• Title/Summary/Keyword: Principal Component Analysis

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Blind Source Separation via Principal Component Analysis

  • Choi, Seung-Jin
    • Journal of KIEE
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    • v.11 no.1
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    • pp.1-7
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    • 2001
  • Various methods for blind source separation (BSS) are based on independent component analysis (ICA) which can be viewed as a nonlinear extension of principal component analysis (PCA). Most existing ICA methods require certain nonlinear functions (which leads to higher-order statistics) depending on the probability distributions of sources, whereas PCA is a linear learning method based on second-order statistics. In this paper we show that the PCA can be applied to the task of BBS, provided that source are spatially uncorrelated but temporally correlated. Since the resulting method is based on only second-order statistics, it avoids the nonlinear function and is able to separate mixtures of several colored Gaussian sources, in contrast to the conventional ICA methods.

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Analysis of Chinese Provinces for Introduction of Reverse Mortgage Scheme Using Principal Component Analysis (주성분분석을 활용한 중국 행정구역별 역모기지 도입 순위 분석)

  • Wang, Ping;Kim, Jipyo
    • Journal of Korean Institute of Industrial Engineers
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    • v.40 no.2
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    • pp.205-214
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    • 2014
  • As a result of the rapid economic growth and birth control policy, China is experiencing low fertility rates and increasing life expectancy, which makes Chinese population aging very quickly and unprepared for their retired life. The reverse mortgage may be an attractive option for the elderly because it is a loan against a house that they do not have to pay back as long as they live there. In this paper, in order to introduce the reverse mortgage scheme in China the factors that could influence the demand of reverse mortgage are reviewed and the Chinese market environment is analyzed. Then the principal component analysis is performed in order to recommend the regions or cities that have higher potential for successful implementation of a reverse mortgage than any other ones in China.

Principal Component Analysis Based Method for Effective Fault Diagnosis (주성분 분석을 이용한 효과적인 화학공정의 이상진단 모델 개발)

  • Park, Jae Yeon;Lee, Chang Jun
    • Journal of the Korean Society of Safety
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    • v.29 no.4
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    • pp.73-77
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    • 2014
  • In the field of fault diagnosis, the deviations from normal operating conditions are monitored to identify the type of faults and find their root causes. One of the most representative methods is the statistical approaches, due to a large amount of advantages. However, ambiguous diagnosis results can be generated according to fault magnitudes, even if the same fault occurs. To tackle this issue, this work proposes principal component analysis (PCA) based method with qualitative information. The PCA model is constructed under normal operation data and the residuals from faulty conditions are calculated. The significant changes of these residuals are recorded to make the information for identifying the types of fault. This model can be employed easily and the tasks for building are smaller than these of other common approaches. The efficacy of the proposed model is illustrated in Tennessee Eastman process.

A Fuzzy Neural Network Combining Wavelet Denoising and PCA for Sensor Signal Estimation

  • Na, Man-Gyun
    • Nuclear Engineering and Technology
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    • v.32 no.5
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    • pp.485-494
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    • 2000
  • In this work, a fuzzy neural network is used to estimate the relevant sensor signal using other sensor signals. Noise components in input signals into the fuzzy neural network are removed through the wavelet denoising technique . Principal component analysis (PCA) is used to reduce the dimension of an input space without losing a significant amount of information. A lower dimensional input space will also usually reduce the time necessary to train a fuzzy-neural network. Also, the principal component analysis makes easy the selection of the input signals into the fuzzy neural network. The fuzzy neural network parameters are optimized by two learning methods. A genetic algorithm is used to optimize the antecedent parameters of the fuzzy neural network and a least-squares algorithm is used to solve the consequent parameters. The proposed algorithm was verified through the application to the pressurizer water level and the hot-leg flowrate measurements in pressurized water reactors.

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Joint Exponential Smoothing and Trend-based Principal Component Analysis for Anomaly Detection in Wireless Sensor Networks (무선 센서 네트워크에서의 이상 징후 감지를 위한 공동 지수 평활법 및 추세 기반 주성분 분석)

  • Dang, Thien-Binh;Yang, Hui-Gyu;Tran, Manh-Hung;Le, Duc-Tai;Kim, Moonseong;Choo, Hyunseung
    • Proceedings of the Korea Information Processing Society Conference
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    • pp.145-148
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    • 2019
  • Principal Component Analysis (PCA) is a powerful technique in data analysis and widely used to detect anomalies in Wireless Sensor Networks. However, the performance of conventional PCA is not high on time-series data collected by sensors. In this paper, we propose a Joint Exponential Smoothing and Trend-based Principal Component Analysis (JES-TBPCA) for Anomaly Detection which is based on conventional PCA. Experimental results on a real dataset show a remarkably higher performance of JES-TBPCA comparing to conventional PCA model in detection of stuck-at and offset anomalies.

