• Journal of the Korean earth science society
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• v.35 no.1
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• pp.69-87
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• 2014

This paper suggests a conditional simulation framework based on multiple data transformations for geostatistical simulation of compositional data. First, log-ratio transformation is applied to original compositional data in order to apply conventional statistical methodologies. As for the next transformations that follow, minimum/maximum autocorrelation factors (MAF) and indicator transformations are sequentially applied. MAF transformation is applied to generate independent new variables and as a result, an independent simulation of individual variables can be applied. Indicator transformation is also applied to non-parametric conditional cumulative distribution function modeling of variables that do not follow multi-Gaussian random function models. Finally, inverse transformations are applied in the reverse order of those transformations that are applied. A case study with surface sediment compositions in tidal flats is carried out to illustrate the applicability of the presented simulation framework. All simulation results satisfied the constraints of compositional data and reproduced well the statistical characteristics of the sample data. Through surface sediment classification based on multiple simulation results of compositions, the probabilistic evaluation of classification results was possible, an evaluation unavailable in a conventional kriging approach. Therefore, it is expected that the presented simulation framework can be effectively applied to geostatistical simulation of various compositional data.

• International Journal of Naval Architecture and Ocean Engineering
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• v.8 no.5
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• pp.496-510
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• 2016

Simulation technology is a type of shipbuilding product lifecycle management solution used to support production planning or decision-making. Normally, most shipbuilding processes are consisted of job shop production, and the modeling and simulation require professional skills and experience on shipbuilding. For these reasons, many shipbuilding companies have difficulties adapting simulation systems, regardless of the necessity for the technology. In this paper, the data model for shipyard production simulation model generation was defined by analyzing the iterative simulation modeling procedure. The shipyard production simulation data model defined in this study contains the information necessary for the conventional simulation modeling procedure and can serve as a basis for simulation model generation. The efficacy of the developed system was validated by applying it to the simulation model generation of the panel block production line. By implementing the initial simulation model generation process, which was performed in the past with a simulation modeler, the proposed system substantially reduced the modeling time. In addition, by reducing the difficulties posed by different modeler-dependent generation methods, the proposed system makes the standardization of the simulation model quality possible.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.889-906
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• 2020

Analysis of multi-physics systems and the visualization of simulation data are crucial and difficult in computational science and engineering. In Korea, Korea Institute of Science and Technology Information KISTI developed EDISON, a web-based computational science simulation platform, and it is now the ninth year since the service started. Hitherto, the EDISON platform has focused on providing a robust simulation environment and various computational science analysis tools. However, owing to the increasing issues in collaborative research, data format standardization has become more important. In addition, as the visualization of simulation data becomes more important for users to understand, the necessity of analyzing input / output data information for each software is increased. Therefore, it is necessary to organize the data format and metadata for the representative software provided by EDISON. In this paper, we analyzed computational fluid dynamics (CFD) and computational structural dynamics (CSD) simulation software in the field of mechanical engineering where several physical phenomena (fluids, solids, etc.) are complex. Additionally, in order to visualize various simulation result data, we used existing web visualization tools developed by third parties. In conclusion, based on the analysis of these data formats, it is possible to provide a foundation of multi-physics and a web-based visualization environment, which will enable users to focus on simulation more conveniently.

• Journal of the Korean Solar Energy Society
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• v.34 no.1
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• pp.8-18
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• 2014

Building simulation is used in a variety of sectors. In its early years, building simulation was mainly used in the design phase of a building for basic functions. Recently, however, it has become increasingly important during the operating phase, for commissioning and facility management. Most building simulation tools are used to estimate the thermal environment and energy consumption performance, and hence, they require the inputting of hourly weather data. A building simulation used for prediction should take into account the use of standard weather data. Weather data, which is used as input for a building simulation, plays a crucial role in the prediction performance, and hence, the selection of appropriate weather data is considered highly important. The present study proposed a technique for generating real-time weather data files, as opposed to the standard weather data files, which are required for running the building simulation. The forecasted weather elements provided by the Korea Meteorological Administration (KMA), the elements produced by the calculations, those utilizing the built-in functions of Energy Plus, and those that use standard values are combined for hourly input. The real-time weather data files generated using the technique proposed in the present study have been validated to compare with measured data and simulated data via EnergyPlus. The results of the present study are expected to increase the prediction accuracy of building control simulation results in the future.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.12
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• pp.109-118
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• 2004

