• Korean Journal of Computational Design and Engineering
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• v.13 no.4
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• pp.243-254
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• 2008

As modular production becoming increasingly widespread in globalized manufacturing industries, sub modules or parts of the final product are being provided by many suppliers. Some part suppliers design their own products for themselves. In some cases, part suppliers provide the same type of product to multiple OEM companies. Because all part suppliers and OEM companies typically cannot use the same CAD system, engineering change in the CAD model of one company cannot be directly propagated to related CAD models of other companies. Even if two companies use the same CAD system, it may be difficult to share their CAD model owing to corporate security policy. In this paper, a neutral reference model that consists of a neutral skeleton model and an external reference data model is proposed as a new medium for the sharing and propagation of engineering change information among collaborating companies.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.120-123
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• 1997

This paper presents models of fuzzy inference systems that can be built from a set of input-output training data pairs through hybrid structure-parameter learning. Fuzzy inference systems has the difficulty of parameter learning. Here we develop a coding format to determine a fuzzy neural network(FNN) model by chromosome in a genetic algorithm(GA) and present systematic approach to identify the parameters and structure of FNN. The proposed FNN can automatically identify the fuzzy rules and tune the membership functions by modifying the connection weights of the networks using the GA and the back-propagation learning algorithm. In order to show effectiveness of it we simulate and compare with conventional methods.

• Proceedings of the IEEK Conference
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• 2004.06a
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• pp.143-146
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• 2004

Signals of the short wave part of centimetre, millimetre and optic wave length ranges are being broadly used in the communication, location and remote sensing systems with space channels. In this case the presence of discrete non-uniform mediums like orbital debris, space dust and other discrete formations in the propagation channel may have substantial influence upon the characteristics of wave processes. and thus upon the data system quality. Mathematical models for studying the discrete non-uniform mediums effect on the characteristics of electromagnetic wave propagation are analyzed in this paper.

• Journal of Korean Society of Transportation
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• v.20 no.1
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• pp.101-110
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• 2002

This study first explains general features of traffic assignment models and network loading methods, and investigates the relationship between them. Then it introduces a dynamic network loading method, which accounts far time variable additionally. First of all, this study suggests that it is important to consider some requirements for the dynamic network loading, such as causality, FIFO(First-In-First-Out) discipline, the flow propagation, and the flow conservation. The details of dynamic network loafing methods are explained in the form of algorithm, and numerical examples are shown in the test network by adopting deterministic queuing model for a link Performance function.

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.1935-1939
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• 2006

In this study, the new dispersion-correction terms are added to leap-frog finite difference scheme for the linear shallow-water equations with the purpose of considering the dispersion effects such as linear Boussinesq equations for the propagation of tsunamis. And, dispersion-correction factor is determined to mimic the frequency dispersion of the linear Boussinesq equations. The numerical model developed in this study is tested to the problem that initial free surface displacement is a Gaussian hump over a constant water depth, and the results from the numerical model are compared with analytical solutions. The results by present numerical model are accurate in comparison with the past models.

• International Journal of Aeronautical and Space Sciences
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• v.17 no.4
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• pp.491-500
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• 2016

This paper presents a highly efficient aeroelastic optimization method based on a surrogate model; the model is verified by considering the case of a high-aspect-ratio composite wing. Optimization frameworks using the Kriging model and genetic algorithm (GA), the Kriging model and improved particle swarm optimization (IPSO), and the back propagation neural network model (BP) and IPSO are presented. The feasibility of the method is verified, as the model can improve the optimization efficiency while also satisfying the engineering requirements. Moreover, the effects of the number of design variables and number of constraints on the optimization efficiency and objective function are analysed in detail. The accuracy of two surrogate models in aeroelastic optimization is also compared. The Kriging model is constructed more conveniently, and its predictive accuracy of the aeroelastic responses also satisfies the engineering requirements. According to the case of a high-aspect-ratio composite wing, the GA is better at global optimization.

• Journal of the Korean Institute of Gas
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• v.3 no.2 s.7
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• pp.34-42
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• 1999

The discrete variables such as time and sequence must be considered for automating HAZOP analysis of batch processes in contrast with continuous processes. Because these variables can not be explained by the method used in the HAZOP analysis of continuous processes, we have developed the methodology for HAZOP analysis of batch processes on the basis of the relation between discrete variables and continuous ones. In this study, we have discussed the performance of the methodology on a Latex batch process to evaluate its effectiveness.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.21 no.3
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• pp.248-257
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• 2011

It was reported recently that railpads can be included as a continuous elastic support of the rail and the model was justified from experiments. In general, however, railpads are installed discretely on sleepers with a regular span. The effect of the discrete railpad was not clearly examined so far in such a high frequency range. In this paper, the effect of the railpads in track modelling for high frequencies is investigated by means of the finite element analysis. To do that, the railpads are regarded as 'a continuous elastic support' and 'a discrete elastic support' in this paper. The dispersion relations and decaying features are predicted and compared between the two models up to 80 kHz.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.32 no.4
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• pp.395-401
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• 1996

In this paper, the propagation characteristics due to the horizontal water temperature variations in the sea such as thermal fronts is analyzed by the ray theory. Two models for the temperature anomaly layer are chosen. One is a plane type and the other is a cylindrical type. In the plane type, the temperature increases linearly from a isothermal region to 5km with the gradient of about $2^{\circ}C.$/km, and decreases with the same gradient in next 5km. In the cylindrical type, water temperature increases only with the same gradient from a half cylindrical thermal boundary surface. The result showed that the gradient of acoustic rays decreases in the temperature increasing region and vice versa in temperature decreasing region. And, the transmission loss due to the temperature variation was less than O.2dB in the plane type model as well as in the cylindrical one.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.8
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• pp.777-782
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• 2011

Neural network technique is widely employed in the fields of signal processing, control systems, pattern recognition, etc. Learning of neural networks is an important procedure to accomplish dynamic system modeling. This paper presents a novel learning approach for differential neural network models based on the Kalman-Bucy filter theory. We construct an augmented state vector including original neural state and parameter vectors and derive a state estimation rule avoiding gradient function terms which involve to the conventional neural learning methods such as a back-propagation approach. We carry out numerical simulation to evaluate the proposed learning approach in nonlinear system modeling. By comparing to the well-known back-propagation approach and Kalman-Bucy filtering, its superiority is additionally proved under stochastic system environments.