• The Korean Journal of Applied Statistics
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• v.32 no.4
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• pp.607-618
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• 2019

A new and interesting task in statistics is to effectively analyze functional data that frequently comes from advances in modern science and technology in areas such as meteorology and biomedical sciences. Functional linear regression with scalar response is a popular functional data analysis technique and it is often a common problem to determine a functional association if a functional predictor variable affects the scalar response in the models. Recently, Kong et al. (Journal of Nonparametric Statistics, 28, 813-838, 2016) established classical testing methods for this based on functional principal component analysis (of the functional predictor), that is, the resulting eigenfunctions (as a basis). However, the eigenbasis functions are not generally suitable for regression purpose because they are only concerned with the variability of the functional predictor, not the functional association of interest in testing problems. Additionally, eigenfunctions are to be estimated from data so that estimation errors might be involved in the performance of testing procedures. To circumvent these issues, we propose a testing method based on fixed basis such as B-splines and show that it works well via simulations. It is also illustrated via simulated and real data examples that the proposed testing method provides more effective and intuitive results due to the localization properties of B-splines.

• Journal of the Korean Data and Information Science Society
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• v.23 no.3
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• pp.487-494
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• 2012

This paper discusses a method for getting a basis set of estimable functions of model parameters in a two-way fixed effects model. Since the fixed effects model has more parameters than those that can be estimated, model parameters are not estimable. So it is not possible to make inferences for nonestimable functions of parameters. When the assumed model of matrix notation is reparameterized by the estimable functions in a basis set, it also discusses how to use projections for the estimation of estimable functions.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2009.04a
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• pp.488-491
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• 2009

본 논문에서는 응력함수와 Kantorovich method를 이용하여 기저판(substrate)에 인장과 굽힘이 작용할 때 복합재 패치의 3차원 응력을 해석하였다. 면내 방향과 면외 방향의 두 응력함수에 가상 공액일의 법칙(Complementary virtual work principle)을 적용하였으며 복합재 패치의 자유 경계조건과 바닥의 기저판으로부터 전달되는 전단 수직 응력 조건을 부여하였다. 이를 통해서 패치 구조물의 지배방정식을 연립 미분 방정식 형태의 고유치 문제로 변환하여 응력함수를 구하였다. 위 방법의 타당성과 효용성을 검증하기 위한 수치 예제로 cross-ply, angle-ply, quasi-isotropic의 패치 적층 배열을 고려하였으며, 층간 응력함수 값이 자유 경계에서 최고치를 나타내고 패치 중심부로 갈수록 급격히 감소하는 모습을 확인하였다. 제안된 기법은 기저판에 인장하중이 작용하는 경우뿐만 아니라 굽힘 하중이 작용하는 경우에도 정확한 예측이 가능하여, 패치 구조물의 층간 응력을 포함한 3차원 응력을 해석하는데 있어서 효율적인 해석 도구로서 사용할 수 있을 것이라 사료된다.

• The Journal of the Korea Contents Association
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• v.5 no.6
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• pp.41-48
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• 2005

This paper presents a face recognition method based on the combination of well-known statistical representations of Principal Component Analysis(PCA), and Linear Discriminant Analysis(LDA) with Radial Basis Function Networks. The original face image is first processed by PCA to reduce the dimension, and thereby avoid the singularity of the within-class scatter matrix in LDA calculation. The result of PCA process is applied to LDA classifier. In the second approach, the LDA process Produce a discriminational features of the face image, which is taken as the input of the Radial Basis Function Network(RBFN). The proposed approaches has been tested on the ORL face database. The experimental results have been demonstrated, and the recognition rate of more than 93.5% has been achieved.

