• 한국정보통신설비학회:학술대회논문집
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• 한국정보통신설비학회 2007년도 학술대회
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• pp.69-72
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• 2007

In this paper, we analyze the performance of the network management system with intelligent mobile agent system. The proposed system dynamically selects appropriate its destinations. Thus, the system has an advantage of flexible network management in mobile network environments as well as dynamic change of traffic. Comparing its delay and throughput performance with the conventional SNMP based network management system, we find that the proposed mobile agent system performs better efficiency than the conventional one.

• 한국정밀공학회지
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• 제12권6호
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• pp.120-127
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• 1995

A nonlinear empirical state-space model of the Artificial Neural Network(ANN) has been developed. The nonlinear model structure incorporates characteristic, so as to enable identification of the transient response, as well as the steady-state response of a dynamic system. A hybrid feedfoward/feedback neural network, namely a Local Time Delayed Recurrent Multi-layer Perception(RMLP), is the model structure developed in this paper. RMLP is used to identify nonlinear dynamic system in an input/output sense. The feedfoward protion of the network architecture provides with the well-known curve fitting factor, while local recurrent and cross-talk connections provides the dynamics of the system. A dynamic learning algorithm is used to train the proposed network in a supervised manner. The derived dynamic learning algorithm exhibit a computationally desirable characteristic; both network sweep involved in the algorithm are performed forward, enhancing its parallel implementation. RMLP state-space and its associate learning algorithm is demonstrated through a simple examples. The simulation results are very encouraging.

• Journal of Electrical Engineering and information Science
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• 제2권6호
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• pp.64-73
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• 1997

Network technology has been developed for very high-speed networking and multimedia data whose characteristics are the continuous and bursty transmission as well as a large amount of data. With this trend users wish to view the information about the application services as well as network devices and system hardware. However, it is rarely available for the users the information of performance or faults of the application services. Most of information is limited to the information related network devices or system hardware. Furthermore, users expect the best services without knowing the service environments in the network and there is no good way of delivering the service related problems and fault information of application services in a high speed network yet. In this paper we present a web-based application management system that we have developed for the past year. It includes a method to build an agent system that uses an existing network management standards, SNMP MIB and SNMP protocols. The user interface of the system is also developed to support visualization effects with web-based Java interface which offers a convenient way not only to access management information but also to control networked applications.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2009년도 정보 및 제어 심포지움 논문집
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• pp.45-47
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• 2009

Wireless Sensor Network(WSN) has been very popular in unattended surveillance systems recently. For Applying WSN into Building Automation system(BAS), a novel hierarchial network structure and static routing algorithm which eliminates the scalability limitation of Zigbee are proposed in this paper. The static routing algorithm relying on the hierarchial network address reduces the computational complexity to a great extent and has a real-time performance which satisfies urgent applications well.

• Structural Engineering and Mechanics
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• 제16권1호
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• pp.83-99
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• 2003

The aim of this paper is to develop the ISCOSTFUN (Intelligent System for Prediction of Concrete Strength by Functional Networks) in order to provide in-place strength information of the concrete to facilitate concrete from removal and scheduling for construction. For this purpose, the system is developed using Functional Network (FN) by learning functions instead of weights as in Artificial Neural Networks (ANN). In serial functional network, the functions are trained from enough input-output data and the input for one functional network is the output of the other functional network. Using ISCOSTFUN it is possible to predict early strength as well as 7-day and 28-day strength of concrete. Altogether seven functional networks are used for prediction of strength development. This study shows that ISCOSTFUN using functional network is very efficient for predicting the compressive strength development of concrete and it takes less computer time as compared to well known Back Propagation Neural Network (BPN).

• ETRI Journal
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• 제39권6호
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• pp.841-850
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• 2017

We investigate neural network image reconstruction for magnetic particle imaging. The network performance strongly depends on the convolution effects of the spectrum input data. The larger convolution effect appearing at a relatively smaller nanoparticle size obstructs the network training. The trained single-layer network reveals the weighting matrix consisting of a basis vector in the form of Chebyshev polynomials of the second kind. The weighting matrix corresponds to an inverse system matrix, where an incoherency of basis vectors due to low convolution effects, as well as a nonlinear activation function, plays a key role in retrieving the matrix elements. Test images are well reconstructed through trained networks having an inverse kernel matrix. We also confirm that a multi-layer network with one hidden layer improves the performance. Based on the results, a neural network architecture overcoming the low incoherence of the inverse kernel through the classification property is expected to become a better tool for image reconstruction.

