• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2003.10a
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• pp.131-134
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• 2003

The purpose of this study is for classification to do a short distance rural area with the object to the center to Cheongju area. This study used principal component analysis and geography information system, and it was disciplined oneself. It was done a study object region to Cheongju-si, Cheongwon-gun Goesan-gun, Eumseong-gun, and we divided an index by of 22 large class and 104 small class, and the SPSS analyzed the Principal Component Analysis. We used a Geography Information System, and it was made graphical data by the results that have finished Principal Component Analysis.

• Bulletin of the Korean Chemical Society
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• v.32 no.7
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• pp.2332-2338
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• 2011

In this study, we proposed a new promising idea of utilizing moving window principal component analysis (MWPCA) as a sensitive diagnostic tool to detect the presence of peak position shift. In this approach, the moving window is constructed from a small data segment along the wavenumber axis. For each window bound by a narrow wavenumber region, separate PCA analysis was applied. Simulated spectra with complex spectral feature variations were analyzed to explore the possibility of MWPCA technique. This MWPCA-based detection of the peak shift, potentially coupled with 2D correlation analysis to provide additional verification, may offer an attractive solution.

• Proceedings of the Korean Vacuum Society Conference
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• 2013.02a
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• pp.320-320
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• 2013

Film thickness monitoring with plasma impedance monitoring (PIM) is demonstrated for small area $SiO_2$ RF plasma etching processes in this work. The chamber conditions were monitored by the impedance signal variation from the I-V monitoring system. Moreover, modified principal component analysis (mPCA) was applied to estimate the $SiO_2$ film thickness. For verification, the PIM was compared with optical emission spectroscopy (OES) signals which are widely used in the semiconductor industry. The results indicated that film thickness can be estimated by 1st principal component (PC) and 2nd PC. Film thickness monitoring of small area $SiO_2$ etching was successfully demonstrated with RF plasma harmonic impedance monitoring and mPCA. We believe that this technique can be potentially applied to plasma etching processes as a sensitive process monitoring tool.

• Phonetics and Speech Sciences
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• v.1 no.1
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• pp.69-73
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• 2009

This paper proposes an efficient global covariance-based principal component analysis (GCPCA) for speaker identification. Principal component analysis (PCA) is a feature extraction method which reduces the dimension of the feature vectors and the correlation among the feature vectors by projecting the original feature space into a small subspace through a transformation. However, it requires a larger amount of training data when performing PCA to find the eigenvalue and eigenvector matrix using the full covariance matrix by each speaker. The proposed method first calculates the global covariance matrix using training data of all speakers. It then finds the eigenvalue matrix and the corresponding eigenvector matrix from the global covariance matrix. Compared to conventional PCA and Gaussian mixture model (GMM) methods, the proposed method shows better performance while requiring less storage space and complexity in speaker identification.

• Journal of Plant Biology
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• v.27 no.1
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• pp.33-41
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• 1984

The relationships and character variations on 5 taxa of Calystegia were examined by sluster analysis and principal component analysis. Thirteen Calystegia population samples from the middle part of Korea were observed. Although minor differences were noted, essentially similar results were obtained from the phenograms by UPGMA, UPGMC and Ward's clustering methods, and these results were in accordance with those obtained from the ordination plots by principal component analysis. C. soldanella is distantly connected with the other taxa mainly because of its morphologically different leaf organs. Based on the difference on the first principal component, C. hederacae is kept apart from the rest 3 taxa. In the relationships among C. japonica, C. sepium var. americana and C. davurica, mivor differences were obtained from the 3 clustering methods. As to the character variations among different populations within a taxon, they are slight in C. soldanella and C. sepium var. americana, but remarkable in C. hederacae and C. davurica.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.50 no.1
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• pp.60-66
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• 2005

Pod shape of twenty soybean (Glycine max L. Merrill) genotypes was evaluated quantitatively by image analysis using elliptic Fourier descriptors and their principal components. The closed contour of each pod projection was extracted, and 80 elliptic Fourier coefficients were calculated for each contour. The Fourier coefficients were standardized so that they were invariant of size, rotation, shift, and chain code starting point. Then, the principal components on the standardized Fourier coefficients were evaluated. The cumulative contribution at the fifth principal component was higher than $95\%$, indicating that the first, second, third, fourth, and fifth principal components represented the aspect ratio of the pod, the location of the pod centroid, the sharpness of the two pod tips and the roundness of the base in the pod contour, respectively. Analysis of variance revealed significant genotypic differences in these principal components and seed number per pod. As the principal components for pod shape varied continuously, pod shape might be controlled by polygenes. It was concluded that principal component scores based on elliptic Fourier descriptors yield seemed to be useful in quantitative parameters not only for evaluating soybean pod shape in a soybean breeding program but also for describing pod shape for evaluating soybean germplasm.

• Publications of The Korean Astronomical Society
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• v.32 no.1
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• pp.209-211
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• 2017

We performed Principle Component Analysis (PCA) over 264 galaxies in the IRAS Revised Bright Galaxy Sample (Sanders et al., 2003) using 12, 25, 60 and $100{\mu}m$ flux data observed by IRAS and 9, 18, 65, 90 and $140{\mu}m$ flux data observed by AKARI. We found that (i)the first principle component was largely contributed by infrared to visible flux ratio, (ii)the second principal component was largely contributed by the flux ratio between IRAS and AKARI, (iii)the third principle component was largely contributed by infrared colors.

• Journal of the Korean Society of Industry Convergence
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• v.8 no.3
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• pp.143-148
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• 2005

This paper presents a hybrid method for recognizing the faces by using principal component analysis(PCA) and fixed-point independent component analysis(FP-ICA). PCA is used to whiten the data, which reduces the effects of second-order statistics to the nonlinearities. FP-ICA is applied to extract the statistically independent features of face image. The proposed method has been applied to the problems for recognizing the 20 face images(10 persons * 2 scenes) of 324*243 pixels from Yale face database. The 3 distances such as city-block, Euclidean, negative angle are used as measures when match the probe images to the nearest gallery images. The experimental results show that the proposed method has a superior recognition performances(speed, rate). The negative angle has been relatively achieved more an accurate similarity than city-block or Euclidean.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.05a
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• pp.1035-1036
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• 2010

• Proceedings of the Korea Society of Design Studies Conference
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• 2001.10a
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• pp.60.2-60
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• 2001