• Journal of Electrical Engineering and Technology
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• v.4 no.2
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• pp.149-158
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• 2009

It is important to derive reliable parameter values in the measurement-based load model development of electric power systems. However parameter estimation tasks, in practice, often face the parameter identifiability issue; whether or not the model parameters can be estimated with a given input-output data set in reliable manner. This paper introduces concepts and practical definitions of the local identifiability of model parameters. A posteriori local identifiability is defined in the sense of nonlinear least squares. As numerical examples, local identifiability of third-order induction motor (IM) model and a Z-induction motor (Z-IM) model is studied. It is shown that parameter ill-conditioning can significantly affect on reliable parameter estimation task. Numerical studies show that local identifiability can be quite sensitive to input data and a given local solution. Finally, several countermeasures are proposed to overcome ill-conditioning problem in measurement-based load modeling.

• Korean Management Science Review
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• v.19 no.2
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• pp.125-137
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• 2002

The telecommunications market is expanding rapidly and becoming more substitutive. In this environment, demand forecasting is very difficult, yet important for both practitioners and researchers. in this paper, we adopt the modeling approach proposed dy Jun and Park ［6］. The basic premise is that demand patterns result from choice behavior, where customers choose a product to maximize their utility. We apply a choice-based substitutive diffusion model to the Korean mobile telecommunication service market where digital service has completely replaced analog service. In comparison with Bass-type multigeneration models. our model provides superior fitting and forecasting performance. The choice-based model is useful in that it enables the description of such complicated environments and provides the flexibility to include marketing mix variables such as price and advertising in the regression analysis.

• Journal of the Korean Data and Information Science Society
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• v.14 no.3
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• pp.641-651
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• 2003

This study suggests the hybrid models for interest rate forecasting using structural changes (or change points). The basic concept of this proposed model is to obtain significant intervals caused by change points, to identify them as the change-point groups, and to reflect them in interest rate forecasting. The model is composed of three phases. The first phase is to detect successive structural changes in the U. S. Treasury bill rate dataset. The second phase is to forecast the change-point groups with data mining classifiers. The final phase is to forecast interest rates with backpropagation neural networks (BPN). Based on this structure, we propose three hybrid models in terms of data mining classifier: (1) multivariate discriminant analysis (MDA)-supported model, (2) case-based reasoning (CBR)-supported model, and (3) BPN-supported model. Subsequently, we compare these models with a neural network model alone and, in addition, determine which of three classifiers (MDA, CBR and BPN) can perform better. For interest rate forecasting, this study then examines the prediction ability of hybrid models to reflect the structural change.

• Journal of the Korean Society of Industry Convergence
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• v.4 no.3
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• pp.329-337
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• 2001

This simulator is developed for the analysis of a MOSFET based on Thermally Coupled Energy Transport Model(TCETM). The simulator has the ability to calculate not only stationary characteristics but also non - stationary characteristics of a MOSFET. It solves basic semiconductor devices equations including Possion equation, current continuity equations for electrons and holes, energy balance equation for electrons and heat flow equation, using finite difference method. The conventional semiconductor device simulation technique, based on the Drift-Diffusion Model (DDM), neglects the thermal and other energy-related properties of a miniaturized device. I, therefore, developed a simulator based on the Thermally Coupled Energy Transport Model (TCETM) which treats not only steady-state but also transient phenomena of such a small-size MOSFET. In particular, the present paper investigates the breakdown characteristics in transient conditions. As a result, we found that the breakdown voltage has been largely underestimated by the DDM in transient conditions.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2007.04a
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• pp.809-812
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• 2007

Automatic generation method of structural analysis model data for a truss bridge is presented through 3-D bridge information modeling based on Industry Foundation Classes(IFC). The mapping schema is proposed between a steel bridge information model based on STEP and a truss bridge information model based on the IFC. The geometry information from mapping is presented by IFC model, and SAP 2000 that can import the IFC file performs the structural analysis. Numerical analysis for a truss bridge is performed in this paper.

• Journal of the Korean Society for Railway
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• v.6 no.1
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• pp.1-9
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• 2003

This article proposes an integrated systems engineering and safety analysis model for safety-critical systems development. A methodology in system design for safety is considered during the early phase of the development life cycle of systems engineering process. The evolution of the design automation technology has enabled engineers to perform the model-based systems engineering. A Computer-Aided Systems Engineering(CASE) tool, CORE, is utilized to integrate the systems engineering model with a system safety analysis model. The results of the functional analysis phase can drive the analysis of the system safety. An example of Communications-Based Train Control(CBTC) system for an Automated Guided Transit(AGT) system demonstrated an application of the integrated model.

• Journal of the Korean Data and Information Science Society
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• v.18 no.3
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• pp.809-815
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• 2007

Since Bollerslev(1986), the GARCH model has been popular in analysing the volatility of the financial time series. In real data analysis, practitioners conventionally put the normal assumption on the innovation random variables of the GARCH model, which is often violated. In this paper, we analyse the domestic financial data based on the GARCH(1,1) model and among existing normality tests, perform the Jarque-Bera test based on the residuals. It is shown that the innovation based on the GARCH(1,1) model dose not follow the normality assumption.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.1
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• pp.228-246
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• 2011

A 3D model based approach for a face representation and recognition algorithm has been investigated as a robust solution for pose and illumination variation. Since a generative 3D face model consists of a large number of vertices, a 3D model based face recognition system is generally inefficient in computation time and complexity. In this paper, we propose a novel 3D face representation algorithm based on a pixel to vertex map (PVM) to optimize the number of vertices. We explore shape and texture coefficient vectors of the 3D model by fitting it to an input face using inverse compositional image alignment (ICIA) to evaluate face recognition performance. Experimental results show that the proposed face representation and recognition algorithm is efficient in computation time while maintaining reasonable accuracy.

• Proceedings of the KIPE Conference
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• 2019.07a
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• pp.459-460
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• 2019

The performance dynamics of battery is very sensitive to operating conditions (i.e temperature, load current, and state of charge). A model developed based on certain conditions may perform well under the similar conditions but can not accurately predict the performance for changing conditions. Thus, a generalized model is needed which can accurately emulate the battery dynamic behavior under all conditions. In addition, the components of the model should relate to the physicochemical processes that occur inside the battery. Electrochemical impedance curve shows better visible reflection of the processes inside battery as compared to voltage curve. The model trained for parameterization using neural network has better generalization than simple curve fitting. Thus, this study proposes recurrent neural network based parameterization of the Lithium ion battery model followed by impedance based identification.

• International journal of advanced smart convergence
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• v.13 no.3
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• pp.312-320
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• 2024

Healthcare services converged with ICT technology are improving quality of life and satisfaction through various customized services. In ICT-based medical services, data interchange between medical services is important, and HL7 FHIR, a medical data standard, enables efficient medical data interchange. FHIR-based medical information services using wireless data broadcasting can efficiently support massive clients. This paper proposes a function point model to evaluate the implementation cost of FHIR-based health information services using wireless data broadcasting. The proposed cost evaluation model can effectively evaluate the development cost by applying the complexity of converting medical data into FHIR format and the complexity of organizing indexes to efficiently support massive clients. The comparison of the proposed feature point evaluation model with simple feature points shows the efficiency and suitability of the proposed cost evaluation model.