• The Pure and Applied Mathematics
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• v.30 no.4
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• pp.347-355
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• 2023

For numerical evaluation of Cauchy principal value integrals, we present a simple rational function with a parameter satisfying some reasonable conditions. The proposed rational function is employed in coordinate transformation for accelerating the accuracy of the Gauss quadrature rule. The efficiency of the proposed rational transformation method is demonstrated by the numerical result of a selected test example.

• Food Science and Biotechnology
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• v.14 no.4
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• pp.554-557
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• 2005

As a device for taste sensation, an 8-channel taste evaluation system was prepared and applied for discriminant analysis of marketed liquor. The biomimetic polymer membranes for the system were prepared through a casting procedure by employing polyvinyl chloride, bis (2-ethylhexyl)sebacate as plasticizer and electroactive materials such as valinomycin in the ratio of 33:66:1, and were separately attached over the sensitive area of ion-selective electrodes to construct the corresponding taste sensor array. The sensor array in conjunction with a double junction reference electrode was connected to a high-input impedance amplifier and the amplified sensor signals were interfaced to a personal computer via an A/D converter. When the signal data from the sensor array for 3 groups of marketed liquor like Maesilju, Soju and beer were analyzed by principal component analysis after normalization, it was observed that the 1st, 2nd and 3rd principal component were responsible for most of the total data variance, and the analyzed liquor samples were discriminated well in 2 dimensional principal component planes composed of the 1st-2nd and the 1st-3rd principal component.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.36 no.4
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• pp.59-63
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• 2013

In biomass gasification, efficiency of energy quantification is a difficult part without finishing the process. In this article, a radial basis function neural network (RBFN) is proposed to predict biomass efficiency before gasification. RBFN will be compared with a principal component regression (PCR) and a multilayer perceptron neural network (MLPN). Due to the high dimensionality of data, principal component transform is first used in PCR and afterwards, ordinary regression is applied to selected principal components for modeling. Multilayer perceptron neural network (MLPN) is also used without any preprocessing. For this research, 3 wood samples and 3 other feedstock are used and they are near infrared (NIR) spectrum data with high-dimensionality. Ash and char are used as response variables. The comparison results of two responses will be shown.

• Journal of the Chungcheong Mathematical Society
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• v.21 no.3
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• pp.371-376
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• 2008

Let $P(M,G,{\pi})=:P$ be a principal fibre bundle with structure Lie group G over a base manifold M. In this paper we get the following facts: 1. The tangent bundle TG of the structure Lie group G in $P(M,G,{\pi})=:P$ is a Lie group. 2. The Lie algebra ${\mathcal{g}}=T_eG$ is a normal subgroup of the Lie group TG. 3. $TP(TM,TG,{\pi}_*)=:TP$ is a principal fibre bundle with structure Lie group TG and projection ${\pi}_*$ over base manifold TM, where ${\pi}_*$ is the differential map of the projection ${\pi}$ of P onto M. 4. for a Lie group $H,\;TH=H{\circ}T_eH=T_eH{\circ}H=TH$ and $H{\cap}T_eH=\{e\}$, but H is not a normal subgroup of the group TH in general.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2001.10a
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• pp.154-157
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• 2001

Through this study we investigated the causes of bottom crack. We then redesigned petaloid bottom to prevent bottom crack. We examined the material property variations according to the stretch ratio of PET and analyzed stretches of bottom in blowing processes. We also performed crack test to observe a crack phenomena. The effective stress and maximum principal stress were examined by computer simulation. We concluded that the bottom crack occurs because of not only insufficient strength of material due to the insufficient stretch of PET but also coarse design of petaloid shape. The highest maximum principal stress occurred at valley in petaloid bottom of bottle and this strongly affected the crack in bottom. We redesigned petaloid shape to minimize maximum principal stress, and this result in increasing the crack resistance.

• Journal of the Korean Data and Information Science Society
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• v.14 no.2
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• pp.345-354
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• 2003

An incremental kernel principal component analysis(IKPCA) is proposed for the nonlinear feature extraction from the data. The problem of batch kernel principal component analysis(KPCA) is that the computation becomes prohibitive when the data set is large. Another problem is that, in order to update the eigenvectors with another data, the whole eigenvectors should be recomputed. IKPCA overcomes this problem by incrementally updating the eigenspace model. IKPCA is more efficient in memory requirement than a batch KPCA and can be easily improved by re-learning the data. In our experiments we show that IKPCA is comparable in performance to a batch KPCA for the classification problem on nonlinear data set.

• Journal of Biomedical Engineering Research
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• v.27 no.2
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• pp.59-63
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• 2006

Magnetocardiogram (MCG) topography is a useful diagnostic technique that employs multi-channel magnetocardiograms. Measurement of artifact-free MCG signals is essenctial to obtain MCG topography or map for a diagnosis of human heart. Principal component analysis (PCA) combined with an artificial neural network (ANN) is proposed to remove a pulse-type artifact in the MCG signals. The algorithm is composed of a PCA module which decomposes the obtained signal into its principal components, followed by an ANN module for the classification of the components automatically. In the experiments with volunteer subjects, 97% of the decisions that were made by the ANN were identical to those by the human experts. Using the proposed technique, the MCG topography was successfully obtained without the artifact.

• Journal of the Korean Statistical Society
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• v.25 no.1
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• pp.49-66
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• 1996

The singular value decomposition is one of the most useful methods in the area of matrix computation. It gives dimension reduction which is the centeral idea in many multivariate analyses. But this method is not resistant, i.e., it is very sensitive to small changes in the input data. In this article, we derive the resistant version of singular value decomposition for principal component analysis. And we give its statistical applications to biplot which is similar to principal component analysis in aspects of the dimension reduction of an n x p data matrix. Therefore, we derive the resistant principal component analysis and biplot based on the resistant singular value decomposition. They provide graphical multivariate data analyses relatively little influenced by outlying observations.

• Communications for Statistical Applications and Methods
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• v.27 no.1
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• pp.149-161
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• 2020

In this paper we extend the functional principal component analysis for real-valued random functions to the case of Hilbert-space-valued functional random objects. For this, we introduce an autocovariance operator acting on the space of real-valued functions. We establish an eigendecomposition of the autocovariance operator and a Karuhnen-Loève expansion. We propose the estimators of the eigenfunctions and the functional principal component scores, and investigate the rates of convergence of the estimators to their targets. We detail the implementation of the methodology for the cases of compositional vectors and density functions, and illustrate the method by analyzing time-varying population composition data. We also discuss an extension of the methodology to multivariate cases and develop the corresponding theory.

• Journal of Korea Technical Association of The Pulp and Paper Industry
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• v.48 no.1
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• pp.34-42
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• 2016

In this study, we classified three copying papers and Korean, Chinese, and Japanese traditional papers using IR and/or NIR spectra and principal component analysis. Various chemicals are used when producing fine papers. In this case, the IR method to analyze functional groups is suitable for the classification of paper. On the other hand, NIR analysis is more suitable for the classification of traditional papers, as it uses nearly raw materials (pulp). Therefore, principal component analysis using IR and NIR depending on the paper production process will be the classification tool of paper.