• Journal of the Korea Society for Simulation
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• v.18 no.1
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• pp.27-34
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• 2009

Currently network users, especially the number of internet users, increase rapidly. Also, high quality of service is required and this requirement results a sudden network traffic increment. As a result, an efficient management system for huge network traffic becomes an important issue. Ontology/data engineering based context awareness using the System Entity Structure (SES) concepts enables network administrators to access traffic data easily and efficiently. The network traffic analysis system, which is studied in this paper, is designed and implemented based on a model and simulation using data engineering methodology to be avaiable in evaluating large network traffic data. Extensible Markup Language (XML) is used for metadata language in this system. The information which is extracted from the network traffic analysis system could be modeled and simulated in Discrete Event Simulation (DEVS) methodology for further works such as post simulation evaluation, web services, and etc.

• Wind and Structures
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• v.18 no.5
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• pp.549-566
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• 2014

In spite of progress in the numerical simulation of typhoon wind field in atmospheric boundary layer (ABL), using typhoon wind field model in conjunction with Monte Carlo simulation method can only accurately evaluate typhoon wind field over a general terrain. This method is not enough for a reliable evaluation of typhoon wind field over the actual complex terrain with surface roughness and topography variations. To predict typhoon wind field over the actual complex terrain in ABL, a hybrid numerical simulation method combined typhoon simulation used the typhoon wind field model proposed by Meng et al. (1995) and CFD simulation in which the Reynolds averaged Navier-Stokes (RANS) equations and k-${\varepsilon}$ turbulence model are used. Typhoon wind filed during typhoon Dujuan and Imbudo are simulated using the hybrid numerical simulation method, and compared with the results predicted by the typhoon wind field model and the wind field measurement data collected by Fugro Geotechnical Services (FGS) in Hong Kong at the bridge site from the field monitoring system of wind turbulence parameters (FMS-WTP) to validate the feasibility and accuracy of the hybrid numerical simulation method. The comparison demonstrates that the hybrid numerical simulation method gives more accurate prediction to typhoon wind speed and direction, because the effect of topography is taken into account in the hybrid numerical simulation method.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.10
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• pp.125-131
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• 2000

By using the test of signal error, model structure of an electrohydraulic servo system is determined. For determining parameter of the electrohydraulic servo system, using time discrete model of parametric method, parameters in time discrete model are searched by the least square method. By bilinear transform, we have found the model of electrohydraulic servo system in s domain. Afterwards, we have compared experimental data with simulation data by MATLAB having the identified parameter. As the result, experimental data is agreed with simulation data very well.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.207-209
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• 1996

The software of the midclass commuter flight simulation is running on multiprocessor/multitasking environments The software is consist of tasks which are periodically alive at a given interval. Each task communicates via shared memory. The data shared by tasks is divided by several block. Only one task, called producer, can produce data for a data block but several tasks, called consumers, can read data from the data block. Double buffer and conditional flag are used to implement a mutual exclusion which prevents the producer and consumers from accessing the same data block simultaneously.

• IEMEK Journal of Embedded Systems and Applications
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• v.9 no.2
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• pp.109-115
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• 2014

Analyzing large scale data has become an important activity for many organizations. Since MapReduce is a promising tool for processing the massive data sets, there are increasing studies to evaluate the performance of various algorithms related to MapReduce. In this paper, we first develop a simulation framework that includes MapReduce workload model, data center model, and the model of data access pattern. Then we propose two algorithms that can reduce the energy consumption of MapReduce systems. Using the simulation framework, we evaluate the performance of the proposed algorithms under different application characteristics and configurations of data centers.

• Industrial Engineering and Management Systems
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• v.13 no.2
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• pp.210-220
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• 2014

This article describes a 2-in-1 methodology utilizing simulation optimization technique and Data Envelopment Analysis in measuring an accurate efficiency score. Given the high level of stochastic data in real environment, a novel methodology known as Data Collection Budget Allocation-Data Envelopment Analysis (DCBA-DEA) is developed. An example of the method application is shown in banking institutions. In addition to the novel approach presented, this article provides a new insight to the application domain of efficiency measurement as well as the way one conducts efficiency study.

