• The Journal of the Acoustical Society of Korea
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• v.29 no.2
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• pp.97-101
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• 2010

FIR type system identification with noisy input and output data can be solved by a total least squares (TLS) estimation. However, the performance of the TLS estimation is very sensitive to the ratio between the variances of the input and output noises. In this paper, we propose an iterative convex combination algorithm between TLS and least squares (LS). This combined algorithm shows robustness against the noise variance ratio. Consequently, the practical workability of the TLS method with noisy data has been significantly broadened.

• Journal of Electrical Engineering and Technology
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• v.13 no.6
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• pp.2344-2353
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• 2018

A coupled recursive total least squares (CRTLS) algorithm is proposed for parameter estimation of permanent magnet synchronous machines (PMSMs). TLS considers the errors of both input variables and output ones, and thus achieves more accurate estimates than standard least squares method does. The proposed algorithm consists of two recursive total least squares (RTLS) algorithms for the d-axis subsystem and q-axis subsystem respectively. The incremental singular value decomposition (SVD) for the RTLS obtained by an approximate calculation with less computation. The performance of the CRTLS is demonstrated by simulation and experimental results.

• Communications for Statistical Applications and Methods
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• v.25 no.6
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• pp.673-685
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• 2018

Procrustes analysis is a useful technique useful to measure, compare shape differences and estimate a mean shape for objects; however it is based on a least squares criterion and is affected by some outliers. Therefore, we propose two generalized Procrustes analysis methods based on M-estimation and least median of squares estimation that are resistant to object outliers. In addition, two algorithms are given for practical implementation. A simulation study and some examples are used to examine and compared the performances of the algorithms with the least square method. Moreover since these resistant GPA methods are available for higher dimensions, we need some methods to visualize the objects and mean shape effectively. Also since we have concentrated on resistant fitting methods without considering shape distributions, we wish to shape analysis not be sensitive to particular model.

• International Journal of Control, Automation, and Systems
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• v.6 no.5
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• pp.713-721
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• 2008

This paper presents the robust velocity estimation of an omnidirectional mobile robot using a polygonal array of optical mice that are installed at the bottom of the mobile robot. First, the velocity kinematics from a mobile robot to an array of optical mice is derived as an overdetermined linear system. The least squares velocity estimate of a mobile robot is then obtained, which becomes the same as the simple average for a regular polygonal arrangement of optical mice. Next, several practical issues that need be addressed for the use of the least squares mobile robot velocity estimation using optical mice are investigated, which include measurement noises, partial malfunctions, and imperfect installation. Finally, experimental results with different number of optical mice and under different floor surface conditions are given to demonstrate the validity and performance of the proposed least squares mobile robot velocity estimation method.

• Communications for Statistical Applications and Methods
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• v.22 no.6
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• pp.615-624
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• 2015

We consider the estimation of seasonal cointegration in the presence of conditional heteroskedasticity (CH) using a feasible generalized least squares method. We capture cointegrating relationships and time-varying volatility for long-run and short-run dynamics in the same model. This procedure can be easily implemented using common methods such as ordinary least squares and generalized least squares. The maximum likelihood (ML) estimation method is computationally difficult and may not be feasible for larger models. The simulation results indicate that the proposed method is superior to the ML method when CH exists. In order to illustrate the proposed method, an empirical example is presented to model a seasonally cointegrated times series under CH.

• The Transactions of the Korean Institute of Power Electronics
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• v.11 no.2
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• pp.159-163
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• 2006

In order to tune the speed controller in the linear servo applications an 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) 4y using the parameter estimation method of Least-Squares algorithm. First, the deterministic autoregressive moving average(DARMA) model of the mechanical dynamic system is derived. Then 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 Data and Information Science Society
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• v.22 no.5
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• pp.999-1005
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• 2011

This paper proposes a weighted least squares support vector machine for asymmetric least squares regression. This method achieves nonlinear prediction power, while making no assumption on the underlying probability distributions. The cross validation function is introduced to choose optimal hyperparameters in the procedure. Experimental results are then presented which indicate the performance of the proposed model.

• 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.

• 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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.3
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• pp.427-432
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• 2018

This paper uses a recursive least squares method to estimate the projectile motion trajectory of an object in real time. The equations of motion of the object are obtained considering the air resistance which occurs in the actual experiment environment. Because these equations consider air resistance, parameter estimation of nonlinear terms is required. However, nonlinear recursive least squares estimation is not suitable for estimating trajectory of projectile in that it requires a lot of computation time. Therefore, parameter estimation for real-time trajectory prediction is performed by recursive least square estimation after using Taylor series expansion to approximate nonlinear terms to polynomials. The proposed method is verified through experiments by using VICON Bonita motion capture system which can get three dimensional coordinates of projectile. The results indicate that proposed method is more accurate than linear Kalman filter method based on the equations of motion of projectile that does not consider air resistance.