• Proceedings of the Korea Society for Simulation Conference
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• 1994.10a
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• pp.12-12
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• 1994

Simulation output knowledge analysis is one of problem-solving and/or knowledge adquistion process by investgating the system behavior under study through simulation . This paper describes an approach to simulation outputknowldege analysis using fuzzy neural network model. A fuzzy neral network model is designed with fuzzy setsand membership functions for variables of simulation model. The relationship between input parameters and output performances of simulation model is captured as system behavior knowlege in a fuzzy neural networkmodel by training examples form simulation exepreiments. Backpropagation learning algorithms is used to encode the knowledge. The knowledge is utilized to solve problem through simulation such as system performance prodiction and goal-directed analysis. For explicit knowledge acquisition, production rules are extracted from the implicit neural network knowledge. These rules may assit in explaining the simulation results and providing knowledge base for an expert system. This approach thus enablesboth symbolic and numeric reasoning to solve problem througth simulation . We applied this approach to the design problem of broadband communication network.

• Journal of the Korea Society for Simulation
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• v.8 no.4
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• pp.33-51
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• 1999

In this study, the system level network simulation is considered with adaptive array antenna in CDMA mobile communication system. A network simulation framework is implemented based on IS-95A/B system to consider dynamic handoff, system level network behavior, and deploying strategy into the overall CDMA mobile communication network under adaptive array algorithm. Its simulation model, such as vector channel model, adaptive beam forming antenna model, handoff model, and power control model, are described in detail with simulation block. In order to maximize SINR of received signal at antenna, Maximin algorithm is particularly considered, and it is computed at each simulation snap shot with SINR based power control and handoff algorithm. Graphic user interface in this system level network simulator is also implemented to define the simulation environments and to represent simulation results on real mapping system. This paper also shows some features of simulation framework and simulation results.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.3 no.4
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• pp.755-764
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• 1999

In this study, the system level network simulation is considered with adaptive array antenna in CDMA mobile communication system. A network simulation framework is implemented based on IS-95A/B system to consider dynamic handoff, system level network behavior, and deploying strategy into the overall CDMA mobile communication network under adaptive array algorithm. Its simulation model, such as vector channel model, adaptive beam forming antenna model, handoff model, and power control model, are described in detail with simulation block. In order to maximize SINR of received signal at antenna, maximin algorithm is particularly considered, and it is computed at each simulation snap shot with SINR based power control and handoff algorithm. Graphic user interface in this system level network simulator is also implemented to define the simulation environments and to represent simulation results on real mapping system. This paper also shows some features of simulation framework and simulation results.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1568-1570
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• 2005

The internet is already a part of life. It is very convenient and people can do almost everything with internet that should be done in real life. Along with the increase of the number of internet user, various network attacks through the internet have been increased as well. Also, Large-scale network attacks are a cause great concern for the computer security communication. These network attack becomes biggest threat could be down utility of network availability. Most of the techniques to detect and analyze abnormal traffic are statistic technique using mathematical modeling. It is difficult accurately to analyze abnormal traffic attack using mathematical modeling, but network simulation technique is possible to analyze and simulate under various network simulation environment with attack scenarios. This paper performs modeling and simulation under virtual network environment including $NGSS^{1}$ system to analyze abnormal traffic-flooding attack.

• 한국정보통신설비학회:학술대회논문집
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• 2006.08a
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• pp.39-42
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• 2006

There are various analysis functions(including prediction of path loss, analyzing of capacity and coverage, etc.) of simulation tool to design and optimize the mobile communication network. Its reliability absolutely effects the performance of mobile communication network. Especially as the wireless network highly advancing focused on data service, it more needs to research and develop on the standard establishment of reliability of the simulation tool. Also it is important the systematic research how to improve the reliability of simulation tool. In this paper, to give the concrete process and skill about how to improve reliability, we define the kinds of reliability at first. And then we explain the comparison results between real field measurement data and theoretic simulation data.

