• Communications of the Korean Mathematical Society
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• v.16 no.4
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• pp.543-566
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• 2001

이차원 선형 탄성방정식을 소개하고 약한 형식 타원성을 보여준다(P-1)순응 유한요소를 사용할 때 나타나는 잠김현상을 설명하고 그 해결책으로서 비순응 유한요소법과 penalty 항을 가진 혼합문제, 일계 최소자승법 등을 소개한다.

• Journal of Satellite, Information and Communications
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• v.7 no.3
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• pp.94-98
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• 2012

In order to serve the exact torque clamping force, the torque measurement system use the curve fitting algorithm by the least square. The corrected least square curve fitting algorithm which suggested in this paper can surpport more exact clamping force for fastner in variable industry field using the torque. At first, This paper introduces mathematical modeling for curve fitting algorithm, and simulate it. As a result, the corrected least square algorithm have shown lower standard error value than that of the used algoritm with torque, and so this corrected least square algorithm prove high accuracy than nomal least square algorithm. The suggested algorithm will contribute to improvement of cost and safety on industry field with bolt clamping force for precision industry parts, electronics parts, aircraft, aerospace, etc.

• Proceedings of the Korean Society of Computer Information Conference
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• 2024.01a
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• pp.419-422
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• 2024

본 논문에서는 다항식 보간법의 일종인 이동최소자승법(Moving least squares, MLS)을 네트워크로 학습하여, Divergence-constrained MLS 벡터장을 효율적으로 표현하는 방법을 제안한다. 벡터장을 구성하기 위해 MLS는 스칼라가 아닌 벡터 보간을 해야 하므로 행렬과 벡터의 크기가 더 커지며, 이는 계산량이 커짐을 나타낸다. 고차 보간(High-order interpolation)이 가능한 특징은 장점이 되지만, 계산량이 매우 크기 때문에 시뮬레이션에는 활용이 어렵다. Divergence-constrained MLS를 유체 시뮬레이션에 적용한 경우가 있지만, 실제로 슈퍼컴퓨터(Supercomputer)를 해야 장면 제작이 가능하므로 효용성이 떨어진다. 본 논문에서는 이러한 문제를 해결하기 위해 네트워크 학습을 통한 Divergence-constrained MLS 벡터장을 표현할 수 있는 결과를 보여준다.

• Journal of the Korea Society of Computer and Information
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• v.1 no.1
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• pp.95-104
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• 1996

The Hough transform Is a robust technique Which Is useful in defecting straight lines in an picture. However, the extension of the conventional Hough transform to recover circles and ellipses has been limited by slow speed and excessive memory .This paper presents a method of detecting ellipses from the Image by using Least Square Method. This method Is reduced calculation cost and memory requirement .When detecting ellipse. Instead of obtaining accumulation of Hough transform for determination of ellipse parameters. particular points containing geometric properties of ellipse are selected. Parameters of the ellipse are calculated by Least Square Method using those particular points.

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.193-196
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• 2000

본 논문은 희소어레이의 패턴을 원하는 패턴과 실제 희소어레이의 패턴간의 오차의 계수적 자승치를 미세탐색을 이용하여 최소화하여 최적화하는 방법을 제시한다. 센서의 간격이 어레이 중심에 관하여 대칭인 경우와 비대칭인 경우에 대하여 성능을 점검하며, 어레이 공간의 주어진 영역의 오차함수에 성능 향상을 위하여 계수를 적용한다. 미세탐색을 이용함으로써 계수 최소 방법의 성능이 주빔 부근의 측면롭에 관련하여 향상되는 것이 판명되었다.

• Journal of the Korea Computer Graphics Society
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• v.23 no.4
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• pp.1-9
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• 2017

We propose an efficient method to determine the position of blueprint by using a vector field with optimized MLS(Moving Least Squares). Typically, a professional architectural design office takes a long time to work as well as a high processing cost because the designer manually determines the location to place the buildings in a specific area. In order to solve this inefficient problem, we propose a method to automatically determine the location of the blueprint based on the optimized MLS method. In the proposed framework, the designer selects the desired region in the actual city data and calculates the flow of the vector based on the region. Use the optimized MLS method to extract the vector field and determine the amount of rotation of the drawing based on this field. The location of the blueprint determined by the proposed method is very similar to the flow seen when the actual building is located. As a result, the efficiency of the overall architectural design process is further improved by reducing the designer's inefficient workforce.

• Proceedings of the KIEE Conference
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• 1984.07a
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• pp.96-98
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• 1984

• The Journal of the Acoustical Society of Korea
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• v.14 no.1E
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• pp.56-64
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• 1995

The least mean fourth (LMF) adaptive algorithm is a stochastic gradient method that minimizes the error in the mean fourth sense. Despite its potential advantages, the algorithm is much less popular than the conventional least mean square (LMS) algorithm in practice. This seems partly because the analysis of the LMF algorithm is much more difficult than that of the LMS algorithm, and thus not much still has been known about the algorithm. In this paper, we explore the statistical convergence behavior of the LMF algorithm when the input to the adaptive filter is zero-mean, wide-sense stationary, and Gaussian. Under a system idenrification mode, a set of nonlinear evolution equations that characterizes the mean and mean-squared behavior of the algorithm is derived. A condition for the conbergence is then found, and it turns out that the conbergence of the LMF algorithm strongly depends on the choice of initial conditions. Performances of the LMF algorithm are compared with those of the LMS algorithm. It is observed that the mean convergence of the LMF algorithm is much faster than that of the LMS algorithm when the two algorithms are designed to achieve the same steady-state mean-squared estimation error.

• Korean Journal of Remote Sensing
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• v.13 no.2
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• pp.121-132
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• 1997

This paper describes the development of a stereo matching algorithm for extracting Digital Elevation Model(DEM) from satellite images. This matching algorithm is based on a non-linear least squares correlation estimation but has improved matching speed. The algorithm consists of three steps: matching execution, matching control and matching optimization. Each is described. The performance of the presented algorithm is quantitatively analyzed with experiments on matching probability, matching speed and matching convergence radius.

• The Journal of the Acoustical Society of Korea
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• v.14 no.3
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• pp.37-48
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• 1995

This paper presents an sstimation of ARMA coefficients of noisy ARMA system using generalized total least square (GTLS) method. GTLS problem for ARMA system is defined as minimizing the errors between the noisy output vectors and estimated noisy-free output. The GTLS problem is solved in closed form by eigen-problem and the perturbation analysis of GTLS is presented. Also its recursive solution (recursive GTLS) is proposed using the power method and the covariance formula of the projected output error vector into the input vector space. The simulation results show that GTLS ARMA coefficients estimator is an unbiased estimator and that recursive GTLS achieves fast convergence.