• 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.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.28-31
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• 1996

We propose an estimator design method of Stewart platform, which gives the 6DOF, positions and velcities of Stewart platform from the measured cylinder length. The solution of forward kinematics is not solved yet as a useful realtime application tool because of the complexity of the equation with multiple solutions. Hence we suggest an nonlinear estimator for the forward kinematics solution using Luenberger observer with nonlinear error correction term. But the way of residual gain selection of the estimator is not clear, so we suggest an algebraic Riccati equation for gain matrix using Lyapunov method. This algorithm gives the sufficient condition of the stability of error dynamics and can be extended to general nonlinear system.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.5
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• pp.429-433
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• 2007

A number of techniques have been developed for estimation of speed or position in motor drives. The accuracy of these techniques is affected by the variation of motor parameters such as the stator resistance, stator inductance or torque constant. This paper is proposed a neural network based estimator for torque and ststor resistance in IPMSM Drives. The neural weights are initially chosen randomly and a model reference algorithm adjusts those weights to give the optimum estimations. The neural network estimator is able to track the varying parameters quite accurately at different speeds with consistent performance. The neural network parameter estimator has been applied to slot and flux linkage torque ripple minimization of the IPMSM. The validity of the proposed parameter estimator is confirmed by the operating characteristics controlled by neural networks control.

• Proceedings of the KIPE Conference
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• 1999.07a
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• pp.505-508
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• 1999

Conventional adaptive speed estimators cannot avoid the influence of the non-linear inductance variation under the saturation conditions. Without speed sensors, it is difficult to identify the inductance variation using a reactive power mode because the model contains a term of the rotor speed. In this paper, we propose a novel speed estimator having hybrid architecture in order to estimate both the rotor speed and the inductance variation simultaneously when the motor flux is saturated. Proposed estimator consists of the error between the flux obtained from the stator voltage equation and the flux estimated from the rotor flux observer. Introducing a new correction term into the estimator increases the estimation ability of the conventional speed estimator even though the motor flux is saturated. The convergence of the speed estimation error is examined by simulation Furthermore, the experimental results show the validity of the proposed method.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.33A no.5
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• pp.31-43
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• 1996

In this paper, we analyze the delay characteristics of the multimdedia traffic in B-ISDN, and propose a weighted variable-size window estimator considering the QoS characteristics of the media and the variable delay characteristics of the networks, and present a delay estimator-based synchronization protocol for the efficient synchronization in ATM-based B-ISDN. The proposed estimator assigns a high weight value to recent cells arrived in the receiver, and suitably adjusts window size in order to efficiently adapt to delay variation by the prameter to detect the delay variation of the networks. The proposed synchronization protocol estimates end-to-end delay by the weighted variable-size window estimator, and dynamically schedules th evirtual channel of the transmitter to playout multimedia data on time in the receiver. Also, we evaluate the performance of the delay estimator, which is the most importnat functional element in our proposed synchronization protocol, by the simulation and analyze the results of the simulation.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.4
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• pp.228-234
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• 2000

The aim of the work is to present a new method of estimating the existence of a mass unbalance and mass unbalance under a crack on a rotating shaft. This is an advanced new method for the detection of a mass unbalance and a new way to estimate the position of it under crack influence. As the first step, the shaft is physically modelled with a finite element method and the dynamic mathematical model is derived by using the Hamilton principle; thus, the system is represented by various subsystems. The equation of motion of the shaft with a mass unbalance and a crack are established by adapting the local mass unbalance and the stiffness change. this is a reference system for the given system. Based on a model for transient behavior induced from vabrations measured at the bearings, an elementary Estimator is designed to detect mass unblance on the shaft. Using the Estimator, a bank of the Estimator is established to estimate the estimate the position of the mass unbalance and arranged at a certain location on the shaft. The informations for the given system are the measurements of bearing displacements and velocity.

• International Journal of Reliability and Applications
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• v.12 no.1
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• pp.61-77
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• 2011

One of the most important problems in the estimation of the parameter of the failure model, is the cost of experimental sampling units, which can be reduced by using any prior information available about ${\theta}$, and devising a two-stage pooling shrunken estimation procedure. We have proposed an estimator of the reliability function (R(t)) of the exponential model using two-stage time censored data when a prior value about the unknown parameter (${\theta}$) is available from the past. To compare the performance of the proposed estimator with the classical estimator, computer intensive calculations for bias, mean squared error, relative efficiency, expected sample size and percentage of the overall sample size saved expressions, were done for varying the constants involved in the proposed estimator (${\tilde{R}}$(t)).

• Journal of the Korean Statistical Society
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• v.26 no.3
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• pp.299-308
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• 1997

In the simple linear regression model Y = .alpha.$_{0}$ + .beta.$_{0}$Z + .epsilon. under the right censorship of the response variables, the estimation of the mean lifetime E(Y) is an interesting problem. In this paper we propose a method of estimating E(Y) based on the observations modified by the arguments of Buckley and James (1979). It is shown that the proposed estimator is consistent and our proposed procedure in the simple linear regression case can be naturally extended to the multiple linear regression. Finally, we perform simulation studies to compare the proposed estimator with the estimator introduced by Gill (1983).83).

• Communications for Statistical Applications and Methods
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• v.18 no.1
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• pp.111-118
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• 2011

This paper suggests a ratio-cum product estimator of a finite population mean using information on the coefficient of variation and the fcoefficient of kurtosis of auxiliary variate in stratified random sampling. Bias and MSE expressions of the suggested estimator are derived up to the first degree of approximation. The suggested estimator has been compared with the combined ratio estimator and several other estimators considered by Kadilar and Cingi (2003). In addition, an empirical study is also provided in support of theoretical findings.

• Communications for Statistical Applications and Methods
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• v.20 no.4
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• pp.311-319
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• 2013

Cooray and Ananda (2005) proposed a composite lognormal-Pareto model to analyze loss payment data in the actuarial and insurance industries. Their model is based on a lognormal density up to an unknown threshold value and a two-parameter Pareto density. In this paper, we implement the minimum density power divergence estimation for the composite lognormal-Pareto density. We compare the performances of the minimum density power divergence estimator (MDPDE) and the maximum likelihood estimator (MLE) by simulations and an example. The minimum density power divergence estimator performs reasonably well against various violations in the distribution. The minimum density power divergence estimator better fits small observations and better resists against extraordinary large observations than the maximum likelihood estimator.