• Title/Summary/Keyword: Principal Component Analysis

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An eigenspace projection clustering method for structural damage detection

  • Zhu, Jun-Hua;Yu, Ling;Yu, Li-Li
    • Structural Engineering and Mechanics
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    • v.44 no.2
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    • pp.179-196
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    • 2012
  • An eigenspace projection clustering method is proposed for structural damage detection by combining projection algorithm and fuzzy clustering technique. The integrated procedure includes data selection, data normalization, projection, damage feature extraction, and clustering algorithm to structural damage assessment. The frequency response functions (FRFs) of the healthy and the damaged structure are used as initial data, median values of the projections are considered as damage features, and the fuzzy c-means (FCM) algorithm are used to categorize these features. The performance of the proposed method has been validated using a three-story frame structure built and tested by Los Alamos National Laboratory, USA. Two projection algorithms, namely principal component analysis (PCA) and kernel principal component analysis (KPCA), are compared for better extraction of damage features, further six kinds of distances adopted in FCM process are studied and discussed. The illustrated results reveal that the distance selection depends on the distribution of features. For the optimal choice of projections, it is recommended that the Cosine distance is used for the PCA while the Seuclidean distance and the Cityblock distance suitably used for the KPCA. The PCA method is recommended when a large amount of data need to be processed due to its higher correct decisions and less computational costs.

Principal component regression for spatial data (공간자료 주성분분석)

  • Lim, Yaeji
    • The Korean Journal of Applied Statistics
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    • v.30 no.3
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    • pp.311-321
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    • 2017
  • Principal component analysis is a popular statistical method to reduce the dimension of the high dimensional climate data and to extract meaningful climate patterns. Based on the principal component analysis, we can further apply a regression approach for the linear prediction of future climate, termed as principal component regression (PCR). In this paper, we develop a new PCR method based on the regularized principal component analysis for spatial data proposed by Wang and Huang (2016) to account spatial feature of the climate data. We apply the proposed method to temperature prediction in the East Asia region and compare the result with conventional PCR results.

Eye detection on Rotated face using Principal Component Analysis (주성분 분석을 이용한 기울어진 얼굴에서의 눈동자 검출)

  • Choi, Yeon-Seok;Mun, Won-Ho;Cha, Eui-Young
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • pp.61-64
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    • 2011
  • There are many applications that require robust and accurate eye tracking, such as human-computer interface(HCI). In this paper, a novel approach for eye tracking with a principal component analysis on rotated face. In the process of iris detection, intensity information is used. First, for select eye region using principal component analysis. Finally, for eye detection using eye region's intensity. The experimental results show good performance in detecting eye from FERET image include rotate face.

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Simple principal component analysis using Lasso (라소를 이용한 간편한 주성분분석)

  • Park, Cheolyong
    • Journal of the Korean Data and Information Science Society
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    • v.24 no.3
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    • pp.533-541
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    • 2013
  • In this study, a simple principal component analysis using Lasso is proposed. This method consists of two steps. The first step is to compute principal components by the principal component analysis. The second step is to regress each principal component on the original data matrix by Lasso regression method. Each of new principal components is computed as the linear combination of original data matrix using the scaled estimated Lasso regression coefficient as the coefficients of the combination. This method leads to easily interpretable principal components with more 0 coefficients by the properties of Lasso regression models. This is because the estimator of the regression of each principal component on the original data matrix is the corresponding eigenvector. This method is applied to real and simulated data sets with the help of an R package for Lasso regression and its usefulness is demonstrated.

Nonlinear Feature Extraction using Class-augmented Kernel PCA (클래스가 부가된 커널 주성분분석을 이용한 비선형 특징추출)

  • Park, Myoung-Soo;Oh, Sang-Rok
    • Journal of the Institute of Electronics Engineers of Korea SC
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    • v.48 no.5
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    • pp.7-12
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    • 2011
  • In this papwer, we propose a new feature extraction method, named as Class-augmented Kernel Principal Component Analysis (CA-KPCA), which can extract nonlinear features for classification. Among the subspace method that was being widely used for feature extraction, Class-augmented Principal Component Analysis (CA-PCA) is a recently one that can extract features for a accurate classification without computational difficulties of other methods such as Linear Discriminant Analysis (LDA). However, the features extracted by CA-PCA is still restricted to be in a linear subspace of the original data space, which limites the use of this method for various problems requiring nonlinear features. To resolve this limitation, we apply a kernel trick to develop a new version of CA-PCA to extract nonlinear features, and evaluate its performance by experiments using data sets in the UCI Machine Learning Repository.