Life cycle assessment (LCA) usually involves some uncertainty. These uncertainties are generally divided in two categories such lack of data and data inaccuracy in life cycle inventory (LCI). This paper explo.es a methodology on dealing with uncertainty due to lack of data in LCI. In order to treat uncertainty of LCI data, a model for data uncertainty is proposed. The model works with probabilistic curves as inputs and with Monte Carlo Simulation techniques to propagate uncertainty. The probabilistic curves were derived from the results of survey in expert network and Monte Carlo Simulation was performed using the derived probabilistic curves. The results of Monte Carlo Simulation were verified by statistical test. The proposed approach should serve as a guide to improve data quality and deal with uncertainty of LCI data in LCA projects.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2004.04a
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• pp.255-261
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• 2004

Through spot-investigation and observation of water quality environment of Jinhae Bay, this conducts the sea movement simulation on the current water quality of Jinhae Bay, grasps the flowing characteristics of Jinhae Bay and carries out ocean pollution simulation by using the data collected. Sea movement simulation results are tested by using Ocean Investigation Bureau's data of tide and current, and are also used in the ocean pollution spreading simulation. Water quality spreading simulation is conducted by using the data of sea movement and data of observation and then the results are compared with data of spot investigation. COD condensity is calculated and the water quality characteristics of Jinhae Bay are grasped.

• International Journal of Aeronautical and Space Sciences
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• v.18 no.1
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• pp.108-117
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• 2017

This paper presents a distribution simulation model for dual purpose improved conventional munitions based on flight test data. A systematic procedure for designing a dispersion simulation model is proposed. A new accumulated broken line graph was suggested for designing the distribution shape. In the process of verification and simulation for the distribution simulation model, verification was performed by first comparing data with firing test results, and an application simulation was then conducted. The Monte Carlo method was used in the simulations, which reflected the relationship between ejection conditions and real distribution data. Before establishing the simulation algorithm, the dominant ejection parameter of the submunitions was examined. The relationships between ejection conditions and distribution results were investigated. Five key distribution parameters were analyzed with respect to the ejection conditions. They reflect the characteristics of clustered particle dynamics and aerodynamics.

• Proceedings of the Korea Society for Simulation Conference
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• 2003.06a
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• pp.179-184
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• 2003

HLA-RTI is middleware for the distribute simulation that developed in the US Department of Defense. This provides fast accomplishment speed and reliability than distribute simulation Middleware by transfer. However, DDM(Data Distribution Management) service is used as data filtering technology in the existing HLA-RTI. As for this, the problem that network traffic increases in data exchange between the mobility simulation objects is generated. it proposes applying agent technology to the mobility simulation object in order to solve these problems in this paper in this. And this paper applies that to practical simulation and analyzes performance between each data filtering technology with comparison.

• Journal of Internet Computing and Services
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• v.21 no.3
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• pp.41-51
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• 2020

EDISON is a web-based platform that provides easy and convenient use of simulation software on high-performance computers. One of the most important roles of a computational science platform, such as EDISON, is to post-process and represent the simulation results data so that the user can easily understand the data. We interviewed EDISON users and collected requirements for post-processing and represent of simulation results, which included i) flexible data representation, ii) supporting various data representation components, and iii) flexible and easy development of view template. In previous studies, it was difficult to develop or contribute data representation components, and the view templates were not able to be shared or recycled. This causes a problem that makes it difficult to create ecosystems for the representation tool development of numerous simulation software. EDISON-VIEW is a framework for post-processing and representing simulation results produced from the EDISON platform. This paper proposes various methods used in the design and development of the EDISON-VIEW framework to solve the above requirements and problems. We have verified its usefulness by applying it to simulation software in various fields such as material, computational fluid dynamics, computational structural dynamics, and computational chemistry.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3501-3518
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• 2020

The method of DNS data collection is one of the most important parts of DNS simulation. DNS data contains a lot of information. When it comes to analyzing the DNS security issues by simulation on the cyber range with customized features, we only need some of them, such as IP address, domain name information, etc. Therefore, the data we need are supposed to be light-weighted and easy to manipulate. Many researchers have designed different schemes to obtain their datasets, such as LDplayer and Thales system. However, existing solutions consume excessive computational resources, which are not necessary for DNS security simulation. In this paper, we propose a light-weighted active data collection method to prepare the datasets for DNS simulation on cyber range. We evaluate the performance of the method and prove that it can collect DNS data in a short time and store the collected data at a lower storage cost. In addition, we give two examples to illustrate how our method can be used in a variety of applications.