• Journal of the Korean Society for Precision Engineering
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• v.16 no.7
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• pp.178-187
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• 1999

In order to identify a transfer function model with noise, penalty function method has been widely used. In this method, estimation process for possible model parameters from low to higher order proceeds the model identification process. In this study, based on linear estimation method, a new approach unifying the estimation and the identification of ARMAX model is proposed. For the parameter estimation of a transfer function model with noise, linear estimation method by noise separation is suggested instead of nonlinear estimation method. The feasibility of the proposed model identification and estimation method is verified through simulations, namely by applying the method to time series model. In the case of time series model with noise, the proposed method successfully identifies the transfer function model with noise without going through model parameter identification process in advance. A new algorithm effectively achieving model identification and parameter estimation in unified frame has been proposed. This approach is different from the conventional method used for identification of ARMAX model which needs separate parameter estimation and model identification processes. The consistency and the accuracy of the proposed method has been verified through simulations.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.503-506
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• 2003

In this paper, we present the results of parameter estimation for do-dc converter model by system identification. The parameter estimation for dc-dc converter aims at the diagnosis of its operating status. we applied the system identification method for parameter estimation. For verification of estimated parameter, we compare bode plot of estimated system transfer function and measurement results of HP4194 instrument.

• Clean Technology
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• v.7 no.2
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• pp.119-126
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• 2001

The prediction of ozone formation was studied using the neural network and the stochastic method. Parameter estimation method and artificial neural network(ANN) method were employed in the stochastic scheme. In the parameter estimation method, extended least squares(ELS) method and recursive maximum likelihood(RML) were used to achieve the real time parameter estimation. Autoregressive moving average model with external input(ARMAX) was used as the ozone formation model for the parameter estimation method. ANN with 3 layers was also tested to predict the ozone formation. To demonstrate the performance of the ozone formation prediction schemes used in this work, the prediction results of ozone formation were compared with the real data. From the comparison it was found that the prediction schemes based on the parameter estimation method and ANN method show an acceptable accuracy with limited prediction horizon.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.1091-1094
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• 2005

This paper formulates the parameter estimation of cornering behavior of a vehicle. Especially some vehicle parameter is very important on stability control of chassis by ECU, but some parameter is so hard to get by sensor which parameter is included the nonlinear characteristic of tire cornering force. So we need to deduce that parameter from used signal and numerical method. In this study, we propose a estimation method and present the simulation by parameter estimation technique.

• Steel and Composite Structures
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• v.20 no.5
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• pp.1119-1131
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• 2016

As a first attempt, an inverse hybrid numerical method for small scale parameter estimation of functionally graded (FG) nanobeams using measured frequencies is presented. The governing equations are obtained with the Eringen's nonlocal elasticity assumptions and the first-order shear deformation theory (FSDT). The equations are discretized by using the differential quadrature method (DQM). The discretized equations are transferred from temporal domain to frequency domain and frequencies of the nanobeam are obtained. By applying random error to these frequencies, measured frequencies are generated. The measured frequencies are considered as input data and inversely, the small scale parameter of the beam is obtained by minimizing a defined functional. The functional is defined as root mean square error between the measured frequencies and calculated frequencies by the DQM. Then, the conjugate gradient (CG) optimization method is employed to minimize the functional and the small scale parameter is obtained. Efficiency, convergence and accuracy of the presented hybrid method for small scale parameter estimation of the beams for different applied random error, boundary conditions, length-to-thickness ratio and volume fraction coefficients are demonstrated.

• Communications for Statistical Applications and Methods
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• v.16 no.3
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• pp.463-477
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• 2009

Parameter estimation methods such as maximum likelihood estimation method, probability weighted moments method, regression method have been popularly applied to various extreme value models in numerous literature. Among three methods above, the performance of regression method has not been rigorously investigated yet. In this paper the regression method is compared with the other methods via Monte Carlo simulation studies for estimation of parameters of the Generalized Extreme Value(GEV) distribution and the Generalized Pareto(GP) distribution. Our simulation results indicate that the regression method tends to outperform other methods under small samples by providing smaller biases and root mean square errors for estimation of location parameter of the GEV model. For the scale parameter estimation of the GP model under small samples, the regression method tends to report smaller biases than the other methods. The regression method tends to be superior to other methods for the shape parameter estimation of the GEV model and GP model when the shape parameter is -0.4 under small and moderately large samples.

• Transactions of the Korean Society of Mechanical Engineers
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• v.8 no.1
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• pp.65-70
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• 1984

A recursive parameter estimation method is applied to spindle deflection model during boring process. The spindle infeed rate is then determined to preserve the diametral tolerance of bore. This estimation method is further extended to adaptive control by application of the variance perturbation method. The results of computer simulation attest that the proposed method renders the optimal cutting conditions, maintaining the diametral accuracy of bore, regardless of parameter fluctuations. The proposed method necessitating only post-process measurements features that initialization of parameter guess values in simple, a priori knowledge on parameter variations is not needed and the accurate estimation of optimal spindle infeed rate is obtained, even if the parameter estimation may be poor.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.32 no.3
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• pp.99-109
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• 2009

Negative binomial yield model for semiconductor manufacturing consists of two parameters which are the average number of defects per die and the clustering parameter. Estimating the clustering parameter is quite complex because the parameter has not clear closed form. In this paper, a Bayesian approach using Markov Chain Monte Carlo is proposed to estimate the clustering parameter. To find an appropriate estimation method for the clustering parameter, two typical estimators, the method of moments estimator and the maximum likelihood estimator, and the proposed Bayesian estimator are compared with respect to the mean absolute deviation between the real yield and the estimated yield. Experimental results show that both the proposed Bayesian estimator and the maximum likelihood estimator have excellent performance and the choice of method depends on the purpose of use.

• The Journal of the Acoustical Society of Korea
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• v.15 no.1E
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• pp.65-69
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• 1996

This paper presents an algorithm for multipath channel parameter estimation by an improved Prony method. This algorithm applies a modified regularized spectral estimation to the conventional SVD Prony method. This method requires no a priori information on the number of multipath. The performance of the proposed algorithm is almost the same as that of the SVD based multipath channel parameter estimation algorithm.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.9 no.2
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• pp.112-118
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• 2000

This paper deals with the application of sensitivity method to the parameter estimation for the dynamic analysis of gener-al mechanical system. In this procedure we take the derivatives of the given system with respect to a certain parameter and use this information to implement the steepest descent method. This paper will give two examples of this technique applied to simple vehicle models. This paper will give two examples of this technique applied to simple vehicle models. Simulation results show excellent convergence and accuracy of parameter estimates.