• Proceedings of the Korean Information Science Society Conference
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• 2006.10d
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• pp.56-58
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• 2006

Sensing and coverage are the two relevant tasks for a sensor network. Quality of sensor network totally depends upon the sensing ability of sensors. In a certain monitored region success of detecting or sensing an object with the help of sensor network tells that how efficiently the network coverage perform. Here in this paper we have proposed a clustering algorithm for the deployment of sensors and thus calculated the object detection probability. Actually by this work we can easily identify the present network coverage status and accordingly can take action for its improvement.

• The Transactions of the Korea Information Processing Society
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• v.5 no.6
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• pp.1603-1609
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• 1998

In this paper, we present a development of IP addresstranslator for virtual group network service and an application to Korea Information Infrastructure Pilot projects in Daeduk Science town network, This method can be a way to provide network service for limited virtual groups which are connected to a specialized network. Also this can be used to provide a new network environment for closed user groups in Intranet applications.

• Proceedings of the IEEK Conference
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• summer
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• pp.171-175
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• 2004

Network management for detail analysis can cause speed decline of application in case of lack band width by traffic increase of the explosive Internet. Because a manager requests MIB value for the desired objects to an agent by management policy, and then the agent responds to the manager. Such processes are repeated, so it can cause increase of network traffic. Specially, repetitious occurrence of sending-receiving information is very inefficient for a same object when a trend analysis of traffic is performed. In this paper, an efficient SNMP is proposed to add new PDUs into the existing SNMP in order to accept time function. Utilizing this PDU, it minimizes unnecessary sending-receiving message and collects information for trend management of network efficiently. This proposed SNMP is tested for compatibility with the existing SNMP and decreases amount of network traffic largely

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.187-188
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• 2006

In CDMA based mobile communication network, signaling messages are routed through Signaling Transfer Point(STP) which is responsible for MTP layer 3 switching. As WCDMA based UMTS network is considered as next generation technology allowing global roaming services, all nodes in networks have their own identity number called Global Title(GT). Therefore, it is essential to introduce Signaling Gateway(SG) responsible for SCCP layer switching to solve the problem each node has all GT tables including even all overseas nodes. In this paper, we propose the signaling network architecture in UMTS networks using SG and we show that we can reduce CAPEX and OPEX in each node. To show the validity of the proposed method some simulations are given in which the results can be expected by intuitive observation.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.11
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• pp.51-57
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• 2014

This paper presents an algorithmic type computing technique of process coefficient in predicting model of temperature for reheating furnace and also suggests a design method of neural network model to find an adequate value of process coefficient for arbitrary operating conditions including test conditons. The proposed neural network use furnace temperature, line speed and slab information as input variables, and process coefficient is output variable. Reasonable process coefficients can be obtained by an algorithmic procedure proposed in this paper using process data gathered at test conditons. Also, neural network model output equal process coefficient under same input conditions. This means that adquate process coefficients can be found by only computing neural network model without additive test even if operating conditions vary.

• The Journal of Information Systems
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• v.8 no.1
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• pp.109-129
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• 1999

This study deals with the problem of information dissemination in a communication network, which is defined to be the process whereby a set of messages, generated by an originator, is transmitted to all the members within the network. Since this type of message generally includes control data to manage the network or global information that all members should know, it is to be required to transmit it to all the members as soon as possible. In this study, it is assumed that a member can either transmit or receive a message and an informed member can transmit it to only one of its neighbors at time. This type of transmission is called 'local broadcasting' Several schemes of call sequencing are designed for a general-type network with nonuniform edge transmission times, and then computer simulations are performed. Some heuristics for information dissemination are proposed and tested. For this, optimal call sequence in a tree-type network, sequencing theory and graph theory are applied. The result shows that call sequencing based on the shortest path tree is the most desirable.

• The Research Journal of the Costume Culture
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• v.6 no.3
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• pp.21-33
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• 1998

A project manager is required to have systematic knowledge in the project management in fashion and apparel industry. The Network Technique is a modern project management technique and applies to almost all industry sectors, including fashion and apparel industry. Through the Network Technique, we can save project time and cost. The purpose of present study are - to introduce the Network Technique into fashion and apparel industry management area, and - to make an practical application of he Network Technique to fashion and apparel industry. For these purpose, fist we define the Network Technique, and then we apply it to fashion show as an example. Only time component is considered in this study. Cost component can be dealt with later on in the project planning area.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.155-162
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• 2003

Today's network consists of a large number of routers and servers running a variety of applications. Policy-based network provides a means by which the management process can be simplified and largely automated. In this paper we build a foundation of policy-based network modeling and simulation environment. The procedure and structure for the induction of policy rules from vulnerabilities stored in SVDB (Simulation based Vulnerability Data Base) are developed. The structure also transforms the policy rules into PCIM (Policy Core Information Model). The effect on a particular policy can be tested and analyzed through the simulation with the PCIMs and SVDB.

• Journal of Korean Society of Transportation
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• v.12 no.3
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• pp.49-64
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• 1994

The development of a mathematical modular framework based on Petri Network theory to model a traffic network is the subject of this paper. Traffic intersections are the primitive elements of a transportation network and are characterized as event driven and asynchronous systems. Petri network have been utilized to model these discrete event systems; further analysis of their structure can reveal information relevant to the concurrency, parallelism, synchronization, and deadlock avoidance issuse. The Petri-net based model of a generic traffic junction is presented. These modular networks are effective in synchronizing their components and can be used for modeling purposes of an asynchronous large scale transportation system. The derived model is suitable for simulations on a multiprocessor computer since its program execution safety is secured. The software pseudocode for simulating a transportation network model on a multiprocessor system is presented.

• Asia pacific journal of information systems
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• v.1 no.1
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• pp.63-86
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• 1991

In this paper, our concern is the artificial neural network-based patten classification, when can resolve the difficulties in the Autoregressive Moving Average(ARMA) model identification problem To effectively classify a time series into an approriate ARMA model, we adopt the Multi-layered Backpropagation Network (MLBPN) as a pattern classifier, and Extended Sample Autocorrelation Function (ESACF) as a feature extractor. To improve the classification power of MLBPN's we suggest an integrated neural network system which consists of an AR Network and many small-sized MA Networks. The output of AR Network which will gives the MA order. A step-by-step training strategy is also suggested so that the learned MLBPN's can effectively ESACF patterns contaminated by the high level of noises. The experiment with the artificially generated test data and real world data showed the promising results. Our approach, combined with a statistical parameter estimation method, will provide a way to the automation of ARMA modeling.