• Journal of the Korea institute for structural maintenance and inspection
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• v.19 no.5
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• pp.30-37
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• 2015

The estimation of system damping parameter of the response signal with lower natural frequency and higher damping parameter from free vibration is affected by the wavelet center frequency. This study discusses these considerations in the context of the wavelet's multi-resolution character and includes guidelines for selection of wavelet center frequency. The experiment with H-Beam and numerical examples with respect to three cases (i)single mode, (ii)separated modes and (iii)close modes demonstrate the validity of method to improve the accuracy of the estimated damping parameter. The localization of the corresponding scale for the total scales is determined by the natural frequency of the analysing mode and is affected by the wavelet center frequency. Thus, the reliability for the accuracy of the estimated damping parameter can be improved by the corresponding scale of the natural frequency for the analysing mode is localized at the half of the total scales.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.735-740
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• 2012

In this paper, we introduce design methodologies of polynomial radial basis function neural network classifier with the aid of Principal Component Analysis(PCA) and Linear Discriminant Analysis(LDA). By minimizing the information loss of given data, Feature data is obtained through preprocessing of PCA and LDA and then this data is used as input data of RBFNNs. The hidden layer of RBFNNs is built up by Fuzzy C-Mean(FCM) clustering algorithm instead of receptive fields and linear polynomial function is used as connection weights between hidden and output layer. In order to design optimized classifier, the structural and parametric values such as the number of eigenvectors of PCA and LDA, and fuzzification coefficient of FCM algorithm are optimized by Artificial Bee Colony(ABC) optimization algorithm. The proposed classifier is applied to some machine learning datasets and its result is compared with some other classifiers.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.4
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• pp.153-159
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• 2011

The MUSIC (MUltiple SIgnal Classification) method estimates the directions of arrival (DOAs) of the signals impinging on a sensor array based on the fact that the noise subspace is orthogonal to the signal subspace. In the conventional MUSIC, an estimate of the basis for the noise subspace is obtained by eigendecomposing the sample matrix, which is computationally expensive. In this paper, we present a simple DOA estimation method which finds an estimate of the signal subspace basis directly from the columns of the sample matrix from which the noise power components are removed. DOA estimates are obtained by searching for minimum points of a cost function which is defined using the estimated signal subspace basis. The minimum points are efficiently found through the Brent method which employs parabolic interpolation. Simulation shows that the simple estimation method virtually has the same performance as the complex conventional method based on the eigendecomposition.

• The Korean Journal of Applied Statistics
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• v.27 no.4
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• pp.553-560
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• 2014

This paper deals with estimable functions of parameters of less than full rank linear model. In general, the parameters of an overspecified model are not uniquely determined by least squares solutions. It discusses how to formulate linear estimable functions as functions of parameters in the model and shows how to use projection matrices to check out whether a parameter or function of the pamameters is estimable. It also presents a method to form a basis set of estimable functions using linearly independent characteristic vectors generating the row space of the model matrix.

• Journal of Korean Association for Spatial Structures
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• v.7 no.4
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• pp.89-96
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• 2007

The fundamental frequency maximization of beam structures is carried out by using strain energy based topology optimization technique. It mainly uses the modal strain energy distributions induced by the mode shapes of the structures. The modal strain energy to be minimized is employed as the objective function and the initial volume of structures is adopted as the constraint function. The resizing algorithm devised from the optimality criteria method is used to update the hole size of the cell existing in each finite element. The beams with three different boundary conditions are used to investigate the optimum topologies against natural mode shapes. From numerical test, it is found to be that the optimum topologies of the beams produced by the adopted technique have hugh increases in some values of natural frequencies and especially the technique is very effective to maximize the fundamental frequency of the structures.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.6 no.2
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• pp.174-185
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• 1994

This paper presents a modal solution of linear three-dimensional hydrodynamic equations using similarity transform technique. The solution over the vertical space domain is obtained using the Galerkin method with linear shape funtions (Galerkin-FEM model). Application of similarity transform to resulting tri-diagonal matrix equations gives rise 掠 a set of uncoupled partial differential equations of which the unknowns are coefficients of mode shape vectors. The proposed method.