• 제목/요약/키워드: Principal Component Analysis

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Visible and NIR Image Synthesis Using Laplacian Pyramid and Principal Component Analysis (라플라시안 피라미드와 주성분 분석을 이용한 가시광과 적외선 영상 합성)

  • Son, Dong-Min;Kwon, Hyuk-Ju;Lee, Sung-Hak
    • Journal of Sensor Science and Technology
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    • v.29 no.2
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    • pp.133-140
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    • 2020
  • This study proposes a method of blending visible and near infrared images to enhance edge details and local contrast. The proposed method consists of radiance map generation and color compensation. The radiance map is produced by a Laplacian pyramid and a soft mixing method based on principal component analysis. The color compensation method uses the ratio between the composed radiance map and the luminance channel of a visible image to preserve the visible image chrominance. The proposed method has better edge details compared to a conventional visible and NIR image blending method.

Principal component analysis for Hilbertian functional data

  • Kim, Dongwoo;Lee, Young Kyung;Park, Byeong U.
    • 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.

Comparison of hydrochemical informations of groundwater obtained from two different underground storage systems

  • Lee, Jeonghoon;Kim, Jun-Mo;Chang, Ho-Wan
    • Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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    • pp.110-113
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    • 2002
  • Statistical- based, principal component analysis (PCA) was applied to chemical data from two underground storage systems containing LPG to assess the usefulness of such technique at the initial stage (Pyeongtaek) or middle stage (Ulsan) of hydrochemical studies. For the first case, both natural and anthropogenic contamination characterize regional groundwater. Saline water buffered by Namyang lake affects as a natural factor, whereas cement grouting influence as an artificial factor. For the second study area, contaminations due to operation of LPG caverns, such as disinfection activity and cement grouting effect, deteriorate groundwater quality. This study indicates that principal component analysis would be particularly useful for summarizing large data set for the purpose of subsurface characterization, assessing their vulnerability to contamination and protecting recharge zones.

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Evaluation of Slope Condition using Principal Component Analysis (주성분분석법을 이용한 사면 상태 평가)

  • Jung, Soo-Jung;Kim, Tae-Hyung;Kang, Ki-Min;Lee, Young-Jun
    • Proceedings of the Korean Geotechical Society Conference
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    • pp.416-422
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    • 2010
  • Estimating condition of geotechnical structures are difficult because of nonlinear time dependency and seasonal effects. Measuring data of structure failure is highly variable in time and space, and a unique approach cannot be defined to model structure movements. Characteristics of movements are obtained by using a statistical method called Principal Component Analysis(PCA). The PCA is a non-parametric method to separate unknown, statistically uncorrelated source processes from observed mixed processes. Instead, since the "best" mathematical relationship is estimated for given data sets of the input and output measured from target systems. As a consequence, this method is advantageous in modeling systems whose geomechanical properties are unknown or difficult to be measured.

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Joint Channel Coding Based on Principal Component Analysis

  • Hyun, Dong-Il;Lee, Dong-Geum;Park, Young-Cheol;Youn, Dae-Hee;Seo, Jeong-Il
    • ETRI Journal
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    • v.32 no.5
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    • pp.831-834
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    • 2010
  • This paper proposes a new joint channel coding algorithm based on principal component analysis. A conventional joint channel coder using passive downmixing undergoes a reduction of both the primary-to-ambient energy ratio (PAR) of the downmix signal and the panning gain ratio of the primary source. The proposed system preserves the PAR of the downmix signal by using active downmixing which reflects spatial characteristic. The proposed system also improves the accuracy of the panning gain ratio estimation. Computer simulations and subjective listening tests verify the performance of the proposed system.

Real-Time Small Exposed Area $SiO_2$ Films Thickness Monitoring in Plasma Etching Using Plasma Impedance Monitoring with Modified Principal Component Analysis

  • Jang, Hae-Gyu;Nam, Jae-Uk;Chae, Hui-Yeop
    • Proceedings of the Korean Vacuum Society Conference
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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.

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Principal Component Analysis Based Two-Dimensional (PCA-2D) Correlation Spectroscopy: PCA Denoising for 2D Correlation Spectroscopy

  • Jung, Young-Mee
    • Bulletin of the Korean Chemical Society
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    • v.24 no.9
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    • pp.1345-1350
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    • 2003
  • Principal component analysis based two-dimensional (PCA-2D) correlation analysis is applied to FTIR spectra of polystyrene/methyl ethyl ketone/toluene solution mixture during the solvent evaporation. Substantial amount of artificial noise were added to the experimental data to demonstrate the practical noise-suppressing benefit of PCA-2D technique. 2D correlation analysis of the reconstructed data matrix from PCA loading vectors and scores successfully extracted only the most important features of synchronicity and asynchronicity without interference from noise or insignificant minor components. 2D correlation spectra constructed with only one principal component yield strictly synchronous response with no discernible a asynchronous features, while those involving at least two or more principal components generated meaningful asynchronous 2D correlation spectra. Deliberate manipulation of the rank of the reconstructed data matrix, by choosing the appropriate number and type of PCs, yields potentially more refined 2D correlation spectra.

A Numerical Taxonomic Study of Calystegia in Korea by the Cluster Analysis and Principal Component Analysis (류집분석과 주성분분석에 의한 한국산 메꽃과의 수량분류학적 연구)

  • Kim, Yun Shik
    • 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.

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A Study on Evaluation of the Characteristics Value in Principal Component Analysis (주성분분석에 의한 특성치평가에 관한 연구 - 신체검사의 예를 중심으로 -)

  • 최진영;정관희
    • Journal of the Society of Korea Industrial and Systems Engineering
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    • v.3 no.3
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    • pp.23-34
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    • 1980
  • The method of principal component analysis is originated by K. Pearson, who considered this as geometrical method Principal component analysis is the most elementary method, and this means that the information having various type of characteristics which have been correlated among themselves, are summarized by orthogonal transformations of characteristics. I: Even though we have different result whether this method is applied to homogeneous population or not. In this research we should deal with the case of homogeneous population only. II: On the other hand, we can have different result whether we start from covariance matrix or matrix of correlation- coefficients. In this research we are studying based on covariance matrix.

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Evaluation of Water Quality using Principal Component Analysis in the Nakdong Rivev Estuary (주성분 분석법을 이용한 낙동강 하구 해역의 수질 평가)

  • Sin, Seong-Gyo;Park, Cheong-Gil;Song, Gyo-Uk
    • Journal of Environmental Science International
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    • v.7 no.2
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    • pp.171-176
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    • 1998
  • This study was conducted to evaluate water quality utilizing principal component analysis in the Nakdong River Estuary. From the results of analysis, water quality in the Nakdong River Estuary could be explained up to 65.3 Percente by three factors which were Included In river loadlnwastes from the Nakdong River and rainfalls : 39.1%1, sediment resuspension(13.7BS) and metabolism(12.5%). In the eastern part of estuary In flowing the Nakdong River, river loading factor score(factor 1 Pas higher than that In western part. Sediment resuspension factor score(factor 2) was high in shallow water, while metabolism factor score(factor 3) was high in deeper water. For seasonal variations of factors score, factor 1 was h19h- 1y related to rainfall season.

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