• Title, Summary, Keyword: principal component analysis (PCA)

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Comparison of Significant Term Extraction Based on the Number of Selected Principal Components (주성분 보유수에 따른 중요 용어 추출의 비교)

  • Lee Chang-Beom;Ock Cheol-Young;Park Hyuk-Ro
    • The KIPS Transactions:PartB
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    • v.13B no.3
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    • pp.329-336
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    • 2006
  • In this paper, we propose a method of significant term extraction within a document. The technique used is Principal Component Analysis(PCA) which is one of the multivariate analysis methods. PCA can sufficiently use term-term relationships within a document by term-term correlations. We use a correlation matrix instead of a covariance matrix between terms for performing PCA. We also try to find out thresholds of both the number of components to be selected and correlation coefficients between selected components and terms. The experimental results on 283 Korean newspaper articles show that the condition of the first six components with correlation coefficients of |0.4| is the best for extracting sentence based on the significant selected terms.

The Recognition of Korean Syllables using Parameter Based on Principal Component Analysis (PCA 기반 파라메타를 이용한 숫자음 인식)

  • 박경훈;표창수;김창근;허강인
    • Proceedings of the Korea Institute of Convergence Signal Processing
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    • pp.181-184
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    • 2000
  • The new method of feature extraction is proposed, considering the statistic feature of human voice, unlike the conventional methods of voice extraction. PCA(principal Component Analysis) is applied to this new method. PCA removes the repeating of data after finding the axis direction which has the greatest variance in input dimension. Then the new method is applied to real voice recognition to assess performance. When results of the number recognition in this paper and the conventional Mel-Cepstrum of voice feature parameter are compared, there is 0.5% difference of recognition rate. Better recognition rate is expected than word or sentence recognition in that less convergence time than the conventional method in extracting voice feature. Also, better recognition tate is expected when the optimum vector is used by statistic feature of data.

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Analysis of Functional Connectivity in Human Working Memory using Positron Emission Tomography and Principal Component Analysis

  • Lee, J.S.;Ahn, J.Y.;Jang, M.J.;Lee, D.S.;Chung, J.K.;Lee, M.C.;Park, K.S.
    • Proceedings of the KOSOMBE Conference
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    • v.1998 no.11
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    • pp.257-258
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    • 1998
  • To reveal the interconnected brain regions involved in human working memory, their functional connectivity was analyzed using principal component analysis (PCA). rCBF PET scans were peformed on 5 normal volunteers during the verbal and visual working memory tasks and PCA was applied. PCA produced the first principal components related with the increase of the difficulty and the second one which demonstrate the dissociation of verbal and visual memory system.

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Image Classification Using Grey Block Distance Algorithms for Principal Component Analysis and Kurtosis (주성분분석과 첨도에서의 그레이 블록 거리 알고리즘을 이용한 영상분류)

  • Hong, Jun-Sik
    • Proceedings of the Korea Information Processing Society Conference
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    • pp.779-782
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    • 2002
  • 본 논문에서는 주성분분석(principal component analysis; 이하 PCA) 및 첨도(Kurtosis)에서의 그레이 블록 거리 알고리즘(grey block algorithms; 이하 GBD)을 이용, 영상간의 거리를 측정하여 어느 정도 영상간의 상대적 식별을 용이하게 하여 영상 분류가 되는지 모의실험을 통하여 확인하고자 한다. 모의실험 결과로부터, PCA에서는 k가 9에서 상대적 식별이 불가능함을 보였고, 첨도에서는 k가 4까지만 블록을 택할 할 수 있음을 모의실험을 통하여 확인할 수 있었다.

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A novel method for predicting protein subcellular localization based on pseudo amino acid composition

  • Ma, Junwei;Gu, Hong
    • BMB Reports
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    • v.43 no.10
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    • pp.670-676
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    • 2010
  • In this paper, a novel approach, ELM-PCA, is introduced for the first time to predict protein subcellular localization. Firstly, Protein Samples are represented by the pseudo amino acid composition (PseAAC). Secondly, the principal component analysis (PCA) is employed to extract essential features. Finally, the Elman Recurrent Neural Network (RNN) is used as a classifier to identify the protein sequences. The results demonstrate that the proposed approach is effective and practical.

Robust Primary-ambient Signal Decomposition Method using Principal Component Analysis with Phase Alignment (위상 정렬을 이용한 주성분 분석법의 강인한 스테레오 음원 분리 성능유지 기법)

  • Baek, Yong-Hyun;Hyun, Dong-Il;Park, Young-Cheol
    • Journal of Broadcast Engineering
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    • v.19 no.1
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    • pp.64-74
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    • 2014
  • The primary and ambient signal decomposition of a stereo sound is a key step to the stereo upmix. The principal component analysis (PCA) is one of the most widely used methods of primary-ambient signal decomposition. However, previous PCA-based decomposition algorithms assume that stereo sound sources are only amplitude-panned without any consideration of phase difference. So it occurs some performance degradation in case of live recorded stereo sound. In this paper, we propose a new PCA-based stereo decomposition algorithm that can consider the phase difference between the channel signals. The proposed algorithm overcomes limitation of conventional signal model using PCA with phase alignment. The phase alignment is realized by using inter-channel phase difference (IPD) which is widely used in parametric stereo coding. Moreover, Enhanced Modified PCA(EMPCA) is combined to solve the problem of conventional PCA caused by Primary to Ambient energy Ratio(PAR) and panning angle dependency. The simulation results are presented to show the improvements of the proposed algorithm.

