• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.3
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• pp.353-361
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• 2001

Recently the boundary element method has been developed swiftly. The boundary element method is an efficient and accurate means for analysis of two dimensional elastic crack problems. This paper is concerned with the evaluation and the prediction of the stress intensity factor(SIF) for the crack emanating from the circular hole using boundary element method-neural network. The SIF of the crack emanating from the hole was calculated by using boundary element method. Neural network is used to evaluate and to predict SIF from the results of boundary element method. The organized neural network system (structure of four processing element) was learned with the accuracy 99%. The learned neural network system could be evaluated and predicted with the accuracy of 83.3% and 71.4% (in cases of SIF and virtual SIF). Thus the proposed boundary element method-neural network is very useful to estimate the SIF.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1994.10a
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• pp.133-140
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• 1994

In this paper, we have proposed a new method to determine the initial billet for the forged products using a function approximation in neural networks. the architecture of neural network is a three-layer neural network and the back propagation algorithm is employed to train the network. By utilizing the ability of function approximation of neural network, an optimal billet is determined by applying nonlinear mathematical relationship between shape ratio in the initial billet and the final products. A volume of incomplete filling in the die is measured by the rigid-plastic finite element method. The neural network is trained with the initial billet shape ratio and that of the un-filled volume. After learning, the system is able to predict the filling region which are exactly the same or slightly different to results of finite element method. It is found that the prediction of the filling shape ratio region can be made successfully and the finite element method results are represented better by the neural network.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.51 no.8
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• pp.449-456
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• 2002

3D Equivalent magnetic Circuit Network Method (EMCNM) is comparatively the easy way that analyzes 3D models of Electric Machine by using permeance as a distributive magnetic circuit parameter under the existing magnetic equivalent circuit method and Numerical Method. The existing 3D EMCNM could not correctly describe the shape of an analysis target when using rectangular shape element or fan shape element, so it made errors when calculating permeance. Therefore, this paper proposes the trapezoidal element contained rectangular element, fan-shape element, and quadrilateral element to express a shape. The proposed method in this research was confirmed as a useful and an accurate method through comparing with the analysis result of SRM model that is sufficiently guaranteed by 2D-Analysis.

• Transactions of Materials Processing
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• v.4 no.3
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• pp.214-221
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• 1995

In the paper, we have proposed a new method to determine the initial billet for the forged products using a function approximation in the neural network. The architecture of neural network is a three-layer neural network and the back propagation algorithm is employed to train the network. By utilizing the ability of function approximation of a neural network, an optimal billet is determined by applying the nonlinear mathematical relationship between the aspect ratios in the initial billet and the final products. The amount of incomplete filling in the die is measured by the rigid-plastic finite element method. The neural network is trained with the initial billet aspect ratios and those of the unfilled volumes. After learning, the system is able to predict the filling regions which are exactly the same or slightly different to the results of finite element simulation. This new method is applied to find the optimal billet size for the plane strain rib-web product in cold forging. This would reduce the number of finite element simulation for determining the optimal billet size of forging product, further it is usefully adapted to physical modeling for the forging design.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.138-139
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• 2021

This work introduces neural network based simulation for Poisson Boltzmann equation. First, samples are generated via a finite element method, whose pairs are used to train neural network. We report the performance of the neural network.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.7
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• pp.1756-1764
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• 1996

In this paper, we propose a new switch element of buffered Banyan network and analysis it. The proposed switch element consists of CASO(Content ASsociated Output) buffers, its controller and 2*2 crossbar switch. This switch element increase the performance of buffered Banyan network by removing HOL blocking. Also, we analyze the proposed switch element by mathematical modelling method based on MY analysis model which is one of earier proposed models.

• Proceedings of the Korean Information Science Society Conference
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• 2000.04a
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• pp.187-189
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• 2000

Nowadays, efforts are in progress for the standardization of CORBAbased telecommunication network management framework. To implement a network management system based on the framework completely, CORBA ORB and some of CORBA service should be installed in the element. And then, there must be the naming tree, which correspond to the containment relationships between components in the network element. If we use conventional OMG naming service to form the naming tree, all MOs, a software fragmenthat corresponds to each component in a system, should be instantiated. However, the network element is usually a kind of limited resource system, which cannot provide sufficient resources for applications run on it. Hence, instantiatingall MOs can cause problems for that kind of system, This paper presents Smart Naming Service architecture as a solution to the problem.

• Proceedings of the IEEK Conference
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• 2003.11c
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• pp.172-175
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• 2003

In this paper we propose a method fur the evaluation of the quality of service for VoIP services in NGN. Specifically, let us anatomize the elements of delay of a voice connection in the network in an end-to-end manner and investigate expected value at each point. We extract the delay time in each element in the network such as gateway, network node, and terminal equipment, and estimate an upper bound fur the tolerable delay in each element.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.10A
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• pp.1717-1726
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• 2001

본 논문에서는 CORBA(Common Object Request Architecture)기반의 TINA(Telecommunications Information Networking Architecture) 분산체계에서 통신망 하부 장비들이 SNMP(Simple Network Management Protocol) 혹은 TMN(Telecommunications Management Network) 체계로 혼재되어 관리되는 네트워크의 NE(Network Element)들을 효율적으로 통합 관리할 수 있는 Generic Interface Adaptor(GIA)를 제안하고 이를 설계 및 구현하였다. GIA는 message mapping, protocol conversion 및 DBMS를 이용한 Object Abstract Translation(OAT)을 통해서 각 관리체계에 맞도록 관리정보를 변환시키며, 이를 통해 TINA EML(Element Management Layer) component와 SNMP NE agent 간의 상호연동을 가능하게 한다.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.6
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• pp.539-546
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• 2006

The Air Quality Sensor(AQS), located near the fresh air inlet, serves to reduce the amount of pollution entering the vehicle cabin through the HVAC(heating, ventilating, and air conditioning) system by sending a signal to close the fresh air inlet door/ventilation flap when the vehicle enters a high pollution area. The sensor module which includes two independent sensing elements for responding to diesel and gasoline exhaust gases, and temperature sensor and humidity sensor was designed for intelligent AQS in automobile. With this sensor module, AVR microcontroller was designed with back propagation neural network to a powerful gas/vapor pattern recognition when the motor vehicles pass a pollution area. Momentum back propagation algorithm was used in this study instead of normal backpropagation to reduce the teaming time of neural network. The signal from neural network was modified to control the inlet of automobile and display the result or alarm the situation in this study. One chip microcontroller, ATmega 128L(ATmega Ltd., USA) was used for the control and display. And our developed system can intelligently reduce the malfunction of AQS from the dampness of air or dense fog with the backpropagation neural network and the input sensor module with four sensing elements such as reducing gas sensing element, oxidizing gas sensing element, temperature sensing element and humidity sensing element.