• 한국CDE학회논문집
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• 제5권1호
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• pp.12-22
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• 2000

Network and Internet technology opens up another domain for building future CAD/CAM environment. The environment will be global, network-centric, and spatially distributed. In this paper, we present an approach for network-centric feature-based modeling in a distributed design environment. The presented approach combines the current feature-based modeling technique with distributed computing and communication technology for supporting product modeling and collaborative design activities over the network. The approach is implemented in a client/server architecture, in which Web-enabled feature modeling clients, neutral feature model server, and other applications communicate with one another via a standard communication protocol. The paper discusses how the neutral feature model supports multiple views and maintains naming consistency between geometric entities of the server and clients. Moreover, it explains how to minimize the network delay between the server and client according to incremental feature modeling operations.

• 대한전기학회논문지
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• 제43권5호
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• pp.838-847
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• 1994

Although there is an analytical proof of modeling capability of the neural network, the convergency error in nonlinearity modeling is inevitable, since the steepest descent based practical larning algorithms do not guarantee the convergency of modeling error. Therefore, it is difficult to apply the neural network to control system in critical environments under an on-line learning scheme. Although the convergency of modeling error of a neural network is not guatranteed in the practical learning algorithms, the convergency, or boundedness of tracking error of the control system can be achieved if a proper feedback control law is combined with the neural network model to solve the problem of modeling error. In this paper, the neural network is introduced for compensating a system uncertainty to control a nonlinear dynamic system. And for suppressing inevitable modeling error of the neural network, an iterative neural network learning control algorithm is proposed as a virtual on-line realization of the Adaptive Variable Structure Controller. The efficiency of the proposed control scheme is verified from computer simulation on dynamics control of a 2 link robot manipulator.

• Journal of Electrical Engineering and Technology
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• 제12권4호
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• pp.1548-1555
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• 2017

This paper attempts to employ and investigate neural based approaches as interpolation tools for modeling of Switched Reluctance Motor (SRM) drive. Precise modeling of SRM is essential to analyse the performance of control strategies for variable speed drive application. In this work the suitability of Generalized Regression Neural Network (GRNN) and Extreme Learning Machine (ELM) in addition to conventional neural network are explored for improving the modeling accuracy of SRM. The neural structures are trained with the data obtained by modeling of SRM using Finite Element Analysis (FEA) and the trained neural network is incorporated in the model of SRM drive. The results signify the modeling accuracy with GRNN model. The closed loop drive simulation is performed in MATLAB/Simulink environment and the closeness of the results in comparison with the experimental prototype validates the modeling approach.

• 마이크로전자및패키징학회지
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• 제20권4호
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• pp.75-79
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• 2013

A power distribution network (PDN) is a network that provides connection between the voltage source supply and the power/ground terminals of a microprocessor chip. It consists of a voltage regulator module, a printed circuit board, a package substrate, a microprocessor chip as well as decoupling capacitors. For power integrity analysis, the board and package layouts have to be transformed into an electrical network of resistor, inductor and capacitor components which may be expressed using the S-parameters models. This modeling process generally takes from several hours up to a few days for a complete board or package layout. When the board and package layouts change, they need to be re-extracted and the S-parameters models also need to be re-generated for power integrity assessment. This not only consumes a lot of resources such as time and manpower, the task of PDN modeling is also tedious and mundane. In this paper, a block-based PDN modeling is proposed. Here, the board or package layout is partitioned into sub-blocks and each of them is modeled independently. In the event of a change in power rails routing, only the affected sub-blocks will be reextracted and re-modeled. Simulation results show that the proposed block-based PDN modeling not only can save at least 75% of processing time but it can, at the same time, keep the modeling accuracy on par with the traditional PDN modeling methodology.

• International Journal of Contents
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• 제5권4호
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• pp.88-93
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• 2009

It is important for communication networks to possess the capability to overcome failures and provide survivable services. We address modeling and analysis of performability affected by both performance and availability of system components for a token ring network under failure and repair conditions. Stochastic reward nets (SRN) is an extension of stochastic Petri nets and provides compact modeling facilities for system analysis. In this paper, hierarchical SRN modeling techniques are used to overcome state largeness problem. The upper level model is used to compute availability and the lower level model captures the performance. And Normalized Throughput Loss (NTL) is obtained for the composite ring network for each node failures occurrence as a performability measure. One of the key contributions of this paper constitutes the Petri nets modeling techniques instead of complicate numerical analysis of Markov chains and easy way of performability analysis for a token ring network under SRN reward concepts.

• 방송공학회논문지
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• 제13권1호
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• pp.92-98
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• 2008

네트워크 데이터 모델링은 침입 탐지 시스템의 성능 평가, 네트워크 모니터링, 네트워크 데이터 분석 기법 연구에 있어서 반드시 필요한 연구이다. 네트워크 데이터의 모델링에는 반드시 네트워크의 실제 데이터를 분석하고, 분석된 데이터를 이용하여 효과적으로 데이터를 구성하여야만, 실제 네트워크 데이터의 충분한 정보를 모델링 된 데이터에 반영할 수 있다. 본 연구에서는 대규모의 네트워크 데이터에서 실제 네트워크에서 사용 가능한 모든 성분에 대해 수량화하였으며, 수량화 된 데이터를 통계적 분석방법을 통하여 모델링 데이터에서 가장 효과적인 분류 기준으로 작용할 수 있는 성분을 분석하였다.

• 소성∙가공
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• 제27권1호
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• pp.28-36
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• 2018

Electromagnetic forming is a type of high-speed forming process to deform a workpiece through a Lorentz force. As the high strain rate in an electromagnetic-forming simulation causes infeasibility in determining constitutive parameters, we employed inverse parameter estimation in the previous study. However, the inverse parameter estimation process required us to spend considerable time, which leads to an increase in computational cost. To overcome the computational obstacle, in this research, we applied two types of surrogate modeling methods and compared them to each other to evaluate which model is best for the electromagnetic-forming simulation. We exploited an artificial neural network and we reduced-order modeling methods. During the construction of a reduced-order model, we extracted orthogonal bases with proper orthogonal decomposition and predicted basis coefficients by utilizing an artificial neural network. After the construction of the surrogate models, we verified the artificial neural network and reduced-order models through training and testing samples. As a result, we determined the artificial neural network model is slightly more accurate than the reduced-order model. However, the construction of the artificial neural network model requires a considerably larger amount of time than that of the reduced-order model. Thus, a reduced order modeling method is more efficient than an artificial neural network for estimating the electromagnetic forming and for the rapid approximation of structural simulations which needs repetitive runs.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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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.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.488-491
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• 1997

The PMGS(Plant Model Generating System) was developed based on modular modeling method and fluid network calculation concept. Fluid network calculation is used as a method of real-time computation of fluid network, and the module which has a topology with node and branch is defined to take advantages of modular modeling. Also, the database which have a shared memory as an instance is designed to manage simulation data in real-time. The applicability of the PMGS was examined implementing the HRSG(Heat Recovery Steam Generator) control logic on DCS.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 추계종합학술대회 논문집(1)
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• pp.181-184
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• 2000

In this paper, the design of WDM network using the traffic estimation modeling is implemented. Because of the lack of data of real traffic volumes, the information of statistic data is used. using the modeling results, the WDM channels is assigned for each node, and the network is simulated using OPNET simulation tools. As a result, the realistic WDM network design for Korea topology is proposed.