Regional Geological Mapping by Principal Component Analysis of the Landsat TM Data in a Heavily Vegetated Area (식생이 무성한 지역에서의 Principal Component Analysis 에 의한 Landsat TM 자료의 광역지질도 작성)

  • 朴鍾南;徐延熙
    • Korean Journal of Remote Sensing
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    • v.4 no.1
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    • pp.49-60
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    • 1988
  • Principal Component Analysis (PCA) was applied for regional geological mapping to a multivariate data set of the Landsat TM data in the heavily vegetated and topographically rugged Chungju area. The multivariate data set selection was made by statistical analysis based on the magnitude of regression of squares in multiple regression, and it includes R1/2/R3/4, R2/3, R5/7/R4/3, R1/2, R3/4. R4/3. AND R4/5. As a result of application of PCA, some of later principal components (in this study PC 3 and PC 5) are geologically more significant than earlier major components, PC 1 and PC 2 herein. The earlier two major components which comprise 96% of the total information of the data set, mainly represent reflectance of vegetation and topographic effects, while though the rest represent 3% of the total information which statistically indicates the information unstable, geological significance of PC3 and PC5 in the study implies that application of the technique in more favorable areas should lead to much better results.

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Structural Health Monitoring of Aircraft Reciprocating Engine Based on Principal Component Analysis (PCA) (주성분 분석(PCA)에 의한 항공기 왕복 엔진의 구조 건전도 모니터링)

  • Kim, Ji-Hwan;Park, Seong-Eun;Lee, Hyeong-Cheol
    • Journal of Aerospace System Engineering
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    • v.6 no.1
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    • pp.13-18
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    • 2012
  • This paper presents a structural health monitoring method of aircraft reciprocating engine using Principal Component Analysis (PCA) which analyzes vibration expressed by Averaged Normalized Power Spectral Density (ANPSD). Because ANPSD of the rotating shaft is sensitive to the rotating speed, this paper proposes to use a post-processing method of ANPSD is used to reduce the sensitivity. The PCA extracts compressed information from the post-processed ANPSDs and the information means the difference between current and normal cases of the engine. The experimental results demonstrate the feasibility and effectiveness of the proposed method to detect abnormal cases of the engine.

Classification for intraclass correlation pattern by principal component analysis

  • Chung, Hie-Choon;Han, Chien-Pai
    • Journal of the Korean Data and Information Science Society
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    • v.21 no.3
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    • pp.589-595
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    • 2010
  • In discriminant analysis, we consider an intraclass correlation pattern by principal component analysis. We assume that the two populations are equally likely and the costs of misclassification are equal. In this situation, we consider two procedures, i.e., the test and proportion procedures, for selecting the principal components in classifica-tion. We compare the regular classification method and the proposed two procedures. We consider two methods for estimating error rate, i.e., the leave-one-out method and the bootstrap method.

A Study on Classification of Micro-Cracks in Silicon Wafer Through the Fusion of Principal Component Analysis and Neural Network (주성분분석과 신경회로망의 융합을 통한 실리콘 웨이퍼의 마이크로 크랙 분류에 관한 연구)

  • Seo, Hyoung Jun;Kim, Gyung Bum
    • Journal of the Korean Society for Precision Engineering
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    • v.32 no.5
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    • pp.463-470
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    • 2015
  • Solar cell is typical representative of renewable green energy. Silicon wafer contributes about 66 percent to its cost structure. In its manufacturing, micro-cracks are often occurred due to manufacturing process such as wire sawing, grinding and cleaning. Their detection and classification are important to process feedback information. In this paper, a classification method of micro-cracks is proposed, based on the fusion of principal component analysis(PCA) and neural network. The proposed method shows that it gives higher results than single application of two methods, in terms of shape and size classification of micro-cracks.

LMS and LTS-type Alternatives to Classical Principal Component Analysis

  • Huh, Myung-Hoe;Lee, Yong-Goo
    • Communications for Statistical Applications and Methods
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    • v.13 no.2
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    • pp.233-241
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    • 2006
  • Classical principal component analysis (PCA) can be formulated as finding the linear subspace that best accommodates multidimensional data points in the sense that the sum of squared residual distances is minimized. As alternatives to such LS (least squares) fitting approach, we produce LMS (least median of squares) and LTS (least trimmed squares)-type PCA by minimizing the median of squared residual distances and the trimmed sum of squares, in a similar fashion to Rousseeuw (1984)'s alternative approaches to LS linear regression. Proposed methods adopt the data-driven optimization algorithm of Croux and Ruiz-Gazen (1996, 2005) that is conceptually simple and computationally practical. Numerical examples are given.