• The Transactions of the Korean Institute of Power Electronics
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• v.23 no.1
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• pp.72-75
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• 2018

An initial rotor position estimation method is proposed in this study for an interior permanent-magnet synchronous motor without a resolver or an absolute encoder. This method uses least squares approximation to estimate the initial rotor position. The magnetic polarity is identified by injection of short pulses. The proposed estimation process is robust because it does not require complex signal processing that depends on the performance of a digital filter. In addition, it can be applied to various servo systems because it does not require additional hardware. Experimental results validate the effectiveness of the proposed method using a standard industrial servomotor with interior-permanent magnets.

• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.145-146
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• 2007

This paper presents the sensitivity analysis of the leasst qsuares velocity estimation of an omnidirectional mobile robot using a regular polygonal array of optical mice. First, the velocity kinematics from a mobile robot to an array of optical mice is derived as an overdetermined linear system. Then, for a given set of optical mouse readings, the least squares velocity estimation of a mobile robot is obtained as the simple average. Finally, the sensitivity analysis of the proposed least squares velocity estimation to imprecise installation is made.

• Journal of the Korean Data and Information Science Society
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• v.17 no.2
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• pp.401-409
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• 2006

We consider a generalized partly-parametric additive risk model which generalizes the partly parametric additive risk model suggested by McKeague and Sasieni (1994). As an estimation method of this model, we propose to use the weighted least square estimation, suggested by Huffer and McKeague (1991), for Aalen's additive risk model by a piecewise constant risk. We provide an illustrative example as well as a simulation study that compares the performance of our method with the ordinary least squares method.

• The Korean Journal of Applied Statistics
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• v.6 no.1
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• pp.79-93
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• 1993

We consider adaptive L-estimation of estimating slope parameter in regression model. The proposed estimator is simple extension of trimmed least squares estimator proposed by ruppert and carroll. The efficiency of the proposed estimator is especially well compared with usual least squares estimator, least absolute value estimator, and M-estimators designed for asymmetric distributions under asymmetric error distributions.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.36 no.4
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• pp.64-70
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• 2013

Estimation approaches for casual relation model with high-order factors have strict restrictions or limits. In the case of ML (Maximum Likelihood), a strong assumption which data must show a normal distribution is required and factors of exponentiation is impossible due to the uncertainty of factors. To overcome this limitation many PLS (Partial Least Squares) approaches are introduced to estimate the structural equation model including high-order factors. However, it is possible to yield biased estimates if there are some differences in the number of measurement variables connected to each latent variable. In addition, any approach does not exist to deal with general cases not having any measurement variable of high-order factors. This study compare several approaches including the repeated measures approach which are used to estimate the casual relation model including high-order factors by using PLS (Partial Least Squares), and suggest the best estimation approach. In other words, the study proposes the best approach through the research on the existing studies related to the casual relation model including high-order factors by using PLS and approach comparison using a virtual model.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.47-51
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• 2005

Using satellite images, an optimal solution to water motion has been presented in this study. Since temperature patterns are suitable tracers in water motion, Sea Surface Temperature (SST) images of Caspian Sea taken by MODIS sensor on board Terra satellite have been used in this study. Two daily SST images with 24 hours time interval are used as input data. Computation of templates correspondence between pairs of images is crucial within motion algorithms using non-rigid body objects. Image matching methods have been applied to estimate water body motion within the two SST images. The least squares matching technique, as a flexible technique for most data matching problems, offers an optimal spatial solution for the motion estimation. The algorithm allows for simultaneous local radiometric correction and local geometrical image orientation estimation. Actually, the correspondence between the two image templates is modeled both geometrically and radiometrically. Geometric component of the model includes six geometric transformation parameters and radiometric component of the model includes two radiometric transformation parameters. Using the algorithm, the parameters are automatically corrected, optimized and assessed iteratively by the least squares algorithm. The method used in this study, has presented more efficient and robust solution compared to the traditional motion estimation schemes.

• The Journal of the Acoustical Society of Korea
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• v.38 no.3
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• pp.283-289
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• 2019

RLS (Recursive-least-squares) algorithm is known to have good convergence and excellent error level after convergence. However, there is a disadvantage that numerical instability is included in the algorithm due to inverse matrix calculation. In this paper, we propose an algorithm with no matrix inversion to avoid the instability aforementioned. The proposed algorithm still keeps the same convergence performance. In the proposed algorithm, we adopt an averaged gradient-based step size as a self-adjusted step size. In addition, a variable forgetting factor is introduced to provide superior performance for time-varying channel estimation. Through simulations, we compare performance with conventional RLS and show its equivalency. It also shows the merit of the variable forgetting factor in time-varying channels.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.927-931
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• 2003

In this paper, a new Recursive Least-Squares(RLS) algorithm based on matrix pseudo-inverses is presented. The aim is to use the proposed new RLS algorithm for not only the over-determined but also the under-determined estimation problem. Compared with previous results, e.g., Jie Zhou et al., the derivation of the proposed recursive form is much easier, and the recursion form is also much simpler. Furthermore, it is shown by simulations that the proposed RLS algorithm is more efficient and numerically stable than the existing algorithms.

• Smart Structures and Systems
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• v.4 no.3
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• pp.279-290
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• 2008

The modeling and parameter estimation of a damped one-degree-of-freedom mass-spring system is examined. This paper presents a method for estimating the system parameters (damping coefficients and natural frequency) from measured free-vibration motion of a system that is modeled to include both subcritical viscous damping and kinetic Coulomb friction. The method applies a commercially available least-squares curve-fitting software function to fit the known solution of the equations of motion to the measured response. The method was tested through numerical simulation, and it was applied to experimental data collected from a laboratory mass-spring apparatus. The mass of this apparatus translates on linear bearings, which are the primary source of light inherent damping. Results indicate that the curve-fitting method is effective and accurate for both perfect and noisy measurements from a lightly damped mass-spring system.

• Communications for Statistical Applications and Methods
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• v.19 no.3
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• pp.423-432
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• 2012

This paper is concerned with the detection of multiple change-points in linear regression models. The proposed procedure relies on the local estimation for global change-point estimation. We propose a multiple change-point estimator based on the local least squares estimators for the regression coefficients and the split measure when the number of change-points is unknown. Its statistical properties are shown and its performance is assessed by simulations and real data applications.