• Proceedings of the Korea Information Processing Society Conference
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• 2004.05a
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• pp.745-748
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• 2004

얼굴 이미지의 대부분은 표본의 수보다 특징 변수의 수가 많기 때문에 이러한 점을 고려한 특징 추출 방법이 필요하다. 본 논문에서는 부분 최소제곱법을 이용하여 특징 벡터의 차원을 축소하는 방법을 제안한다. 전통적인 차원 축소 방법인 주성분 분석은 클래스의 정보를 고려하지 않고 최대 변이를 가지는 성분을 추출하기 때문에, 클래스의 구분에 필요한 특징을 필수적으로 추출하지 못한다. 이에 비해, 부분 최소제곱법은 클래스 변수에 대한 정보를 포함하여 성분을 추출한다. 그러므로, 분류를 하는데 있어서는 주성분 분석에 의해 추출된 성분보다는 부분 최소제곱법에 의해 추출된 성분이 보다 더 예측적이다. 맨체스터와 ORL 얼굴 데이터베이스를 이용하여 실험한 결과, 분류와 차원 축소 측면에서 주성분 분석 방법보다는 부분 최소제곱법을 이용한 방법이 그 성능이 우수함을 알 수 있었다.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2010.04a
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• pp.349-353
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• 2010

The existing calculation methods of the cadastral traverse survey was established in 1910s and are mostly outdated. The quality of these methods are not adequate to satisfy today's surveyor's needs that use a simple calculation method to distribute the error values. Thus, the main objective of this research is to find a methodology for appropriate calculation methods of the cadastral traverse survey that uses the general least square adjustment method with weight. Consequently, least square adjustment method has a better result than the previous one.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2009.04a
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• pp.308-313
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• 2009

본 논문은 확장된 이동최소제곱 유한차분법을 이용하여 1차원 Stefan 문제를 해석할 수 있는 수치기법이 제시한다. 이동하는 경계의 자유로운 묘사를 위해 요소망이나 그리드 없이 절점만을 사용하는 이동최소제곱 유한차분법을 사용하였으며, 계면경계의 특이성을 모형화하기 위해 Taylor 다항식에 쐐기함수를 도입했다. 지배방정식은 안정성이 높은 음해법(implicit method)을 이용하여 차분하였다. 미분의 특이성을 갖는 이동경계를 포함한 반무한 융해문제의 수치해석을 통해 확장된 이동최소제곱 유한차분법이 높은 정확성과 효율성을 갖는 것을 보였다.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2011.04a
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• pp.719-722
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• 2011

본 연구에서는 이동최소제곱 차분법을 2차원 동적고체문제를 해석하기 위하여 확장시켰으며 Newmark ${\beta}$ 방법을 통해 explicit와 implicit 시간적분법을 모두 적용하여 그 차이를 비교하였다. 이동최소제곱 차분법은 Taylor 다항식을 이용하여 미분계산을 근사화 함으로써 내부 및 경계에서도 강형식을 그대로 이용할 수 있다. 그래서 계산이 빠르고 수치적분이 필요하지 않아 무요소법의 장점을 잘 살릴 수 있고 해석차수를 손쉽게 조정할 수 있어 cubic 등의 고차 근사계산이 간편하다. 두 가지 수치예제를 통하여 동적해석에 대한 이동최소제곱 차분법의 적용성과 안정성을 검증하였다.

• Journal of Cadastre & Land InformatiX
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• v.45 no.2
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• pp.117-130
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• 2015

A least squares method for adjusting the horizontal network satisfies the conditions which is minimizing the sum of the squares of errors based on probability theory. This research compared accuracy of 3rd cadastral control points adjusted by traditional and least square method with respect to the result of Network-RTK. Test results showed the least square method more evenly distribute closure error than traditional method. Mean errors of least square and traditional adjusting method are 2.7cm, 2.2cm respectively. In addition, blunder in angle observations can be detected by comparing position errors which calculated by forward and backward initial coordinates. However, distance blunder cannot offer specific observation line occurred mistake because distance error propagates several observation lines which have similar directions.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.20 no.6
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• pp.779-787
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• 2007

This paper presents a highly efficient moving least squares finite difference method (MLS FDM) for a heat transfer problem of bi-material with interfacial boundary. The MLS FDM directly discretizes governing differential equations based on a node set without a grid structure. In the method, difference equations are constructed by the Taylor polynomial expanded by moving least squares method. The wedge function is designed on the concept of hyperplane function and is embedded in the derivative approximation formula on the moving least squares sense. Thus interfacial singular behavior like normal derivative jump is naturally modeled and the merit of MLS FDM in fast derivative computation is assured. Numerical experiments for heat transfer problem of bi-material with different heat conductivities show that the developed method achieves high efficiency as well as good accuracy in interface problems.

• The KIPS Transactions:PartB
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• v.13B no.4 s.107
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• pp.393-400
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• 2006

There are many feature extraction methods for face recognition. We need a new method to overcome the small sample problem that the number of feature variables is larger than the sample size for face image data. The paper considers partial least squares(PLS) as a new dimension reduction technique for feature vector. Principal Component Analysis(PCA), a conventional dimension reduction method, selects the components with maximum variability, irrespective of the class information. So, PCA does not necessarily extract features that are important for the discrimination of classes. PLS, on the other hand, constructs the components so that the correlation between the class variable and themselves is maximized. Therefore PLS components are more predictive than PCA components in classification. The experimental results on Manchester and ORL databases shows that PLS is to be preferred over PCA when classification is the goal and dimension reduction is needed.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.22 no.4
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• pp.315-322
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• 2009

This paper presents a novel numerical method based on the extended moving least squares finite difference method(MLS FDM) for solving 1-D Stefan problem. The MLS FDM is employed for easy numerical modelling of the moving boundary and Taylor polynomial is extended using wedge function for accurate capturing of interfacial singularity. Difference equations for the governing equations are constructed by implicit method which makes the numerical method stable. Numerical experiments prove that the extended MLS FDM show high accuracy and efficiency in solving semi-infinite melting, cylindrical solidification problems with moving interfacial boundary.

• The Korean Journal of Applied Statistics
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• v.29 no.6
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• pp.1147-1154
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• 2016

In this paper, we numerically compare two penalized least square methods, the ${\ell}_0$-penalized method and the fused lasso regression (FLR, ${\ell}_1$ penalization), in finding multiple change points of a signal. We find that the ${\ell}_0$-penalized method performs better than the FLR, which produces many false detections in some cases as the theory tells. In addition, the computation of ${\ell}_0$-penalized method relies on dynamic programming and is as efficient as the FLR.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.26 no.1
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• pp.39-48
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• 2013

This paper develops a 2-D moving least squares(MLS) difference method for Stefan problem by extending the 1-D version of the conventional method. Unlike to 1-D interfacial modeling, the complex topology change in 2-D domain due to arbitrarily moving boundary is successfully modelled. The MLS derivative approximation that drives the kinetics of moving boundary is derived while the strong merit of MLS Difference Method that utilizes only nodal computation is effectively conserved. The governing equations are differentiated by an implicit scheme for achieving numerical stability and the moving boundary is updated by an explicit scheme for maximizing numerical efficiency. Numerical experiments prove that the MLS Difference Method shows very good accuracy and efficiency in solving complex 2-D Stefan problems.