Classification of International Container Ports by Using Principal Component Analysis and Cluster Analysis (주성분분석 및 군집분석을 이용한 컨테이너항만의 분류)

  • 문성혁;이준구
    • Journal of Korean Port Research
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    • v.13 no.1
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    • pp.11-26
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    • 1999
  • The subject of port efficiency is one of the important issues facing port authorities and policy makers today. A number of studies have been undertaken which compare ports in terms of their efficiency. But any port comparison can only be valid and meaningful if a port’s efficiency is compared with a similar port. The main objective of this paper is to introduce a systematic approach to identifying similar ports based on the technique of principal component analysis and cluster analysis. And it seeks to identify the most important factors underlying the port classification. Lack of awareness of which factors differentiate ports has resulted in an unnecessary collection of data which are of limited use in port classification. This paper has identified five groupings of similar ports within which port comparision can be justifiably made. This approach can be used for any future port comparision.

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Performance Improvement of Polynomial Adaline by Using Dimension Reduction of Independent Variables (독립변수의 차원감소에 의한 Polynomial Adaline의 성능개선)

  • Cho, Yong-Hyun
    • Journal of the Korean Society of Industry Convergence
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    • v.5 no.1
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    • pp.33-38
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    • 2002
  • This paper proposes an efficient method for improving the performance of polynomial adaline using the dimension reduction of independent variables. The adaptive principal component analysis is applied for reducing the dimension by extracting efficiently the features of the given independent variables. It can be solved the problems due to high dimensional input data in the polynomial adaline that the principal component analysis converts input data into set of statistically independent features. The proposed polynomial adaline has been applied to classify the patterns. The simulation results shows that the proposed polynomial adaline has better performances of the classification for test patterns, in comparison with those using the conventional polynomial adaline. Also, it is affected less by the scope of the smoothing factor.

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In-situ Endpoint Detection for Dielectric Films Plasma Etching Using Plasma Impedance Monitoring and Self-plasma Optical Emission Spectroscopy with Modified Principal Component Analysis

  • Jang, Hae-Gyu;Chae, Hui-Yeop
    • Proceedings of the Korean Vacuum Society Conference
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    • pp.153-153
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    • 2012
  • Endpoint detection with plasma impedance monitoring and self-plasma optical emission spectroscopy is demonstrated for dielectric layers etching processes. For in-situ detecting endpoint, optical-emission spectroscopy (OES) is used for in-situ endpoint detection for plasma etching. However, the sensitivity of OES is decreased if polymer is deposited on viewport or the proportion of exposed area on the wafer is too small. To overcome these problems, the endpoint was determined by impedance signal variation from I-V monitoring (VI probe) and self-plasma optical emission spectroscopy. In addition, modified principal component analysis was applied to enhance sensitivity for small area etching. As a result, the sensitivity of this method is increased about twice better than that of OES. From plasma impedance monitoring and self-plasma optical emission spectroscopy, properties of plasma and chamber are analyzed, and real-time endpoint detection is achieved.

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Telephone Speech Recognition with Data-Driven Selective Temporal Filtering based on Principal Component Analysis

  • Jung Sun Gyun;Son Jong Mok;Bae Keun Sung
    • Proceedings of the IEEK Conference
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    • pp.764-767
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    • 2004
  • The performance of a speech recognition system is generally degraded in telephone environment because of distortions caused by background noise and various channel characteristics. In this paper, data-driven temporal filters are investigated to improve the performance of a specific recognition task such as telephone speech. Three different temporal filtering methods are presented with recognition results for Korean connected-digit telephone speech. Filter coefficients are derived from the cepstral domain feature vectors using the principal component analysis.

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Magnetocardiogram Topography with Automatic Artifact Correction using Principal Component Analysis and Artificial Neural Network

  • Ahn C.B.;Kim T.H.;Park H.C.;Oh S.J.
    • 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.