• Journal of Astronomy and Space Sciences
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• v.28 no.3
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• pp.241-252
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• 2011

A simulated network protocol provides the capability of distributed simulation to a generic simulator. Through this, full coverage of management of data and service handling among separated simulators is achieved. The distributed simulation environment is much more conducive to handling simulation load balancing and hazard treatment than a standalone computer. According to the simulated network protocol, one simulator takes on the role of server and the other simulators take on the role of client, and client is controlled by server. The purpose of the simulated network protocol is to seamlessly connect multiple simulator instances into a single simulation environment. This paper presents the development of a simulated network (simNetwork) that provides the capability of distributed simulation to a generic simulator (GenSim), which is a software simulator of satellites that has been developed by the Korea Aerospace Research Institute since 2010, to use as a flight software validation bench for future satellite development.

• Journal of the Korea Society for Simulation
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• v.14 no.3
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• pp.67-77
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• 2005

Logistics network of the enterprise is defined to determine the optimal node and link considering the production, inventory and transportation based on the demand forecasting. This study consider the optimal logistics network of A painter company which maintain the existing transportation network and plan to relocate its plants and build new distribution centers. For this, we design possible alternative scenarios and install the simulation models for analysis of each scenario. The result of simulation will help the proper logistic network and determining the size of distribution center further.

• Journal of the Korea Society for Simulation
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• v.3 no.1
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• pp.151-165
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• 1994

Broadband network based on the Asynchronous Transfer Mode(ATM) concept are becoming the target technology for the emerging Broadband Integrated Services Digital Network(B-ISDN). Since B-ISDN is very complex and requites a great amount of investment, optimum design and performance analysis of such systems are very important. Simulation can be widely used to analyze and examine the broadband network behavior. However, for the complicated system like broadband networks it is extremely difficult and time-consuming to develop a complete model for simulation. In this paper, an object-oriented modeling approach for the broadband network simulation is presented for the effective and efficient modeling. Object-oriented approaches can provide a good structuring capability for complicated simulation models and facilitate the development of reusable and extensible simulation models. We have developed an object-oriented model which consists of object model and behavior model. In the object mode., the components of the broadband network and both constant bit rate(CBR) and variable bit rate(VBR) traffic types of call level, burst level, and cell level are modeled as object classes. In the behavior model, the dynamic features for each object class are represented using the state transition diagram. It has been shown by illustration that objectoriented modeling is an effective tool for modeling the complicated B-ISDN.

• Journal of Internet Computing and Services
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• v.25 no.4
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• pp.1-6
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• 2024

OMNeT++ (Objective Modular Network Testbed in C++) is an extensible and modular C++ simulation library and framework for building network simulators. OMNeT++ provides simulation models independently developed for various fields, including sensor networks, and Internet protocols. This enables researchers to use the tools and features required for their desired simulations. OMNeT++ uses NED (Network Description) Language to define nodes and network topologies, and it is able to implement the creation and behavior of defined network objects in C++. Moreover, the INET framework is an open-source model library for the OMNeT++ simulation environment, containing models for various networking protocols and components, making it convenient for designing and validating new network protocols. This paper aims to explain the concepts of OMNeT++ and the procedures for network simulation using the INET framework to assist novice researchers in modeling and analyzing various network scenarios.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.203-206
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• 2004

Recently home network has been developed in the field of Information Technology. And there are many protocols for smart home, such as Lonworks, Echonet, KNX, LnCP etc. However, the performance evaluation has not been nearly known between the protocols. Hence, this paper evaluates the performance of KNX by Konnex Association and LnCP(Living network Control Protocol) by LG Electronics. We developed simulation model using flowchart of KNX and LnCP and simulation scenario through analysis of message to be generated in the home network. Furthermore, we evaluate simulation performance, such as mean transmission delay, maximum transmission delay, and collision rate of both protocols.