• Journal of Electrical Engineering and Technology
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• v.5 no.4
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• pp.538-544
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• 2010

This paper proposes an estimation algorithm for the separate primary and secondary leakage inductances of a three phase $Y-\Delta$ transformer using least squares method. The voltage equations from the primary and secondary windings are combined into a differential equation to estimate the separate primary and secondary leakage inductances in order to use the line current of the delta winding. Separate primary and secondary leakage inductances are obtained by applying least squares method to the differential equation. The performance of the proposed algorithm is validated under transient states, such as magnetic inrush and overexcitation, as well as in the steady state with various cut-off frequencies of low-pass filter. The proposed technique can accurately generate separate leakage inductances both in the steady and transient states.

• Journal of the Korean Data and Information Science Society
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• v.25 no.5
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• pp.1151-1160
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• 2014

In DNA microarray studies, the number of genes far exceeds the number of samples and the gene expression measures are highly correlated. Partial least squares regression (PLSR) is one of the popular methods for dimensional reduction and known to be useful for the classifications of microarray data by several studies. In this study, we suggest a modified version of the partial least squares regression to analyze gene expression data with survival information. The method is designed as a new gene selection method using PLSR with an iterative procedure of imputing censored survival time. Mean square error of prediction criterion is used to determine the dimension of the model. To visualize the data, plot for variables superimposed with samples are used. The method is applied to two microarray data sets, both containing survival time. The results show that the proposed method works well for interpreting gene expression microarray data.

• Journal of Electrical Engineering and Technology
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• v.7 no.4
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• pp.606-614
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• 2012

A practical recursive linear robust estimation scheme is proposed for target localization in the sensor network which provides range difference of arrival (RDOA) measurements. In order to radically solve the known practical difficulties such as sensitivity for initial guess and heavy computational burden caused by intrinsic nonlinearity of the RDOA based target localization problem, an uncertain linear measurement model is newly derived. In the suggested problem setting, the target localization performance of the conventional linear estimation schemes might be severely degraded under the low SNR condition and be affected by the target position in the sensor network. This motivates us to devise a new sensor network localization algorithm within the framework of the recently developed robust least squares estimation theory. Provided that the statistical information regarding RDOA measurements are available, the estimate of the proposition method shows the convergence in probability to the true target position. Through the computer simulations, the omnidirectional target localization performance and consistency of the proposed algorithm are compared to those of the existing ones. It is shown that the proposed method is more reliable than the total least squares method and the linear correction least squares method.

• International Journal of Aeronautical and Space Sciences
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• v.14 no.1
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• pp.85-90
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• 2013

This paper presents a study on terrain referenced navigation (TRN). The extended Kalman filter (EKF) is adopted as a filter method. A Jacobian matrix of measurement equations in the EKF consists of terrain slope terms, and accurate slope estimation is essential to keep filter stability. Two slope estimation methods are proposed in this study. Both methods are based on the least-squares approach. One is planar regression searching the best plane, in the least-squares sense, representing the terrain map over the region, determined by position error covariance. It is shown that the method could provide a more accurate solution than the previously developed linear regression approach, which uses lines rather than a plane in the least-squares measure. The other proposed method is weighted planar regression. Additional weights formed by Gaussian pdf are multiplied in the planar regression, to reflect the actual pdf of the position estimate of EKF. Monte Carlo simulations are conducted, to compare the performance between the previous and two proposed methods, by analyzing the filter properties of divergence probability and convergence speed. It is expected that one of the slope estimation methods could be implemented, after determining which of the filter properties is more significant at each mission.

• Communications for Statistical Applications and Methods
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• v.8 no.3
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• pp.633-641
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• 2001

The least squares method is usually applied when estimating the parameters in the regression models. However the least square estimator is not very efficient when the distribution of the error is skewed. In this paper, we propose the asymmetric least square estimator for a particular nonlinear time series regression model, and give the simple and practical sufficient conditions for the strong consistency of the estimators.

• The Journal of the Acoustical Society of Korea
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• v.26 no.2E
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• pp.63-68
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• 2007

Using the neural network model for oriented principal component analysis (OPCA), we propose a solution to the data least squares (DLS) problem, in which the error is assumed to lie in the data matrix only. In this paper, we applied this neural network model to channel equalization. Simulations show that the neural network based DLS outperforms ordinary least squares in channel equalization problems.

• Proceedings of the KIEE Conference
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• 1991.07a
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• pp.691-694
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• 1991

In a discrete parameter estimation system, the standard least squares method shows slow convergence. On the other hand, the weighted least squares method has relatively fast convergence. However, if the input is not sufficiently rich, then gain matrix grows unboundedly. In order to solve these problems, this paper proposes a modified least squares algorithm which prevents gain matrix from growing unboundedly and has fast convergence.

• Proceedings of the KIPE Conference
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• 2005.07a
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• pp.427-429
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• 2005

In order to tune the speed controller in the linear servo applications the accurate information of a mover mass including a load mass is always required. This paper suggests the mass estimation method of a permanent magnet linear synchronous motor(PMLSM) by using the parameter estimation method of Least-Squares algorithm. First, the deterministic autoregressive moving average(DARMA) model of the mechanical dynamic system is derived. The application of the Least-Squares algorithm shows that the mass can be accurately estimated both in the simulation results and in the experimental results.

• Journal of the Korean Statistical Society
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• v.33 no.1
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• pp.25-34
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• 2004

In this paper we propose a prediction method on the regression model with randomly censored observations of the training data set. The least squares support vector machine regression is applied for the regression function prediction by incorporating the weights assessed upon each observation in the optimization problem. Numerical examples are given to show the performance of the proposed prediction method.

• Communications for Statistical Applications and Methods
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• v.12 no.2
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• pp.539-544
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• 2005

In this paper, we propose the one-step least squares method based on the squared differences to estimate the parameters of the variogram used for spatial data modelling, and discuss its asymptotic efficiency. The proposed method does not require to specify lags of interest and partition lags, so that we can delete the subjectiveness and ambiguity originated from the lag selection in estimating spatial dependence.