• Journal of the Korean Data and Information Science Society
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• v.26 no.1
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• pp.31-39
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• 2015

This paper deals with a method for estimating variance components on the basis of projections under the assumption of random effects model. It discusses how to use projections for getting sums of squares to estimate variance components. The use of projections makes the vector subspace generated by the model matrix to be decomposed into subspaces that are orthogonal each other. To partition the vector space by the model matrix stepwise procedure is used. It is shown that the suggested method is useful for obtaining Type I sum of squares requisite for the ANOVA method.

• Journal of the Korean Data and Information Science Society
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• v.26 no.2
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• pp.347-354
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• 2015

This paper deals with a method for intrablock anlaysis of balanced incomplete block designs on the basis of projections under the assumption of mixed effects model. It shows how to construct a model at each step by the stepwise procedure and discusses how to use projection for the analysis of intrablock. Projections are obtained in vector subspaces orthogonal to each other. So the estimates of the treatment effects are not affected by the block effects. The estimability of a parameter or a function of parameters is discussed and eigenvectors are dealt for the construction of estimable functions.

• Journal of the Korea Society of Computer and Information
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• v.27 no.5
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• pp.77-84
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• 2022

In this paper, we propose a novel unsupervised feature selection method. Conventional unsupervised feature selection method defines virtual label and uses a regression analysis that projects the given data to this label. However, since virtual labels are generated from data, they can be formed similarly in the space. Thus, in the conventional method, the features can be selected in only restricted space. To solve this problem, in this paper, features are selected using orthogonal projections and low-rank approximations. To solve this problem, in this paper, a virtual label is projected to orthogonal space and the given data set is also projected to this space. Through this process, effective features can be selected. In addition, projection matrix is restricted low-rank to allow more effective features to be selected in low-dimensional space. To achieve these objectives, a cost function is designed and an efficient optimization method is proposed. Experimental results for six data sets demonstrate that the proposed method outperforms existing conventional unsupervised feature selection methods in most cases.

• Journal of Convergence for Information Technology
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• v.7 no.3
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• pp.65-71
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• 2017

The quantum logic was introduced by G. Birkhoff and 1. von Neumann in order to study projections of a Hilbert space for a formulation of quantum mechanics, and Husimi proposed orthomodular law and orthomodular lattices to complement the quantum logic. Abott introduced orthoimplication algebras and its properties to investigate an implication of orthomodular lattice. The commuting relation is an important property on orthomodular lattice which is related with the distributive law and the modular law, etc. In this paper, we define a binary operation on orthoimplication algebra and the greatest lower bound by using this operation and research some properties of this operation. Also we define a homomorphism and characterize the commuting relation of orthoimplication algebra by the homomorphism.

• Annual Conference of KIPS
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• 2002.11a
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• pp.35-38
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• 2002

본 논문에서는 3차원 재구성에 있어서 필수 불가결한 기술인 카메라 교정 방법에 있어서 특정 교정 물체나 또는 영상에서의 제약 조건 등을 요구하지 않고 영상 내에 산재되어 있는 기하학 정보를 이용하여 카메라 내부 파라미터를 추출하고 영상간의 카메라 움직임을 계산하여 3차원 재구성하는 알고리즘을 제안한다. 공간에서의 직교하는 평행선들의 집합이 만들어 낸 자 축 방향으로의 3개의 소실 점을 이용하면 그 투영 영상에 대한 카메라 내부 파라미터를 얻을 수 있게 된다. 또 한 영상간의 대응점 관계를 이용하면 두 영상 사이의 상대적인 카메라의 회진 및 이동 성분을 얻어 낼 수 있다. 따라서 카메라의 내부, 외부 성분을 추출함으로써 사영 행렬을 계산하고 역 투영 방법에 의해서 3차원 재구성을 구현하게 된다.

• Journal of Advanced Navigation Technology
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• v.5 no.2
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• pp.111-122
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• 2001

In this paper, we study the performance of eigencanceller which use a eigenvector and eigenvalue in order to update a weighter vector. Eigencanceller can suppress directional interferences and noise effectively while maintaining specified beam pattern constraints. The constraints and optimal weight vector of eigencanceller vary by using interference and noise or desired signal, interference signal and noise as array input signal. From the analysis results in the steady state, We show that weight vectors in each case are simplified the form of projection equation that belongs to desired subspace orthogonal to interference subspace and eigencanceller has the better performance than RLS method through mathematical analysis and simulation.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.12 no.5
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• pp.694-704
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• 2001

In this work, We evaluate the performance of eigencanceller which can suppress directional interferences and noise effectively while maintaining specified beam pattern constraints. The constraints and optimal weight vector of eigencanceller vary by using interference and noise or desired signal, interference and noise as array input signal. From the analysis results in the steady state, We show that weight vectors in each case are simplified the form of projection equation that belongs to desired subspace orthogonal to interference subspace and eigencanceller has the better performance than DMI method through mathematical analysis and simulation.