• Proceedings of the Korean Information Science Society Conference
• /
• 2010.06c
• /
• pp.390-394
• /
• 2010

본 논문에서는 영상인식에서 널리 사용되는 지역적 특징인 SIFT와 부분공간분석에 의한 차원축소방법의 결합을 통하여 얼굴을 인식하는 방법을 제안한다. 기존의 SIFT기반 영상인식 방법에서는 추출된 키 포인트 각각에 대하여 계산된 특징기술자들을 개별적으로 비교하여 얻어지는 유사도를 바탕으로 인식을 수행하는데 반해, 본 논문에서 제안하는 접근법은 SIFT의 특징기술자를 명도 값으로 표현된 얼굴 영상을 여려 변형에 강건한 형태로 표현되도록 변환하는 표현방식으로 본다. SIFT기반의 특징기술자에 의해 표현된 얼굴 영상을 부분공간분석법에 의해 저차원의 특징벡터로 다시 표현되고, 이 특징벡터를 이용하여 얼굴인식을 수행한다. 잘 알려진 벤치마크 데이터인 AR 데이터베이스에 대한 실험을 통해 제안한 방법이 조명 변화와 가려짐에 강인한 인식 결과를 보여줄 뿐 아니라, 기존의 SIFT 기반의 얼굴 인식 방법에 비하여 우수한 처리 속도를 보임을 확인하였다.

• Bulletin of the Korean Mathematical Society
• /
• v.56 no.6
• /
• pp.1643-1653
• /
• 2019

This paper shows that if $1<p<{\infty}$, ${\alpha}{\geq}-n-2$, ${\alpha}>-1-{\frac{p}{2}}$ and f is holomorphic on the unit ball ${\mathbb{B}}_n$, then $${\int_{{\mathbb{B}}_n}}{\mid}Rf(z){\mid}^p(1-{\mid}z{\mid}^2)^{p+{\alpha}}dv_{\alpha}(z)<{\infty}$$ if and only if $${\int_{{\mathbb{B}}_n}}{\int_{{\mathbb{B}}_n}}{\frac{{\mid}f(z)-F({\omega}){\mid}^p}{{\mid}1-(z,{\omega}){\mid}^{n+1+s+t-{\alpha}}}}(1-{\mid}{\omega}{\mid}^2)^s(1-{\mid}z{\mid}^2)^tdv(z)dv({\omega})<{\infty}$$ where s, t > -1 with $min(s,t)>{\alpha}$.

• Journal of the Korean Society for Industrial and Applied Mathematics
• /
• v.23 no.3
• /
• pp.211-236
• /
• 2019

This paper is devoted to the study and investigation of the Runge-Kutta discontinuous Galerkin method for a system of differential equations consisting of two hyperbolic conservation laws. The numerical coupling flux which is used at a given interface (x = 0) is the upwind flux. Moreover, in the linear case, we derive optimal convergence rates in the $L_2$-norm, showing an error estimate of order ${\mathcal{O}}(h^{k+1})$ in domains where the exact solution is smooth; here h is the mesh width and k is the degree of the (orthogonal Legendre) polynomial functions spanning the finite element subspace. The underlying temporal discretization scheme in time is the third-order total variation diminishing Runge-Kutta scheme. We justify the advantages of the Runge-Kutta discontinuous Galerkin method in a series of numerical examples.

• ETRI Journal
• /
• v.40 no.6
• /
• pp.714-725
• /
• 2018

Destination-assisted jamming (DAJ) is usually used to protect confidential information against untrusted relays and eavesdroppers in wireless networks. In this paper, a DAJ-based untrusted relay network with multiple antennas installed is presented. To increase the secrecy, a joint optimization of beamforming and power allocation at the source and destination is studied. A matched-filter precoder is introduced to maximize the cooperative jamming signal by directing cooperative jamming signals toward untrusted relays. Then, based on generalized singular-value decomposition (GSVD), a novel transmitted precoder for confidential signals is devised to align the signal into the subspace corresponding to the confidential transmission channel. To decouple the precoder design and optimal power allocation, an iterative algorithm is proposed to jointly optimize the above parameters. Numerical results validate the effectiveness of the proposed scheme. Compared with other schemes, the proposed scheme shows significant improvement in terms of security performance.

