• Journal of the Korean Society of Safety
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• v.9 no.4
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• pp.42-48
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• 1994

A rotor flux error-based approach for correcting the rotor time constant estimation used in the slip frequency calculator of indirect field oriented controller is presented in this paper. The controller was derived from the d-q induction machine model. Slip frequency gain is dependent on the machine parameter errors. And parameter errors result in rotor flux error. Thus, estimated rotor flux is compared to commanded rotor flux. The error between them is used for the estimation of rotor time constant. Simulation results which demonstrate the performance of this approach are presented.

• Journal of Applied Reliability
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• v.3 no.2
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• pp.127-136
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• 2003

This paper investigates the effect of individual observations in regression models with MA(1) errors through the 'hat matrix' It shows that the first observation has the largest hat matrix diagonal component for $\theta$＜0 in the regression model with an intercept. This provides additional evidence for retaining the first observation in performing estimation in this setting. When the regression model goes to the origin and the independent variable has a deterministic trend, the last observation has the greatest leverage for │$\theta$│＜1 and may have potentially large impact on parameter estimation.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.6 no.3
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• pp.148-151
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• 2013

A new direction of arrival estimation method using effects of model errors and sensitivity analysis is proposed. Since a desired signal is obtained after interference rejection through correction effects of model error, the effect of channel interference on the estimation is significantly reduced. Through simulation, we show that the proposed method offers significantly improved estimation resolution and accuracy relative to existing method.

• Journal of the Ergonomics Society of Korea
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• v.22 no.4
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• pp.59-78
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• 2003

Most regression models of anthropometric variables use stature and/or weight as regressors; however, these 'flat' regression models result in large errors for anthropometric variables having low correlations with the regressors. To develop more accurate regression models for anthropometric variables, this study proposed a method to estimate anthropometric variables in a hierarchical manner based on the relationships among the variables and a process to develop and improve corresponding regression models. By applying the proposed approach, a hierarchical estimation structure was constructed for 59 anthropometric variables selected for the occupant package design of a passenger car and corresponding regression models were developed with the 1988 US Army anthropometric survey data. The hierarchical regression models were compared with the corresponding flat regression models in terms of accuracy. As results, the standard errors of the hierarchical regression models decreased by 28% (4.3mm) on average compared with those of the flat models.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.69.1-69
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• 2001

In this paper, observability properties of a multiantenna GPS measurement system for the estimation of errors in INS are presented. It is shown that time-invariant INS error models are observable with measurements from at least three GPS antennas on the vehicle. There is at least one unobservable mode with two antennas. There are three unobservable modes with one antenna. It is also shown that time-varying INS error models are instantaneously observable with measurements from three GPS antennas. A numerical simulation results are given to verify the effectiveness of the multiantenna measurement system on the INS error estimation. In the simulation, a GPS measurement system is considered in which a trade-off between computational load and accuracy of estimation is achieved.

• Korean Management Science Review
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• v.19 no.1
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• pp.117-128
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• 2002

In this Paper we investigate the performance of the five efficiency estimation methods which include the stochastic frontier model estimated by maximum likelihood (SFML), the stochastic frontier model estimated by corrected ordinary least squares (SFCOLS), the data envelopment analysis (DIA) model, the combined estimation of SFML and DEA (SFML + DEA), and the combined estimation of SFCOLS arid DIA (SFCOLS+ DEA) using Monte Carlo analysis. The results include: 1) SFML provides most accurate efficiency estimates for the sample sloe 150 or over,2) SFML+DEAor SFCOLS + DIA Perform better for the cases with sample sloe 25, 50, and low random errors, 3) SFCOLS performs better for the close with sample sloe 25, 50, and very high random errors.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.4
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• pp.497-502
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• 2010

The accurate estimation of position and direction of the mobile robot is essential for preparing precise movement and works in the inner complex environment. In this paper, we propose a robust estimation method of location and direction using the velocity of mobile robot in the inner GPS environment. The estimation using the inner GPS with ultrasonic sensors have to consider with various acoustic noise and sensor errors. We design a robust estimation method using a membership function based on uncertainty of the obtained information and robot velocity. The simulation results of the proposed method show effectiveness in the contaminated environment with position errors.

• Journal of the Korean Data and Information Science Society
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• v.16 no.4
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• pp.1079-1086
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• 2005

Most discussions of single imputation methods and the EM algorithm concern point estimation of population quantities with missing values. A second concern is how to get standard errors of the point estimates obtained from the filled-in data by single imputation methods and EM algorithm. Now we focus on how to estimate standard errors with incorporating the additional uncertainty due to nonresponse. There are some approaches to account for the additional uncertainty. The general two possible approaches are considered. One is the jackknife method of resampling methods. The other is multiple imputation(MI). These two approaches are reviewed and compared through simulation studies.

• Journal of the Korean Data and Information Science Society
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• v.25 no.2
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• pp.439-446
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• 2014

Quantile regression models with errors in variables have received a great deal of attention in the social and natural sciences. Some eorts have been devoted to develop eective estimation methods for such quantile regression models. In this paper we propose an orthogonal distance quantile regression model that eectively considers the errors on both input and response variables. The performance of the proposed method is evaluated through simulation studies.

• Journal of the Korean Statistical Society
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• v.36 no.4
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• pp.523-542
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• 2007

In this paper we consider semiparametric random effect panel models that contain AR(p) disturbances. We derive the efficient score function and the information bound for estimating the slope parameters. We make minimal assumptions on the distribution of the random errors, effects, and the regressors, and provide semiparametric efficient estimates of the slope parameters. The present paper extends the previous work of Park et al.(2003) where AR(1) errors were considered.