• Journal of Korean Institute of Industrial Engineers
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• v.25 no.1
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• pp.111-124
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• 1999

This evapaper is toluate the forecasting performance of three neural network(NN) approaches against ARIMA model using the famous time series analysis competition data. The first NN approach is to analyze the second Makridakis (M2) Competition Data using Multilayer Perceptron (MLP) that has been the most popular NN model in time series analysis. Since it is recently known that MLP suffers from bias/variance dilemma, two approaches are suggested in this study. The second approach adopts Cascade Correlation Network (CCN) that was suggested by Fahlman & Lebiere as an alternative to MLP. In the third approach, a time series is separated into two series using Noise Filtering Network (NFN) that utilizes autoassociative memory function of neural network. The forecasts in the decomposition analysis are the sum of two prediction values obtained from modeling each decomposed series, respectively. Among the three NN approaches, Decomposition Analysis shows the best forecasting performance on the M2 Competition Data, and is expected to be a promising tool in analyzing socio-economic time series data because it reduces the effect of noise or outliers that is an impediment to modeling the time series generating process.

• Proceedings of the Korea Concrete Institute Conference
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• 1999.04a
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• pp.233-238
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• 1999

Localized failure analysis of concrete structures can be carried out effectively by modeling fracture process zone of concrete during crack initiation and propagation. But, the analysis techniques are still insufficient for crack modeling because of difficulties in numerical analysis procedure which describe progressive crack. In this paper, a finite element with embedded displacement discontinuity is introduced to remove the difficulties of remeshing for crack propagation in discrete crack model during progressive failure analysis of concrete structures. The performance of this so-called embedded crack approach for concrete failure analysis is verified by several analysis examples. The analysis results show that the embedded crack approach retains mesh size objectivity and can simulate localized failure under mixed mode loading. It can be concluded that the embedded crack approach cab be an effective alternate to the smeared and discrete crack approaches.

• ETRI Journal
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• v.20 no.2
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• pp.192-213
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• 1998

The current approaches in Object-Oriented Analysis have limitations on modeling complex real world systems because they require priori knowledge about objects and their interactions before applying them. This may be practical in small systems and systems with clear domain knowledge, but not in large real world systems with unclear domain knowledge. Our approach uses a stepwise refinement technique in a top-down manner to the Object-Oriented Analysis stage with the application of use cases. This approach is especially good for new areas where we do not know all the information in advance. We present the approach with an example of its application to the B-ISDN service modeling and distributed systems.

• Journal of the Korean Institute of Telematics and Electronics D
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• v.35D no.3
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• pp.28-38
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• 1998

In our paper is reported a new 3D(dimensional) trajectory split approach with greatly improved efficiency for the Monte Carlo simulation of the 3D profiles of implanted ionand point defect concentrations in single-crystal silicon. This approach has been successfully implemented in our TRICSI Monte Carlo code. Combined with the previously developed model for damage accumalation in our TRICSI code, this model allows phasically based dynamic simulation of 3D profiles over an subsequent process simulation such as diffusion modeling and simulation. A typical time saving of over 10 timeshas been achieved for 3D simulation. Our method ensures much better region aground the implanted area. For 1-D simulation, the optimized condition for trajectory split has set to 3,000 pseudoparticles with 2 split branches.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.1100-1102
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• 2000

Most systems in tile real world are non-linear and can be represented by the non-linear autoregressive moving average (NARMA) model. The extension of fuzzy system for modeling the system that is represented by NARMA model will be proposed in this paper. Here, fuzzy basis function (FBF) is used as fuzzy NARMA(p,q) model. Then, an approach to Identify fuzzy NARMA models based on fuzzy basis functions is proposed. The efficacy of the proposed approach is shown from experimental results.

• Computers and Concrete
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• v.17 no.6
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• pp.703-721
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• 2016

A novel approach to modeling concrete behavior at the stage of its maturing is presented in this paper. This approach assumes that at any point in the structure, concrete is composed of a set of layers that are activated in time layer by layer, based on amount of released heat that is produced during process of the concrete's maturing. This allows one to assume that each newly created layer has nominal stiffness moduli and tensile/compressive strengths. Hence introduction of explicit stiffness moduli and tensile/compressive strength dependencies on time, or equivalent time state parameter, is not needed. Analysis of plain concrete (PC) and reinforced concrete (RC) structures, especially massive ones, subjected to any kind of straining in their early stage of existence, mostly due to external loads but especially by thermal loading and shrinkage, is the goal of the approach. In this article a simple elasto-plastic softening model with creep is used for each layer and a general layered model behavior is illustrated on one-dimensional (1D) examples.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.11a
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• pp.85-89
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• 2001

Hamiltonian modeling approach has been extensively adopted for rigid body dynamics whereas its usage for deforming flexible continuum dynamics has been limited. A set of Hamilton＇s equations for flexible body motion with finite deformation has been derived and applied for a nonlinear impact problem.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.186-193
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• 1996

This paper presents a fuzzy structural modeling using the concept of Saaty's pairwise comparison matrix. The merit of this approach can be reduced the number of pairwise comparison.

• Nuclear Engineering and Technology
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• v.41 no.5
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• pp.739-746
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• 2009

This paper introduces facility operation modeling and simulation based primarily on a discrete event system modeling scheme. Many modern industrial facilities are so complex that their operational status cannot be estimated by simple calculations. In general, a facility can consist of many processes and transfers of material between processes that may be modeled as a discrete event system. This paper introduces the current status of studies on operation modeling and simulation for typical nuclear facilities, along with some examples. In addition, this paper provides insights about how a discrete event system can be applied to a model for a nuclear facility. A headend facility is chosen for operation modeling and the simulation, and detailed procedure is thoroughly described from modeling to an analysis of discrete event results. These kinds of modeling and simulation are very important because they can contribute to facility design and operation in terms of prediction of system behavior, quantification of facility capacity, bottleneck identification and efficient operation scheduling.

• Journal of Electrical Engineering and Technology
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• v.12 no.4
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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.