• Smart Structures and Systems
• /
• v.22 no.6
• /
• pp.711-725
• /
• 2018

Most of the practical engineering structures exhibit nonlinearity due to nonlinear dynamic characteristics of structural joints, nonlinear boundary conditions and nonlinear material properties. Hence, it is highly desirable to detect and characterize the nonlinearity present in the system in order to assess the true behaviour of the structural system. Further, these identified nonlinear features can be effectively used for damage diagnosis during structural health monitoring. In this paper, we focus on the detection of the nonlinearity present in the system by confining our discussion to only a few selective time-frequency analysis and multivariate analysis based techniques. Both damage induced nonlinearity and inherent structural nonlinearity in healthy systems are considered. The strengths and weakness of various techniques for nonlinear detection are investigated through numerically simulated two different classes of nonlinear problems. These numerical results are complemented with the experimental data to demonstrate its suitability to the practical problems.

• Communications for Statistical Applications and Methods
• /
• v.26 no.4
• /
• pp.337-346
• /
• 2019

This paper discusses a method for obtaining nonnegative estimates for variance components in a random effects model. A variance component should be positive by definition. Nevertheless, estimates of variance components are sometimes given as negative values, which is not desirable. The proposed method is based on two basic ideas. One is the identification of the orthogonal vector subspaces according to factors and the other is to ascertain the projection in each orthogonal vector subspace. Hence, an observation vector can be denoted by the sum of projections. The method suggested here always produces nonnegative estimates using projections. Hartley's synthesis is used for the calculation of expected values of quadratic forms. It also discusses how to set up a residual model for each projection.

• Journal of the Korean Data Analysis Society
• /
• v.20 no.6
• /
• pp.2733-2746
• /
• 2018

The two inverse regression estimation methods, SIR and SAVE to estimate the central space are computationally easy and are widely used. However, SIR and SAVE may have poor performance in finite samples and need strong assumptions (linearity and/or constant covariance conditions) on predictors. The two non-parametric estimation methods, MAVE and dMAVE have much better performance for finite samples than SIR and SAVE. MAVE and dMAVE need no strong requirements on predictors or on the response variable. MAVE is focused on estimating the central mean subspace, but dMAVE is to estimate the central space. This paper explores and compares four methods to explain the dimension reduction. Each algorithm of these four methods is reviewed. Empirical study for simulated data shows that MAVE and dMAVE has relatively better performance than SIR and SAVE, regardless of not only different models but also different distributional assumptions of predictors. However, real data example with the binary response demonstrates that SAVE is better than other methods.

• Journal of applied mathematics & informatics
• /
• v.39 no.3_4
• /
• pp.487-496
• /
• 2021

In this paper we show that if A ∈ L(X) and B ∈ L(Y), X and Y complex Banach spaces, then A ⊕ B ∈ L(X ⊕ Y) is subscalar if and only if both A and B are subscalar. We also prove that if A, Q ∈ L(X) satisfies AQ = QA and Q^p = 0 for some nonnegative integer p, then A has property (C) (resp. property (𝛽)) if and only if so does A + Q (resp. property (𝛽)). Finally, we show that A ∈ L(X, Y) and B, C ∈ L(Y, X) satisfying operator equation ABA = ACA and BA ∈ L(X) is subscalar with property (𝛿) then both Lat(BA) and Lat(AC) are non-trivial.

• Journal of the Korean Society for Industrial and Applied Mathematics
• /
• v.24 no.4
• /
• pp.375-385
• /
• 2020

In this paper, a special indefinite inner product, named hyperbolic scalar product, is used and all acquired results have been raised and proved with the proviso that the space is equipped with this indefinite scalar product. The main objective is to be introduced and applied an indefinite oblique projection method, called Indefinite Lanczos J-biorthogonalizatiom process, which in addition to building a pair of J-biorthogonal bases for two used Krylov subspaces, leads to the introduction of a process for solving large non-J-symmetric linear systems, i.e., Indefinite two-sided Lanczos Algorithm for Linear systems.

• Journal of the Chungcheong Mathematical Society
• /
• v.34 no.2
• /
• pp.181-188
• /
• 2021

Let X be a nondegenerate reduced closed subscheme in ℙⁿ. Assume that π_q : X → Y = π_q(X) ⊂ ℙ^n-1 is a generic projection from the center q ∈ Sec(X) \ X where Sec(X) = ℙⁿ. Let Z be the singular locus of the projection π_q(X) ⊂ ℙ^n-1. Suppose that I_X has the almost minimal presentation, which is of the form R(-3)^β_2,1 ⊕ R(-4) → R(-2)^β_1,1 → I_X → 0. In this paper, we prove the followings: (a) Z is either a linear space or a quadric hypersurface in a linear subspace; (b) $H^1({\mathcal{I}_X(k)})=H^1({\mathcal{I}_Y(k)})$ for all k ∈ ℤ; (c) reg(Y) ≤ max{reg(X), 4}; (d) Y is cut out by at most quartic hypersurfaces.