• The Journal of the Acoustical Society of Korea
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• v.25 no.2E
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• pp.43-48
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• 2006

Abstract-Using the recursive generalized eigendecomposition method, we develop a recursive form solution to the data least squares (DLS) problem, in which the error is assumed to lie in the data matrix only. Simulations demonstrate that DLS outperforms ordinary least square for certain types of deconvolution problems.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.5
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• pp.866-874
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• 2008

Motor frequency can be measured by a hall sensor. Among the many hall sensors, a linear type hall sensor is good at high accuracy frequency measuring problem. However, in general, this linear type hall sensor has DC offset which can vary along sensor's operating voltage change. Therefore, In motor frequency measurement problem using the linear hall sensor, it needs an estimator that can estimate frequency and DC offset simultaneously. In this paper, we propose the least mean square estimator to estimate motor frequency. To verify its performance, we compare the LMS estimator with a commercial analog tachometer. Experimental results shows the proposed LMS estimator works well in varying frequency and stationary DC offset.

• Journal of Information Processing Systems
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• v.15 no.4
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• pp.748-764
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• 2019

In order to deal with the filtering delay problem of least mean square adaptive filter noise reduction algorithm and music noise problem of spectral subtraction algorithm during the speech signal processing, we combine these two algorithms and propose one novel noise reduction method, showing a strong performance on par or even better than state of the art methods. We first use the least mean square algorithm to reduce the average intensity of noise, and then add spectral subtraction algorithm to reduce remaining noise again. Experiments prove that using the spectral subtraction again after the least mean square adaptive filter algorithm overcomes shortcomings which come from the former two algorithms. Also the novel method increases the signal-to-noise ratio of original speech data and improves the final noise reduction performance.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.7
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• pp.350-353
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• 2001

In general, the Least Square Error method is used for signal classification to measure distance in the $l^2$ metric or the $L^2$ metric space. A defect of the Least Square Error method is that it does not classify properly some waveforms, which is due to the property of the Least Square Error method: the global analysis. This paper proposes a new linear operator, the Integra-Normalizer, that removes the problem. The Integra-Normalizer possesses excellent property that measures the degree of relative similarity between signals by expanding the functional space with removing the restriction on the functional space inherited by the Least Square Error method. The Integra-Normalizer shows superiority to the Least Square Error method in measuring the relative similarity among one dimensional waveforms.

• 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 KSME Conference
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• 2008.11b
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• pp.2401-2405
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• 2008

In the present study, a three-dimensional least square/level set based two-phase flow code was developed for the simulation of three-dimensional sloshing problems using finite element discretization. The present method can be utilized for the analysis of a free surface flow problem in a complex geometry due to the feature of FEM. Since the finite element method is employed for the spatial discretization of governing equations, an unstructured mesh can be naturally adopted for the level set simulation of a free surface flow without an additional load for the code development except that solution methods of the hyperbolic type redistancing and advection equations of the level set function should be devised in order to give a bounded solution on the unstructured mesh. From the numerical experiments of the present study, it is shown that the proposed method is both robust and accurate for the simulation of three-dimensional sloshing problems.

• Journal of applied mathematics & informatics
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• v.6 no.3
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• pp.757-770
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• 1999

We analyse in this paper the possibility of using preconditioning techniques as for square non-singular systems, also in the case of inconsistent least-squares problems. We find conditions in which the minimal norm solution of the preconditioned least-wquares problem equals that of the original prblem. We also find conditions such that thd Kaczmarz-Extendid algorithm with relaxation parameters (analysed by the author in [4]), cna be adapted to the preconditioned least-squares problem. In the last section of the paper we present numerical experiments, with two variants of preconditioning, applied to an inconsistent linear least-squares model probelm.

• 한국전산유체공학회:학술대회논문집
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• 2008.03b
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• pp.565-567
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• 2008

In the present study, a least square/level set based two-phase flow code has been developed using finite element discretization, which can be utilized for the analysis of a free surface flow problem in a complex geometry. Since the finite element method is employed for the spatial discretization of governing equations, an unstructured mesh can be naturally adopted for the level set simulation of a bubble-in-liquid flow without an additional load for the code development except that solution methods of the hyperbolic type redistancing and advection equations of the level set function should be devised in order to give a bounded solution on the unstructured mesh. For the discretization of hyperbolic type redistancing and advection equations, least square method is adopted. From the numerical experiments of the present study, it is shown that the proposed method is both robust and accurate.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.21 no.4
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• pp.427-432
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• 2010

To reduce the outage probability and to increase the transmission capacity, the importance of repeaters in cellular systems is increasing. But a RF(Radio Frequency) repeater has a problem that the output of the transmit antenna is partially feedback to the receive antenna, which is feedback interference. In this paper, we proposed adaptive Sign-Sign LMS(Least Mean Square) algorithm using correlations for the performance enhancement of RF repeater. The weight vector is updated by using sign of input signal and error signal to the least squared error of the conventional algorithms. When compared with the conventional method, the proposed canceller achieves the maximum 10 dB performance gain in terms of the MSE(Mean Square Error).

• Journal of the Korean Data and Information Science Society
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• v.22 no.3
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• pp.537-547
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• 2011

In this article, we propose an estimation procedure for the treatment eect for the right censored data. We apply the least square method for deriving the estimation equation and obtain an explicit formula for an estimation. Then we consider some asymptotic properties with derivation of the asymptotic normality for the estimate. Finally we illustrate our procedure with an example and discuss some interesting aspects for the estimation procedure.