• Proceedings of the KIPE Conference
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• 2010.07a
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• pp.51-52
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• 2010

In order to simplify the offline identification of induction motor parameters, a method based on optimization using a Tabu Search algorithm is proposed. The Tabu Search algorithm is used to minimize the error between the actual data and an estimated model. The robustness of the method is shown by identifying parameters of the induction motor in three different cases. The simulation results show that the method successfully estimates the motor parameters.

• 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.

• Journal of Satellite, Information and Communications
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• v.8 no.4
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• pp.37-42
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• 2013

In this paper, we propose a compensation method of a non-ideal antenna array and a computationally efficient estimation method of the direction of arrival (DOA) for the antenna array. For DOA estimation, an antenna array is essential. By using the phase difference between the output signals of antennas, we can derive the DOA. In practice, however, mutual coupling between the elements of an antenna array change the beam pattern of each element and degrade the performance of DOA estimation. In the proposed method, we first estimate the DOA for the mid-subarray of the array, where all elements undergo relatively same coupling effect. We use the estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm to estimate the DOA. Then, we expand the array based on the estimated DOA by compensating the coupling effect. Simulation results show that the proposed method is effective when jamming to noise power ratio (JNR)is relative low.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.321-325
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• 2009

Sub-pel level motion estimation contributes to significant increase in R-D performance for H.264|MPEG 4 Part 10 AVC. However, several supplements, such as interpolation, block matching, and Hadamard transform which entails large computational complexity of encoding process, are essential to find best matching block in sub-pel level motion estimation and compensation. In this paper, a fast motion estimation scheme in sub-pel accuracy is proposed based on a parabolic model of SAD to avoid such computational complexity. In the proposed scheme, motion estimation (ME) is only performed in integer-pel levels and the following sub-pel level motion vectors are found from the parametric SAD model for which the model parameters are estimated from the SAD values obtained in the integer-pel levels. Fall-back check is performed to ensure the validity of the parabolic SAD model with the estimated parameters. The experiment result shows that the proposed scheme can reduce the motion estimation time up to about 30% of the total ME times in average with negligible amount of PSNR drops (0.14dB in maximum) and bit increments (2.54%in maximum).

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.47 no.10
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• pp.747-752
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• 2019

By using the failure information and the cumulative test execution time obtained by performing the reliability growth test, it is possible to estimate the parameter of the reliability growth model, and the Mean Time Between Failure (MTBF) of the product can be predicted through the parameter estimation. However the failure information could be acquired periodically or the number of sample data of the obtained failure information could be small. Because there are various constraints such as the cost and time of test or the characteristics of the product. This may cause the error of the parameter estimation of the reliability growth model to increase. In this study, the Bayesian method is applied to estimating the parameters of the reliability growth model when the number of sample data for the fault information is small. Simulation results show that the estimation accuracy of Bayesian method is more accurate than that of Maximum Likelihood Estimation (MLE) respectively in estimation the parameters of the reliability growth model.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.1
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• pp.34-41
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• 2003

As a channel of a DS-CDMA system is shared among several users, the receivers face the problem of MAI. Also the bandlimited channel leads to ISI. Both components are undesired, but unlike the additive noise process, which is usually completely unpredictable, their space-time structure helps to estimate and remove them. This paper investigates a DS-CDMA system with a fading multipath channel. The investigations have been separated into a channel estimation part and a reception part. In the estimation part of seperated two parts, the multipath parameters such as DOA and TOA are evaluated in this paper. In the part of receiver, we used these parameters and tested the performance of this receiver about space-time beamformers(Decorrelating, Match-Filter, Wiener-Hopf, Subspace-Based). To assess many different estimation techniques and beamformers, the simulation compared with theoretical values is performed.

• The Journal of Korea Robotics Society
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• v.3 no.3
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• pp.176-185
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• 2008

In this paper, new filtering method for sensor registration is provided to estimate and correct error of registration parameters in multiple sensor environments. Sensor registration is based on filtering method to estimate registration parameters in multiple sensor environments. Accuracy of sensor registration can increase performance of data fusion method selected. Due to various error sources, the sensor registration has registration errors recognized as multiple objects even though multiple sensors are tracking one object. In order to estimate the error parameter, new nonlinear information filtering method is developed using minimum mean square error estimation. Instead of linearization of nonlinear function like an extended Kalman filter, information estimation through unscented prediction is used. The proposed method enables to reduce estimation error without a computation of the Jacobian matrix in case that measurement dimension is large. A computer simulation is carried out to evaluate the proposed filtering method with an extended Kalman filter.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.9
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• pp.1480-1490
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• 1997

A method by which the system parameter matrices can be estimated from measured time data of excitation force and acceleration has been studied. The acceleration data are integrated numerically to obtain the velocities and displacements, and the systm parameters are estimated from these data by solving equations of motion. The characteristics of the method have been investigated through its application to simulated data of 1 DOF and 2 DOF systems and experimental data measured from a simple structure. It was found that the method is very sensitive to measurement noise and the accuracy of the estimated parameters can be improved by averaging the repeatedly measured data and removing the noise. One of the main advantages of the parameter estimation method is that no a priori information about the system under test is required. The method can be easily extended to non-linear parameter estimation.

• The Korean Journal of Applied Statistics
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• v.19 no.2
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• pp.349-360
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• 2006

PROC MIXED in SAS can be utilized to make inferences on parameters in a mixed model by use of Restricted Maximum Likelihood Estimation Method or Maximum Likelihood Estimation Method which has more merits than ANOVA method. A regression model with unbalanced nested error structure that belongs to a mixed model is used to construct confidence intervals on variances among groups, within groups, and regression coefficients in the model. PROC MIXED is applied to three different sample sizes for simulation. As a result of the simulation study, PROC MIXED generates confidence intervals on parameters that maintain the stated confidence coefficient in a large sample size. However, it does not generate confidence intervals that maintain the stated confidence coefficient for variance components among groups and intercept in a small sample size.

• Communications for Statistical Applications and Methods
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• v.17 no.4
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• pp.599-609
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• 2010

Many of financial data are explained via diffusion models in modern financial research. Various types of estimation methods of diffusion processes were suggested by many authors. In this paper, we tested the properties of the NLL estimation method, suggested by Shoji and Ozaki (1998), of diffusion processes in the view of the bias and variance of the estimators and applied the method to estimate the model parameters for the U.S. fedral funds rate data and Korean inter-bank exchange rate data. By simulation study we showed that the NLL method provides relatively good estimators, in the meaning that the estimator has less bias than the Euler method, while keeping the variance similar level. We also provide the NLL estimates of U.S fedral funds rate data and Korean inter-bank exchange rate data.