• East Asian mathematical journal
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• v.17 no.2
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• pp.325-337
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• 2001

We consider the special hypersurface of the first approximate Matsumoto metric with $b_i(x)={\partial}_ib$ being the gradient of a scalar function b(x). In this paper, we consider the hypersurface of the first approximate Matsumoto space with the same equation b(x)=constant. We are devoted to finding the condition for this hypersurface to be a hyperplane of the first or second kind. We show that this hypersurface is not a hyper-plane of third kind.

• Proceedings of the Korea Multimedia Society Conference
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• 2004.05a
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• pp.608-611
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• 2004

사용자 웹 방문 패턴 발견으로써의 사용자 클러스터링은 웹 사이트를 이용하는 사용자들의 취향과 행동방식을 얻어내는데 매우 유용하다. 또한 이러한 정보는 웹 개인화나 웹 사이트를 재구성 하는 데 필수적 이 다. 본 논문에서 사용자 웹 방문 패스를 클러스터링 하기 위한 시간적으로 효율적이며, 패스 특성을 보다 정확하게 표현하여 클러스터링 할 수 있는 알고리즘이 제안되며, 제안된 알고리즘은 패스 간의 유사도 측정을 통한 클러스터링, 하이퍼플랜을 이용한 K-평균 클러스터링의 2단계 과정으로 이루어져 있다.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.6
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• pp.142-147
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• 2008

In this paper, we propose a novel a roach to improve the performance of speech/music classification for the selectable mode vocoder (SMV) of 3GPP2 using the support vector machine (SVM). The SVM makes it possible to build on an optimal hyperplane that is separated without the error where the distance between the closest vectors and the hyperplane is maximal. We first present an effective analysis of the features and the classification method adopted in the conventional SMV. And then feature vectors which are a lied to the SVM are selected from relevant parameters of the SMV for the efficient speech/music classification. The performance of the proposed algorithm is evaluated under various conditions and yields better results compared with the conventional scheme of the SMV.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.5
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• pp.2375-2382
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• 2011

The user clustering for web navigation pattern discovery is very useful to get preference and behavior pattern of users for web pages. In addition, the information by the user clustering is very essential for web personalization or customer grouping. In this paper, an algorithm for clustering the web navigation path of users is proposed and then some special navigation patterns can be recognized by the algorithm. The proposed algorithm has two clustering phases. In the first phase, all paths are classified into k-groups on the bases of the their similarities. The initial solution obtained in the first phase is not global optimum but it gives a good and feasible initial solution for the second phase. In the second phase, the first phase solution is improved by revising the k-means algorithm. In the revised K-means algorithm, grouping the paths is performed by the hyperplane instead of the distance between a path and a group center. Experimental results show that the proposed method is more efficient.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.2
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• pp.243-253
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• 2002

This paper presents the system modeling, analysis, and controller design and implementation for a rotational inverted pendulum system(RIPS), which is an under-actuated system and has the problem of unattainable angular velocity state. A sliding mode controller using the parameterization of both the hyperplane and the compensator fur output feedback is applied to the RIPS. Also, to improve the performance of the control system, a disturbance observer which estimates the disturbance, parameter variation, and some modeling errors of RIPS with less computational effort is used together. The results of simulation and experiment show that the proposed control system has superior performance for disturbance rejection and regulation at certain initial conditions.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.405-407
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• 2009

SVM(Support Vector Machine) is a classification method which is recently watched in mechanical learning system. Vapnik, Osuna, Platt etc. had suggested methodology in order to solve needed QP(Quadratic Programming) to realize SVM so that have extended application field. SVM find hyperplane which classify into 2 class by converting from input space converter vector to characteristic space vector using Kernel Function. This is very systematic and theoretical more than neural network which is experiential study method. Although SVM has superior generalization characteristic, it depends on Kernel Function. There are three category in the Kernel Function as Polynomial Kernel, RBF(Radial Basis Function) Kernel, Sigmoid Kernel. This paper has analyzed performance of SVM against kernel using virtual data.

• Honam Mathematical Journal
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• v.35 no.1
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• pp.37-49
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• 2013

Consider a non-degenerate open convex cone C with vertex the origin in the $n$2-dimensional Euclidean space $E^n$. We study volume properties of strictly convex hypersurfaces in the cone C. As a result, for example, if the volume of the region of an elliptic cone C cut off by the tangent hyperplane P of M at $p$ is independent of the point $p{\in}M$, then it is shown that the hypersurface M is part of an elliptic hyperboloid.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.2
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• pp.82-88
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• 1999

In this paper, we propose a new structure optimization method of multilayer neural networks which begin and carry out learning from a bigger network. This method redundant links and neurons according to the rough set theory. In order to find redundant links, we analyze the variations of all weights and output errors in every step of the learning process, and then make the decision table from their variation of weights and output errors. We can find the redundant links from the initial structure by analyzing the decision table using the rough set theory. This enables us to build a structure as compact as possible, and also enables mapping between input and output. We show the validity and effectiveness of the proposed algorithm by applying it to the XOR problem.

• ETRI Journal
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• v.31 no.4
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• pp.438-447
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• 2009

In this paper, a novel image projection technique for face recognition application is proposed which is based on linear discriminant analysis (LDA) combined with the relevance-weighted (RW) method. The projection is performed through 2-directional and 2-dimensional LDA, or $(2D)^2$LDA, which simultaneously works in row and column directions to solve the small sample size problem. Moreover, a weighted discriminant hyperplane is used in the between-class scatter matrix, and an RW method is used in the within-class scatter matrix to weigh the information to resolve confusable data in these classes. This technique is called the relevance-weighted $(2D)^2$LDA, or RW$(2D)^2$LDA, which is used for a more accurate discriminant decision than that produced by the conventional LDA or 2DLDA. The proposed technique has been successfully tested on four face databases. Experimental results indicate that the proposed RW$(2D)^2$LDA algorithm is more computationally efficient than the conventional algorithms because it has fewer features and faster times. It can also improve performance and has a maximum recognition rate of over 97%.

• Honam Mathematical Journal
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• v.5 no.1
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• pp.45-69
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• 1983

The hermitian structures on complex manifolds have been studied by several mathematicians ([1], [2], and [3]), and the Kähler structure on hermitian manifolds have been so much too ([6], [12], and [15]). There has been some gradual progress in studying the invariant forms on Grassmann manifolds ([17]). The purpose of this dissertation is to prove the Theorem 3.4 and the Theorem 4.7, with relation to the nature of complex Grassmann manifolds. In $\S$ 2. in order to prove the Theorem 4.7, which will be explicated further in $\S$ 4, the concepts of the hermitian structure, connection and curvature have been defined. and the characteristic nature about these were proved. (Proposition 2.3, 2.4, 2.9, 2.11, and 2.12) Two characteristics were proved in $\S$ 3. They are almost not proved before: particularly. we proved the Theorem 3.3 : $G_{k}(C^{n+k})=\frac{GL(n+k,C)}{GL(k,n,C)}=\frac{U(n+k)}{U(k){\times}U(n)}$ In $\S$ 4. we explained and proved the Theorem 4. 7 : i) Complex Grassmann manifolds are Kahlerian. ii) This Kähler form is $\pi$-fold of curvature form in hyperplane section bundle. Prior to this proof. some propositions and lemmas were proved at the same time. (Proposition 4.2, Lemma 4.3, Corollary 4.4 and Lemma 4.5).