of the computer simulation data and the real experimental data, The best linear unbiased predictor based on a spatial linear model is fitted from the computer simulation data alone. Then nonlinear least squares estimation method is applied to the real experimental data using the fitted prediction model as if it were the true simulation model. An application to the computational nuclear fusion devices is presented, where the nonlinear least squares estimates of four transport coefficients of the theoretical nuclear fusion model are obtained.

• Journal of the Korea Society for Simulation
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• v.14 no.2
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• pp.15-24
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• 2005

The goal of a distributed simulation such as battle field simulation is to combine all kinds of simulations in the same synthetic environment and to make people interact at the same time. It is a key issue to share the same synthetic environment among simulations. To support reusability and affordability in the modeling and simulation area, DMSO(Defense Modeling and Simulation Office) of USA developed concepts such as HLA(High Level Architecture) and SEDRIS (Synthetic Environmental Data Representation and Interchange Specification). In the industrial simulation area, the digital manufacturing is the main stream. To reduce cost and to reuse simulation environment, the standardization becomes the focus of digital manufacturing. This study proposes to use SEDRIS to improve interoperability of manufacturing data. The simulation data of DELMIA, which is a leading commercial digital manufacturing solution, is mapped and translated into the SEDRIS transmittal format. Mapping of the manufacturing simulation data and the synthetic environment are implemented and verified through experiments.

• Wind and Structures
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• v.39 no.2
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• pp.125-140
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• 2024

The simulation of non-stationary wind velocity is particularly crucial for the wind resistant design of slender structures. Recently, some data-driven simulation methods have received much attention due to their straightforwardness. However, as the number of simulation points increases, it will face efficiency issues. Under such a background, in this paper, a time-varying coherence matrix decomposition method based on Diagonal Proper Orthogonal Decomposition (DPOD) interpolation is proposed for the data-driven simulation of non-stationary wind velocity based on S-transform (ST). Its core idea is to use coherence matrix decomposition instead of the decomposition of the measured time-frequency power spectrum matrix based on ST. The decomposition result of the time-varying coherence matrix is relatively smooth, so DPOD interpolation can be introduced to accelerate its decomposition, and the DPOD interpolation technology is extended to the simulation based on measured wind velocity. The numerical experiment has shown that the reconstruction results of coherence matrix interpolation are consistent with the target values, and the interpolation calculation efficiency is higher than that of the coherence matrix time-frequency interpolation method and the coherence matrix POD interpolation method. Compared to existing data-driven simulation methods, it addresses the efficiency issue in simulations where the number of Cholesky decompositions increases with the increase of simulation points, significantly enhancing the efficiency of simulating multivariate non-stationary wind velocities. Meanwhile, the simulation data preserved the time-frequency characteristics of the measured wind velocity well.

• Korean Journal of Computational Design and Engineering
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• v.14 no.2
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• pp.69-76
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• 2009

The development of a simulation system requires many sub modules such as a dynamic module, a visualization module, etc. If a different freeware is used for each sub modules, it is hard to develop the simulation system by incorporating them because they use their own data structures. To solve this problem, a high-level data structure, called Dynamics Scene Graph Data structure (DSGD) is proposed, by wrapping data structures of two freeware; an Open Dynamics Engine (ODE) for the dynamic module and an Open Scene Graph (OSG) for the visualization module. Finally, to evaluate the applicability of the proposed data structure, it is applied to the block erection simulation in shipbuilding. The result shows that it can be used for developing the simulation system.

• Wind and Structures
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• v.37 no.5
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• pp.383-397
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• 2023

In the Structural Health Monitoring (SHM) system of civil engineering, data missing inevitably occurs during the data acquisition and transmission process, which brings great difficulties to data analysis and poses challenges to structural health monitoring. In this paper, Convolution Neural Network (CNN) is used to recover the nonstationary wind speed data missing randomly at sampling points. Given the technical constraints and financial implications, field monitoring data samples are often insufficient to train a deep learning model for the task at hand. Thus, simulation combined transfer learning strategy is proposed to address issues of overfitting and instability of the deep learning model caused by the paucity of training samples. According to a portion of target data samples, a substantial quantity of simulated data consistent with the characteristics of target data can be obtained by nonstationary wind-field simulation and are subsequently deployed for training an auxiliary CNN model. Afterwards, parameters of the pretrained auxiliary model are transferred to the target model as initial parameters, greatly enhancing training efficiency for the target task. Simulation synergy strategy effectively promotes the accuracy and stability of the target model to a great extent. Finally, the structural dynamic response analysis verifies the efficiency of the simulation synergy strategy.