• The Journal of the Acoustical Society of Korea
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• 제18권4E호
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• pp.37-42
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• 1999

This paper concerns a modified perturbation method and a least square approach to synthesize an optimum beam pattern of a thinned sensor array with respect to element spacing. In the modified perturbation, the antenna spacing is perturbed iteratively such that the sidelobes are equalized via a linear programming approach. The least square approach is proposed to improve the array performance for the thinned array using the fact that the number of sidelobes is more than the number of element spacings. It is demonstrated that the least square approach performs better than the modified perturbation method.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제24권1호
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• pp.1-22
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• 2020

An accurate and efficient gradient estimation method on unstructured grid is presented by proposing a switching process between two Least-Square methods. Diverse test cases show that the gradient estimation by Least-Square methods exhibit better characteristics compared to Green-Gauss approach. Based on the investigation, switching between the two Least-Square methods, whose merit complements each other, is pursued. The condition number of the Least-Square matrix is adopted as the switching criterion, because it shows clear correlation with the gradient error, and it can be easily calculated from the geometric information of the grid. To illustrate switching process on general grid, condition number is analyzed using stencil vectors and trigonometric relations. Then, the threshold of switching criterion is established. Finally, the capability of Switching Weighted Least-Square method is demonstrated through various two- and three-dimensional applications.

• 대한전기학회논문지:시스템및제어부문D
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• 제50권7호
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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.

• Journal of the Korean Data and Information Science Society
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• 제9권2호
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• pp.219-225
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• 1998

The parameters in linear models with censored normal responses are usually estimated by the iterative maximum likelihood and least square methods. However, the iterative least square method is simple but hardly has theoretical justification, and the iterative maximum likelihood estimating equations are complicatedly derived. In this paper, we justify these methods via Wedderburn (1974)'s quasi-likelihood approach. This provides an explicit justification for the iterative least square method and also directly the iterative maximum likelihood method for estimating the regression coefficients.

• 소음진동
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• 제6권5호
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• pp.679-684
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• 1996

The Instrumental Variable Method(IVM), modified from least square algorithm, is applied to parameter identification of a noise contaminated bearing test rig. The signal to noise ratio included in Frequency Response Function(FRF) can cause significant errors in parameter identification. Therefore, among several candidates of parameter identification method, results of the applied IVM were compared with noise-contaminated least square method. This study shows that the noise-contaminated least square method can have indonsistent accuracy depending on the degree of noise level, while the IVM has robuster performance to signal to noise ratio than least square method.

• International Journal of Automotive Technology
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• 제8권1호
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• pp.93-102
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• 2007

The frontal crash optimization of an engine room member using the response surface method was studied. The engine room member is composed of the front side member and the sub-frame. The thicknesses of the panels on the front side member and the sub-frame were selected as the design variables. The purpose of the optimization was to reduce the weight of the structure, under the constraint that the objective quantity of crash energy is absorbed. The response surface method was used to approximate the crash behavior in mathematical form for optimization procedure. To research the effect of the regression method, two different methodologies were used in constructing the response surface model, the least square method and the moving least square method. The optimum with the two methods was verified by the simulation result. The precision of the surrogate model affected the optimal design. The moving least square method showed better approximation than the least square method. In addition to the deterministic optimization, the reliability-based design optimization using the response surface method was executed to examine the effect of uncertainties in design variables. The requirement for reliability made the optimal structure be heavier than the result of the deterministic optimization. Compared with the deterministic optimum, the optimal design using the reliability-based design optimization showed higher crash energy absorption and little probability of failure in achieving the objective.

• 한국음향학회:학술대회논문집
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• 한국음향학회 1999년도 학술발표대회 논문집 제18권 2호
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• pp.165-168
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• 1999

This paper concerns a least square approach to optimizing a thinned antenna array with respect to antenna spacing to improve the sidelobe performance. A least square method based on a modified version of the modified perturbation method is proposed to efficiently synthesize an optimum pattern in a thinned array. It is demonstrated that the array performance improves with the proposed method, compared with the conventional method.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 추계학술대회 논문집 전력기술부문
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• pp.279-281
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• 2006

A Voltage Instability Predictor(VIP) estimates the proximity of a power system to voltage collapse in real time. Voltage Instability Index(Z-index) from VIP algorithm is estimated using LS(Least Square) method. But this method has oscillations and noise of result due to the system's changing conditions. To suppress oscillations, a larger data window needs to be used. In this paper. I propose the new other method which improves that weakness. It uses RLS(Recursive Least Square) to estimate voltage instability index without a large moving data window so this method is suitable for on-line monitor and control in real time. In order to verify effectiveness of the algorithm using RLS method, the method is tested on HydroQuebec system in real time digital simulator(HYPERSIM).

• 수산해양기술연구
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• 제46권3호
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• pp.223-231
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• 2010

This paper showed the difference between the selectivity of gill net by least square method with polynomials in Kitahara's and that by maximum likelihood analysis for Japanese sandfish and Korean flounder. Catch experiments for Japanese sandfish using commercial vessels off the eastern coast of Korea were conducted with six different mesh sizes between October and December 2007 and those for Korean flounder with five different mesh sizes between 2008 and 2009. The mesh size of 50% probability of catch corresponding to biological maturity length of fish was not different between that by least square method and that by maximum likelihood analysis for Japanese sandfish, however, a little different for Korean flounder, that is, those mesh sizes of 50% probability of catch for biological maturity length of Korean flounder were 10.6cm and 10.1cm by least square method and maximum likelihood analysis, respectively.

• The Journal of the Acoustical Society of Korea
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• 제25권2E호
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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.