Principal Component Analysis Based Method for a Fault Diagnosis Model DAMADICS Process (주성분 분석을 이용한 DAMADICS 공정의 이상진단 모델 개발)

  • Park, Jae Yeon;Lee, Chang Jun
    • Journal of the Korean Society of Safety
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    • v.31 no.4
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    • pp.35-41
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    • 2016
  • In order to guarantee the process safety and prevent accidents, the deviations from normal operating conditions should be monitored and their root causes have to be identified as soon as possible. The statistical theories-based method among various fault diagnosis methods has been gaining popularity, due to simplicity and quickness. However, according to fault magnitudes, the scalar value generated by statistical methods can be changed and this point can lead to produce wrong information. To solve this difficulty, this work employs PCA (Principal Component Analysis) based method with qualitative information. In the case study of our previous study, the number of assumed faults is much smaller than that of process variables. In the case study of this study, the number of predefined faults is 19, while that of process variables is 6. It means that a fault diagnosis becomes more difficult and it is really hard to isolate a single fault with a small number of variables. The PCA model is constructed under normal operation data in order to get a loading vector and the data set of assumed faulty conditions is applied with PCA model. The significant changes on PC (Principal Components) axes are monitored with CUSUM (Cumulative Sum Control Chart) and recorded to make the information, which can be used to identify the types of fault.

PCA-based filtering of temperature effect on impedance monitoring in prestressed tendon anchorage

  • Huynh, Thanh-Canh;Dang, Ngoc-Loi;Kim, Jeong-Tae
    • Smart Structures and Systems
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    • v.22 no.1
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    • pp.57-70
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    • 2018
  • For the long-term structural health monitoring of civil structures, the effect of ambient temperature variation has been regarded as one of the critical issues. In this study, a principal component analysis (PCA)-based algorithm is proposed to filter out temperature effects on electromechanical impedance (EMI) monitoring of prestressed tendon anchorages. Firstly, the EMI monitoring via a piezoelectric interface device is described for prestress-loss detection in the tendon anchorage system. Secondly, the PCA-based temperature filtering algorithm tailored to the EMI monitoring of the prestressed tendon anchorage is outlined. The proposed algorithm utilizes the damage-sensitive features obtained from sub-ranges of the EMI data to establish the PCA-based filter model. Finally, the feasibility of the PCA-based algorithm is experimentally evaluated by distinguishing temperature changes from prestress-loss events in a prestressed concrete girder. The accuracy of the prestress-loss detection results is discussed with respect to the EMI features before and after the temperature filtering.

Identifying an Appropriate Analysis Duration for the Principal Component Analysis of Water Pipe Flow Data (상수도 관망 유량관측 자료의 주성분 분석을 위한 분석기간의 설정)

  • Park, Suwan;Jeon, Daehoon;Jung, Soyeon;Kim, Joohwan;Lee, Doojin
    • Journal of Korean Society of Water and Wastewater
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    • v.27 no.3
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    • pp.351-361
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    • 2013
  • In this study the Principal Component Analysis (PCA) was applied to flow data in a water distribution pipe system to analyze the relevance between the flow observation dates, which have the outliers of observed night flows, and the maintenance records. The data was obtained from four small size water distribution blocks to which 13 maintenance records such as pipe leak and water meter leak belong. The flow data during four months were used for the analysis. The analysis was carried out to identify an appropriate analysis period for a PCA model for a water distribution block. To facilitate the analyses a computational algorithm was developed. MATLAB was utilized to realize the algorithm as a computer program. As a result, an appropriate PCA period for each of the case study small size water distribution blocks was identified.

Principal Component Analysis with Coefficient of Variation Matrix (변동계수행렬을 이용한 주성분분석)

  • Kim, Ji-Hyun
    • The Korean Journal of Applied Statistics
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    • v.28 no.3
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    • pp.385-392
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    • 2015
  • Principal component analysis (PCA), a dimension-reduction technique, is usually implemented after the variables are standardized when the measurement unit of variables are different. To standardize a variable we divide it by its standard deviation. But there is another way to transform a variable to be independent of its measurement unit. It is to divide it by its mean rather than standard deviation. Implementing PCA on standardized variables is equivalent to implementing PCA with a correlation matrix of original variables. Similarly, implementing PCA on the transformed variables divided by their means is equivalent to implementing PCA with a matrix related to the coefficients of variation of the original variables. We explain why we need to implement PCA on the variables transformed by their means.