• Journal of information and communication convergence engineering
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• v.22 no.3
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• pp.249-255
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• 2024

Advancements in deep learning have enhanced vision-based aggregate analysis. However, further development and studies have encountered challenges, particularly in acquiring large-scale datasets. Data collection is costly and time-consuming, posing a significant challenge in acquiring large datasets required for training neural networks. To address this issue, this study introduces a simulation that efficiently generates the necessary data and labels for training neural networks. We utilized a genetic algorithm (GA) to create optimized lists of aggregates based on the specified values of weight and particle size distribution for the aggregate sample. This enabled sample data collection without conducting sieving tests. Our evaluation of the proposed simulation and GA methodology revealed errors of 1.3% and 2.7 g for aggregate size distribution and weight, respectively. Furthermore, we assessed a segmentation model trained with data from the simulation, achieving a promising preliminary F1 score of 78.18 on the actual aggregate image.

• Journal of the Korean Operations Research and Management Science Society
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• v.28 no.3
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• pp.31-47
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• 2003

Quality design in practice highly depends on human designer's intuition and past experiences due to lack of formal knowledge about the relationship among 10 variables. This paper represents an data mining approach for developing quality design support system that integrates Case Based Reasoning (CBR) and Artificial Neural Networks (ANN) to effectively support all the steps in quality design process. CBR stores design cases in a systematic way and retrieve them quickly and accurately. ANN predicts the resulting quality attributes of design alternatives that are generated from CBR's adaptation process. When the predicted attributes fail to meet the target values, quality design simulation starts to further adapt the alternatives to the customer's new orders. To implement the quality design simulation, this paper suggests (1) the data screening method based on ξ-$\delta$ Ball to obtain the robust ANN models from the large production data bases, (2) the procedure of quality design simulation using ANN and (3) model management system that helps users find the appropriate one from the ANN model base. The integration of CBR and ANN provides quality design engineers the way that produces consistent and reliable design solutions in the remarkably reduced time.

• Journal of Korean Society on Water Environment
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• v.25 no.6
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• pp.832-839
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• 2009

Since construction of the estuarine barrage at the mouth of the Nakdong River, eutrophication and increased abundance of phytoplankton have occurred mainly due to the increased retention time in the reach. However, during the spring, there is a decrease in chlorophyll-a, as a result of an increase in zooplankton number, which preys upon phytoplankton and affects the value of chlorophyll-a. In order to emphasize the importance of zooplankton data in water quality simulation, zooplankton community data were used to simulate water quality and eutrophication at Mulgeum located in 27 km upstream from the barrage. WASP 7.2 was used as the water quality model for the river, using a monthly data set from 2003 to 2005 for model calibration and verification. The results showed that chlorophyll-a, DO, and total nitrogen in the river were simulated well during the verification period. The results of water quality simulation using zooplankton community data in the model were better than those with phytoplankton death rate, in terms of the absolute value of percent bias, root mean square error, and Nash-Sutcliffe efficiency. Those results indicate the use of zooplankton data provides more accurate simulation results for chlorophyll-a and eutrophication.

• The Transactions of the Korea Information Processing Society
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• v.4 no.8
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• pp.2106-2119
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• 1997

NDAS(Network Data Access System) is a system that interconnects high speed network(B-ISDN) and Local Area Network(Ethernet, FDDI). Nowadays, the request of multimedia data increases rapidly, so NDAS system must accept and process that request efficiently. Comparing with normal data traffic characteristics, multimedia data traffic characteristics are much complex, so it is necessary to implement the system that can cover such factor. In this paper, we suggest a NDAS hardware system model and also we propose a proper buffer size of NDAS system through the simulation. For measuring the proper buffer size of NDAS, we assumed two different simulation models, one is the case of using only one buffer and the other is the case of using two different buffers for Ethernet and FDDI, respectively. After comparing and analyzing simulation results, we suggest the proper NDAS system buffer size.