• 전기학회논문지
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• 제56권5호
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• pp.966-972
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• 2007

This paper presents position control of nonlinear three-dimensional crane systems using neural network approach. Such crane system generally includes very complicated characteristic dynamics and mechanical framework such that its mathematical model is expressed by strong nonlinearity. This leads difficulty in control design for the systems. We linearize the nonlinear system model to construct PID control applying well-known linear control theory and then neural network is utilized to compensate system perturbation due to linearization. Thus, control input of the crane system is composed of nominal PID and neural output signals respectively. Our method illustrates simple design procedure, but system perturbation and modelling error are overcome through a neural compensator. As well. adaptive neural control is constructed from online learning. Computer simulation demonstrates our control approach is superior to the classic control systems.

• 한국멀티미디어학회논문지
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• 제6권5호
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• pp.778-787
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• 2003

기존의 규칙기반 침입탐지 시스템은 사후처리식 규칙 추가로 인하여 새로운 변종의 공격을 탐지하지 못한다. 본 논문에서는 규칙기반 시스템의 한계점을 극복하기 위하여, 시간지연 신경망(Time Delay Neural Network; 이하 TDNN) 침입탐지 시스템을 제안한다. 네트워크상의 패킷은 바이트 단위를 하나의 픽셀로 하는 0에서 255사이 값으로 이루어진 그레이 이미 지로 볼 수 있다. 이러한 연속된 패킷이미지를 시간지연신경망의 학습패턴으로 사용한다. 정상적인 흐름과 비정상적인 흐름에 대한 패킷이미지를 학습하여 두 가지 클래스에 대한 신경망 분류기를 구현한다 개발하는 침입탐지 시스템은 알려진 다양한 침입유형뿐만 아니라, 새로운 변종에 대해서도 분류기의 유연한 반응을 통하여 효과적으로 탐지할 수 있다.

• 한국컴퓨터정보학회논문지
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• 제18권12호
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• pp.113-121
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• 2013

본 논문에서는 네트워크의 전반적인 구조와 함께 트래픽 흐름을 한 눈에 보여줄 수 있는 거시적인 view를 제공하는 네트워크 기상도 시스템을 위한 모델을 제안하고, 이를 설계 및 구현 하여 실제 전국 규모의 연구망에 구축 및 활용된 사례를 소개한다. 개발된 기상도 시스템은 전반적인 네트워크 사용 현황 정보에 더해 네트워크 내의 주요 라우터 노드 및 백본 링크들에 대한 현 상황은 물론 과거의 주요 트래픽 플로우 상황 정보들을 모두 데이터베이스화하고, 이렇게 축적된 데이터를 바탕으로 웹 기반의 질의 응답 기능까지 모두 제공하는 통합 관리 시스템으로, 저비용으로 효율적인 네트워크 관리 시스템을 필요로 하는 중소규모 망 사업자 및 관리자들에게 하나의 좋은 관리 도구가 될 수 있을 것이다.

• Smart Structures and Systems
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• 제32권1호
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• pp.49-59
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• 2023

A bridge transportation network supplies products from various source nodes to destination nodes through bridge structures in a target region. However, recent frequent earthquakes have caused damage to bridge structures, resulting in extreme direct damage to the target area as well as indirect damage to other lifeline structures. Therefore, in this study, a surrogate model-based comprehensive framework to estimate the seismic resilience of bridge transportation networks is proposed. For this purpose, total system travel time (TSTT) is introduced for accurate performance indicator of the bridge transportation network, and an artificial neural network (ANN)-based surrogate model is constructed to reduce traffic analysis time for high-dimensional TSTT computation. The proposed framework includes procedures for constructing an ANN-based surrogate model to accelerate network performance computation, as well as conventional procedures such as direct Monte Carlo simulation (MCS) calculation and bridge restoration calculation. To demonstrate the proposed framework, Pohang bridge transportation network is reconstructed based on geographic information system (GIS) data, and an ANN model is constructed with the damage states of the transportation network and TSTT using the representative earthquake epicenter in the target area. For obtaining the seismic resilience curve of the Pohang region, five epicenters are considered, with earthquake magnitudes 6.0 to 8.0, and the direct and indirect damages of the bridge transportation network are evaluated. Thus, it is concluded that the proposed surrogate model-based framework can efficiently evaluate the seismic resilience of a high-dimensional bridge transportation network, and also it can be used for decision-making to minimize damage.