• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.708-710
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• 2013

Web based simulation service is utilized to computationally analyze various phenomena in real world according to the progress of network and computing technology. To provide suitable simulation service, web based simulation service will be improved by analyzing execution data. Especially, an API for data retrieval is designed, because every analyst needs different data. In this paper, we design the retrieval API for a user to analyze the web based simulation execution data, which is used on CFD (Cumputational Fluid Dynamics) and Chemistry. The designed retrieval API provides an simulation execution data depending on an user requirement such as a user type, an execution period, and an affiliation, which is based on the core information such as an user, a simulation program, and a cyberlab. Besides, a developer can provides a suitable information for a user to execute a simulation well such as a simulation statistics, an execution trend, and a frequent error.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.8
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• pp.883-888
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• 2014

Simulation is now using in various area as an effective decision analysis tool in complex environment of today. But, There is a focus to the simulation model development and execution better than result analysis. This article will emphasis to the importance of result analysis apart from model development in simulation, and will use R package for profit optimization simulation. R has a various function in statistic analysis and data manipulation, graphic display. So this research can show the value of R as a tool for simulation.

• ETRI Journal
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• v.44 no.2
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• pp.313-326
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• 2022

This study presents a noble scheme for distributed and parallel simulations with optimized agent placement for simulation instances. The traditional parallel simulation has some limitations in that it does not provide sufficient performance even though using multiple resources. The main reason for this discrepancy is that supporting parallelism inevitably requires additional costs in addition to the base simulation cost. We present a comprehensive study of parallel simulation architectures, execution flows, and characteristics. Then, we identify critical challenges for optimizing large simulations for parallel instances. Based on our cost-benefit analysis, we propose a novel approach to overcome the performance constraints of agent-based parallel simulations. We also propose a solution for eliminating the synchronizing cost among local instances. Our method ensures balanced performance through optimal deployment of agents to local instances and an adaptive agent placement scheme according to the simulation load. Additionally, our empirical evaluation reveals that the proposed model achieves better performance than conventional methods under several conditions.

• Journal of the military operations research society of Korea
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• v.27 no.2
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• pp.134-148
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• 2001

In this paper, air-defense simulation model, called "DADSim", will be introduced. DADSim(Distributed Air Defense Simulation Model) was developed by Modeling&Simulation Lab of K.N.D.U.(Korea National Defence Univ) Weapon Systems Department. This model is an analysis-purpose model in the engagement-level. DADSim can simulate not only the global air-defense or Korean Peninsula but also the local air-defense or a battle field. DADSim uses the DTED(digital terrain elevation data) LeveII it for the representation of peninsula terrain characteristics. The weapon systems cooperated in the model are low/medium-range missile systems such as HAWK, NIKE, SAM. DADSim was designed in the way of object-oriented development method, implemented by C++ language. The simulation view is an event-sequenced object-orientation. For the convenience of input, output analysis, GUI(Graphic User Interface) of menu, window, dialog box, etc. are provided to the user, For the execution of DADSim, Silicon Graphic IRIX 6.3 or high version is required. DADSim can be used for the effectiveness analysis of­defence systems. Some illustrative examples will be shown in this paper.

• Journal of the Ergonomics Society of Korea
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• v.21 no.3
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• pp.13-24
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• 2002

Reduction and prevention of human error is one of the major interests for the enhancement of system safety and availability in Nuclear Power Plants (NPPs). As human beings have become the weak point in the system safety, a systematic evaluation on human performance during emergency situation should be performed in advance to identify the potential vulnerability of human tasks. Though the data gathering and analysis from real field is an important precondition, there were no available data in nuclear field of korea. This paper presents the result of human performance analysis on emergency tasks in NNPs. Firstly, a task analysis was performed to identify the characteristics of operator tasks during emergency condition and to classify them into a set of generic emergency tasks. Secondly, simulation data were collected and analyzed for the emergency tasks using the full scope simulator of Younggwang NPPs. The analyzed human performance information cover the event diagnosis time, the execution time of each procedural step, observation parameters, typer of irrelevant response, pattern of communication among staffs, and so on. These performance data would be used for human reliability analysis and the research of human error as technical bases.

• Journal of the Korea Society of Computer and Information
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• v.23 no.11
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• pp.103-109
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• 2018

This paper presents an idea and framework to automate a full cycle business process management and re-engineering by integrating traditional business process management systems, process mining, data mining, machine learning, and simulation. We build our framework on the cloud-based platform such that various data sources can be incorporated. We design our systems to be extensible so that not only beneficial for practitioners of BPM, but also for researchers. Our framework can be used as a test bed for researchers without the complication of system integration. The automation of redesigning phase and selecting a baseline process model for deployment are the two main contributions of this study. In the redesigning phase, we deal with both the analysis of the existing process model and what-if analysis on how to improve the process at the same time, Additionally, improving a business process can be applied in a case by case basis that needs a lot of trial and error and huge data. In selecting the baseline process model, we need to compare many probable routes of business execution and calculate the most efficient one in respect to production cost and execution time. We also discuss the challenges and limitation of the framework, including the systems adoptability, technical difficulties and human factors.

