• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.2
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• pp.412-424
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• 2019

In this study, the changes of the streamflow characteristics of the watershed were analysed depending on the infiltration methods of CAT. The study area, Boryeong-dam watershed located in Chungcheongnam-do area, has been suffered from severe drought in recent years and stabilized regarding on the storage rate through efforts such as constructing a channel connecting the upstream of Boryeong-dam from the downstream of the Geum river. In this study, the effects of soil infiltration parameters on the watershed streamflow characteristics were analyzed by the infiltration methods of CAT such as Rainfall Excess, Green&Ampt and Horton. And the parameter calibrations were conducted by SCEUA-P, a global optimization technique module of the PEST, the package for parameter optimization and uncertainty analysis, to compare the yearly variations of soil parameters for infiltration methods of CAT. In addition, the streamflow characteristics were analyzed for three infiltration methods by applying three different scenarios, such as applying calibrated parameters for every years to simulate the model for each years, applying calibrated parameters for the entire period to simulate the model for entire period, and applying the average value of yearly calibrated parameters to simulate the model for entire period.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.22 no.1
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• pp.25-33
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• 2010

The matching condition at a perforated wall with vertical slits involves the permeability parameter, which can be calculated by two different methods. One expresses the permeability parameter in terms of energy dissipation coefficient and jet length at the perforated wall, being advantageous in that all the related variables are known, but it gives wrong result in the limit of long waves. The other expresses the permeability parameter in terms of friction coefficient and inertia coefficient, giving correct result from short to long waves, but the friction coefficient should be determined on the basis of a best fit between measured and predicted values of such hydrodynamic coefficients as reflection and transmission coefficients. In the present study, an empirical formula for the friction coefficient is proposed in terms of known variables, i.e., the porosity and thickness of the perforated wall and the water depth. This enables direct estimation of the friction coefficient without invoking a best fit procedure. To obtain the empirical formula, hydraulic experiments are carried out, the results of which are used along with other researchers' results. The proposed formula is used to predict the reflection and transmission coefficients of a curtain-wall-pile breakwater, the upper part of which is a curtain wall and the lower part consisting of a perforated wall with vertical slits. The concurrence between the experimental data and calculated results is good, verifying the appropriateness of the proposed formula.

• Proceedings of the KIEE Conference
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• 2008.04c
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• pp.16-18
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• 2008

Several methods are proposed in the literature for losses segregation of single phase induction motor. Generally we could divide two methods for experimental determination of losses segregation for single phase induction motor. The one is relatively complicated method based on Parameter estimation of single phase induction motor. The other is simple method based on IEEE Standard 114. Segregation of losses in single phase induction motor is more complicated than that in three phase induction motor, because of the backward magnetic field component in the motor and multiplicity of different single phase type. In this paper, therefore, we studied losses segregation of capacitor-run single phase induction motor.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.7
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• pp.327-331
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• 2006

We have proposed an image restoration method for a bi-level waveforms whose number of edges is known to us. Based on the information, we parametrize a bi-level waveform using the locations of edges and restore the waveform by estimating the parameter. We estimated the locations by maximizing the correlation coefficients between the hi-level waveform and the measured waveform. In experiments using two dimensional barcode images of the PDF417 specification, the proposed method showed better performance than conventional methods in the sense that the proposed method was able to decode barcode images that were not decoded by the conventional methods.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.11a
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• pp.409-412
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• 2001

Identification of systems operating in closed loop has long been of prime interest in industrial applications. The fundamental problem with closed-loop data is the correlation between the unmeasurable noise and the input. This is the reason why several methods that work in open loop fail when applied to closed-loop data. The prediction error based approaches to the closed-loop system are divided to direct method and indirect method. Both of direct and indirect methods are known to be applied to the closed-loop data without critical modification. But the direct method induces the bias error in the experimental frequency response function and this bias error may deteriorates the parameter estimation performance

• Communications for Statistical Applications and Methods
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• v.20 no.4
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• pp.321-327
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• 2013

Bayesian and empirical Bayesian methods have become quite popular in the theory and practice of statistics. However, the objective is to often produce an ensemble of parameter estimates as well as to produce the histogram of the estimates. For example, in insurance pricing, the accurate point estimates of risk for each group is necessary and also proper dispersion estimation should be considered. Well-known Bayes estimates (which is the posterior means under quadratic loss) are underdispersed as an estimate of the histogram of parameters. The adjustment of Bayes estimates to correct this problem is known as constrained Bayes estimators, which are matching the first two empirical moments. In this paper, we propose a way to apply the constrained Bayes estimators in insurance pricing, which is required to estimate accurately both location and dispersion. Also, the benefit of the constrained Bayes estimates will be discussed by analyzing real insurance accident data.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.95-98
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• 1997

In this paper, several nonlinear system identification theories and the application of these methods to the physiological system are reviewed by extracting significant results from the literature. Methods based on unctional series expansion, parameter estimation, block-oriented models are included. However, there is still considerable debate about the advantages and disadvantages of each approach. This is true primarily because each method has limitations on the types of assumption and interpretation, types of nonlinear elements, etc. This means that user must select an appropriate method and the selection will depend on the problem under investigation.

• Nuclear Medicine and Molecular Imaging
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• v.41 no.2
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• pp.78-84
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• 2007

Quantitative analysis of dynamic brain PET data using a tracer kinetic modeling has played important roles in the investigation of functional and molecular basis of various brain diseases. Parametric imaging of the kinetic parameters (voxel-wise representation of the estimated parameters) has several advantages over the conventional approaches using region of interest (ROI). Therefore, several strategies have been suggested to generate the parametric images with a minimal bias and variability in the parameter estimation. In this paper, we will review the several approaches for parametric imaging with linearized methods which include graphical analysis and mulilinear regression analysis.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.7
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• pp.729-739
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• 1990

This paper deals with the fault detection problem in uncertain linear/non-linear systems having both undermodelling and noise. A robust fault detection method is presented which accounts for the effects of noise, model mismatch and nonlinearities. The basic idea is to embed the unmodelled dynamics in a stochastic process and to use the nominal model with a predetermined fixed denominator. This allows the input /output relationship to be represented as a linear function of the system parameters and also facilitate the quatification of the effect of noise, model mismatch and linearization errors on parameter estimation by the Bayesian method. Comparisons are made via simulations with traditional fault detection methods which do not account for model mismatch or linearization errors. The new method suggested in this paper is shown to have a marked improvement over traditional methods on a number of simulations, which is a consequence of the fact that the new method explicitly for the effects of undermodelling and linearization errors.

• The Korean Journal of Applied Statistics
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• v.26 no.5
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• pp.697-712
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• 2013

When analyzing repeated binary data, the generalized estimating equations(GEE) approach produces consistent estimates for regression parameters even if an incorrect working correlation matrix is used. However, time-varying covariates experience larger changes in coefficients than time-invariant covariates across various working correlation structures for finite samples. In addition, the GEE approach may give biased estimates under missing at random(MAR). Weighted estimating equations and multiple imputation methods have been proposed to reduce biases in parameter estimates under MAR. This article studies if the two methods produce robust estimates across various working correlation structures for longitudinal binary data with time-varying covariates under different missing mechanisms. Through simulation, we observe that time-varying covariates have greater differences in parameter estimates across different working correlation structures than time-invariant covariates. The multiple imputation method produces more robust estimates under any working correlation structure and smaller biases compared to the other two methods.