• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.4D
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• pp.661-670
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• 2006

The utilization of simulation has been limited in planning construction process because it is difficult to collect data and build a model using simulation method. This study collects construction operation data and analyzes the characteristics of its distribution. Through the statistical analysis on the empirical data, this study identifies Beta distribution functions is one of the most proper in duplicating the characteristics of construction equipment operation data into a computer simulation. The information obtained in this study can support preparing input data for another simulation.

• Journal of the Society of Naval Architects of Korea
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• v.53 no.1
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• pp.18-28
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• 2016

In shipbuilding, the scheduling system is susceptible to sudden changes and thus it turns to be difficult to predict the differences between schedule and production records in advance. A computer-based simulation is commonly utilized to overcome the discrepancies occurred in estimating workloads and resulting processing times. The main drawback of this simulation-based solution is its limited applicability because, in most cases, each shipyard requires specific and customized simulation environment. By standardizing the planning data of the midterm scheduling system, as proposed in this paper, the efficiency of the current simulation model can be enhanced. To present an alternative approach, this paper begins with the analysis of the complex planning data structure of several shipyards and then proceeds to construct a standard data structure based on the neutral format. An interface application is developed for the data transaction and simulation in on-line environment. As a result, a simulation-based production management of shipyards can be achieved by the efficient prediction of planning and scheduling.

• Journal of the Society of Naval Architects of Korea
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• v.50 no.5
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• pp.314-323
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• 2013

Production simulation technology is beneficial to solve the complicated and fluctuated problems in a shipyard. It takes too much time and effort to build simulation models in the field, though. This research proposes a feasible method to reduce the difficulties related to simulation modeling for the factory or shop capacity analysis. In addition, a proposed neutral data format for production information is efficient to manage information acquisition for simulation modeling automation. A panel block shop model is contributed to comparison between the conventional technique and the automated one. The automation technique is highly recommended to run a rapid simulation in the shipyard problem.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.393-399
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• 2009

This paper introduces an automated tool named Advanced Stochastic Schedule Simulation System (AS4). The system automatically integrates CPM schedule data exported from Primavera Project Planner (P3) and historical activity duration data obtained from a project data warehouse, computes the best fit probability distribution functions (PDFs) of historical activity durations, assigns the PDFs identified to respective activities, computes the optimum number of simulation runs, simulates the schedule network for the optimum number of simulation runs, and estimates the best fit PDF of project completion times (PCTs). AS4 improves the reliability of simulation-based scheduling by effectively dealing with the uncertainties of the activities' durations, increases the usability of the schedule data obtained from commercial CPM software, and effectively handles the variability of the PCTs by finding the best fit PDF of PCTs. It is designed as an easy-to-use computer tool programmed in MATLAB. AS4 encourages the use of simulation-based scheduling because it is simple to use, it simplifies the tedious and burdensome process involved in finding the PDFs of the many activities' durations and in assigning the PDFs to the many activities of a new network under modeling, and it does away with the normality assumptions used by most simulation-based scheduling systems in modeling PCTs.

• Journal of ILASS-Korea
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• v.19 no.2
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• pp.64-68
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• 2014

A driving vehicle performance which is driven by FTP-75 mode was simulated by computer. Throttle valve position, engine speed, air mass flow rate, fuel consumption et al. were computer simulated. A set of engine part load performance data, automatic transmission shift map and vehicle specifications were used for the computer simulation. Throttle valve position, engine speed, air mass flow rate et al. measured for evaluating the computer simulation results by driving the vehicle with FTP-75 mode on a chassis dynamometer. GT-Power$^{(R)}$ software was used for the computer simulation of the driving vehicle performance. Experimental fuel consumption rate was measured by using an ECU HILS fuel injection system. The experimental data and simulation results were compared. The computer simulation of the driving vehicle performance predicts the measured data well comparatively.

• Proceedings of the Korea Society for Simulation Conference
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• 2004.05a
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• pp.142-147
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• 2004

It is very difficult to acquire high-fidelity flight test data for small airplanes such as typical unmanned aerial vehicles because MEMS-type small sensors used in the tests do not present reliable data in general. Besides, it is not practical to conduct expensive flight tests for low-cost small airplanes in order to simulate their flight characteristics. A practical approach to obtain acceptable flight data, including stability and control derivatives and data of weight and balance, is proposed in this study. Aircraft design software such as Darcorp's AAA is used to generate aerodynamic data for small airplanes, and moments of inertia are calculated using CATIA, structural design software. These flight data from simulation software are evaluated subjectively and tailored using simulation flight by experienced pilots, based on the certified procedures in FAA AC 120-45A and 40B, which are used for manned airplane simulators.

• Journal of the Korea Society for Simulation
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• v.19 no.3
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• pp.119-125
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• 2010

The moving block bootstrap, the stationary bootstrap, and the threshold bootstrap are methods of simulation output analysis, which are applicable to autocorrelated data. These bootstrap methods assume the stationarity of data. However, bootstrap methods cannot work if the stationary assumption is not guaranteed because of seasonality or trends in data. In the simulation output analysis, threshold bootstrap method is the best in describing the autocorrelation structure of original data set. The threshold bootstrap makes the cycle based on threshold value. If we apply the bootstrap to seasonality data, we can get similar accuracy of the results. In this paper, we verify the possibility of applying the bootstrap to seasonal data.

• Journal of the Korea Society for Simulation
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• v.19 no.4
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• pp.21-29
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• 2010

This research suggests a simulation model regarding the performance analysis of the Heat-Shrink-Tube manufacturing process using a simulation method. To analyze this study, firstly, we have collected the operating data from 'A' automobile parts company. Secondly, we have analyzed the collected data to apply a simulation model. Thirdly, we have developed a simulation model to experiment the process analysis and the line balancing methodology. The proposed simulation model can be executed by various input data without changing the simulation model and the performance of the Heat-Shrink-Tube Manufacturing system can be calculated by this model.

• Journal of the Korean Operations Research and Management Science Society
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• v.38 no.2
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• pp.65-73
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• 2013

In the simulation output analysis, bootstrap method is an applicable resampling technique to insufficient data which are not significant statistically. The moving block bootstrap, the stationary bootstrap, and the threshold bootstrap are typical bootstrap methods to be used for autocorrelated time series data. They are nonparametric methods for stationary time series data, which correctly describe the original data. In the simulation output analysis, however, we may not use them because of the non-stationarity in the data set caused by the trend such as increasing or decreasing. In these cases, we can get rid of the trend by differencing the data, which guarantees the stationarity. We can get the bootstrapped data from the differenced stationary data. Taking a reverse transform to the bootstrapped data, finally, we get the pseudo-samples for the original data. In this paper, we introduce the applicability of bootstrap methods to the time series data having trend, and then verify it through the statistical analyses.

• Smart Structures and Systems
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• v.14 no.6
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• pp.1197-1220
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• 2014

Accurate actuator tracking is critical to achieve reliable real-time hybrid simulation results for earthquake engineering research. The frequency-domain evaluation approach provides an innovative way for more quantitative post-simulation evaluation of actuator tracking errors compared with existing time domain based techniques. Utilizing the Fast Fourier Transform the approach analyzes the actuator error in terms of amplitude and phrase errors. Existing application of the approach requires using the complete length of the experimental data. To improve the computational efficiency, two techniques including data decimation and frequency decimation are analyzed to reduce the amount of data involved in the frequency-domain evaluation. The presented study aims to enhance the computational efficiency of the approach in order to utilize it for future on-line actuator tracking evaluation. Both computational simulation and laboratory experimental results are analyzed and recommendations on the two decimation factors are provided based on the findings from this study.