• Journal of the Korea Society for Simulation
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• v.3 no.1
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• pp.29-42
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• 1994

The architecture of a MIMD-type parallel computer system is specified: a simulator is developed to support design and evaluation of systems based on the architecture: and conducted with the simulator to evaluate system performance. The horizontal/vertical-bus(H/V-bus) system architecture provides an NxN array of processing elements which communicate with each other through a network of N horizontal buses and N vertical buses. The simulator, written in SLAM II and FORTRAN, is designed to provide high-resolution in simulating the IPC mechanism. Parameters provide the user with independent control of system size, PE speed and IPC mechanism speed. Results generated by the simulator include execution times, PE utilizations, queue lengths, and other data. The simulator is used to study system performance when a partial differential equation is solved by parallel Gauss-Seidel method. For comparisons, the benchmark is also executed on a single-bus system simulator that is derived from the H/V-bus system simulator. The benchmark is also solved on a single PE to obtain data for computing speedups. An extensive analysis of results is presented.

• Journal of the Korea Society of Computer and Information
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• v.15 no.5
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• pp.49-56
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• 2010

In this paper, we implement a Hadoop based cluster for cloud computing and evaluate the performance of this cluster based on application characteristics by executing RandomTextWriter, WordCount, and PI applications. A RandomTextWriter creates given amount of random words and stores them in the HDFS(Hadoop Distributed File System). A WordCount reads an input file and determines the frequency of a given word per block unit. PI application induces PI value using the Monte Carlo law. During simulation, we investigate the effect of data block size and the number of replications on the execution time of applications. Through simulation, we have confirmed that the execution time of RandomTextWriter was proportional to the number of replications. However, the execution time of WordCount and PI were not affected by the number of replications. Moreover, the execution time of WordCount was optimum when the block size was 64~256MB. Therefore, these results show that the performance of cloud computing system can be enhanced by using a scheduling scheme that considers application's characteristics.

• The Journal of Information Systems
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• v.30 no.1
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• pp.151-177
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• 2021

Purpose This paper aims to improve management performance by effectively responding to production needs and reducing inventory through synchronizing production planning and procurement in the aviation industry. In this study, the differences in production planning and execution were first analyzed in terms of demand, supply, inventory, and process using the big data collected from a domestic aircraft manufacturers. This paper analyzed the problems in procurement and inventory management using legacy big data from ERP system in the company. Based on the analysis, we performed a simulation to derive an efficient procurement and inventory management plan. Through analysis and simulation of operational data, we were able to discover procurement and inventory policies to effectively respond to production needs. Design/methodology/approach This is an empirical study to analyze the cause of decrease in inventory turnover and increase in inventory cost due to dis-synchronize between production requirements and procurement. The actual operation data, a total of 21,306,611 transaction data which are 18 months data from January 2019 to June 2020, were extracted from the ERP system. All them are such as basic information on materials, material consumption and movement history, inventory/receipt/shipment status, and production orders. To perform data analysis, it went through three steps. At first, we identified the current states and problems of production process to grasp the situation of what happened, and secondly, analyzed the data to identify expected problems through cross-link analysis between transactions, and finally, defined what to do. Many analysis techniques such as correlation analysis, moving average analysis, and linear regression analysis were applied to predict the status of inventory. A simulation was performed to analyze the appropriate inventory level according to the control of fluctuations in the production planing. In the simulation, we tested four alternatives how to coordinate the synchronization between the procurement plan and the production plan. All the alternatives give us more plausible results than actual operation in the past. Findings Based on the big data extracted from the ERP system, the relationship between the level of delivery and the distribution of fluctuations was analyzed in terms of demand, supply, inventory, and process. As a result of analyzing the inventory turnover rate, the root cause of the inventory increase were identified. In addition, based on the data on delivery and receipt performance, it was possible to accurately analyze how much gap occurs between supply and demand, and to figure out how much this affects the inventory level. Moreover, we were able to obtain the more predictable and insightful results through simulation that organizational performance such as inventory cost and lead time can be improved by synchronizing the production planning and purchase procurement with supply and demand information. The results of big data analysis and simulation gave us more insights in production planning, procurement, and inventory management for smart manufacturing and performance improvement.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.656-657
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• 2015

Recently, Multi-Objective Optimization of design elements is an important issue in building design. Design variables that considering the specificities of the different environments should use the appropriate algorithm on optimization process. The purpose of this study is to compare and analyze the optimal solution using three evolutionary algorithms and energy modeling simulation. This paper consists of three steps: i)Developing three evolutionary algorithm model for optimization of design elements ; ii) Conducting Multi-Objective Optimization based on the developed model ; iii) Conducting comparative analysis of the optimal solution from each of the algorithms. Including Non-dominated Sorted Genetic Algorithm (NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO) and Random Search were used for optimization. Each algorithm showed similar range of result data. However, the execution speed of the optimization using the algorithm was shown a difference. NSGA-II showed the fastest execution speed. Moreover, the most optimal solution distribution is derived from NSGA-II.