• Title/Summary/Keyword: Principal component Analysis

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Pattern Classification of PM -10 in the Indoor Environment Using Disjoint Principal Component Analysis (분산주성분 분석을 이용한 실내환경 중 PM-10 오염의 패턴분류)

  • 남보현;황인조;김동술
    • Journal of Korean Society for Atmospheric Environment
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    • v.18 no.1
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    • pp.25-37
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    • 2002
  • The purpose of the study was to survey the distribution patterns of inorganic elements of PM-10 in the various indoor environments and analyze the pollution patterns of aerosol in various places of indoor environment using a pattern recognition method based on cluster analysis and disjoint principal component analysis. A total of 40 samples in the indoor had been collected using mini-vol portable samplers. These samples were analyzed for their 19 bulk inorganic compounds such as B, Na, Mg, Al, K, Ca, Ti, V, Cr, Fe, Ni, Cu, Zn, As, Se, Cd, Ba, Ce, and Pb by using an ICP-MS. By applying a disjoint principal component analysis, four patterns of the indoor air pollutions were distinguished. The first pattern was identified as a group with high concentrations of PM-10, Na, Mg, and Ca. The second pattern was identified as a group with high concentrations B, Mg, At, Ca, Fe, Cu, and Ba. The third pattern was a group of sites with high concentrations of K, Zn. Cd. The fourth pattern was a group with low concentrations PM-10 and all inorganic elements. This methodology was found to be helpful enough to set the criteria standard of indoor air quality, corresponding pollutants, and classification of indoor environment categories when making an indoor air quality law.

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.

Analysis for Soil Pollution by Heavy Metals in the Area of Kyongbuk (경북지역 토양의 중금속 분석)

  • Dho, Hyon-Seung;Kim, Sung-Duk;Lee, Seung-Joo
    • Journal of the Korea Safety Management & Science
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    • v.12 no.2
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    • pp.231-236
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    • 2010
  • The investigation was initiated with data from 27 abandoned mines along with 12 locations in Kyongbuk abandoned mines. The analyses for soil pollution by heavy metal pollutants were conducted by using correlation analysis, cluster analysis, and principal component analysis. The correlation analysis indicated that Ni and pH were highly correlated compared to those of other heavy metal ions. The principal component analyses showed that the heavy metal ions might be classified into two catagories, such as antropogenic and lithogenic components. The cluster analysis was also clearly divided by two groups. The respective two groups might be Pb-Zn-Cd-Cu and As-Hg-Ni.

Independent Component Analysis of Nino3.4 Sea Surface Temperature and Summer Seasonal Rainfall (Nino3.4지역 SST 및 여름강수량의 독립성분분석)

  • Kwon Hyun-Han;Moon Young-Il
    • Journal of Korea Water Resources Association
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    • v.38 no.12 s.161
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    • pp.985-994
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    • 2005
  • We examined problems of the principal component analysis(PCA), which is able to analyze at the low dimensionality as a methodologv to assess hydrologic time series, and introduced the theory and characteristics of independent component analysis(ICA) that can supplement problems of principal component analysis. We also applied the global sea surface temperature(SST) of the Nino region and assessed the correlation between El $\tilde{n}ino$-Southern Oscillation(ENSO) and SST. The results of examining separation-ability of principal components using mixed signals indicate that the independent component analysis is statistically superior compared to that of the principal component analysis. Finally, we assessed correlation between ENSO and global anomaly SST. The independent component analysis was applied to the $5^{\circ}{\times}5^{\circ}$(latitude and longitude) global anomaly SST in the Nino+3.4 region that is the El $\tilde{n}ino$ observation section. We assessed the correlation with the ENSO years. These results of the analysis show that only one independent component($86\%$) was able to represent the entire behavior and was consistent with the main ENSO years. Finally, we carried out independent component analysis for summer seasonal rainfalls at nine stations and could extract ICs to reflect geographical characteristics. The increasing trend has been shown at IC-1 and IC-2 since 1970s.

Principal Component Analysis of Compositional Data using Box-Cox Contrast Transformation (Box-Cox 대비변환을 이용한 구성비율자료의 주성분분석)

  • 최병진;김기영
    • The Korean Journal of Applied Statistics
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    • v.14 no.1
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    • pp.137-148
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    • 2001
  • Compositional data found in many practical applications consist of non-negative vectors of proportions with the constraint which the sum of the elements of each vector is unity. It is well-known that the statistical analysis of compositional data suffers from the unit-sum constraint. Moreover, the non-linear pattern frequently displayed by the data does not facilitate the application of the linear multivariate techniques such as principal component analysis. In this paper we develop new type of principal component analysis for compositional data using Box-Cox contrast transformation. Numerical illustrations are provided for comparative purpose.

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Analyses of Power Consumption of the Heat Pump Dryer in the Automobile Drying Process by using the Principal Component Analysis and Multiple Regression (주성분 분석과 다중회귀모형을 사용한 자동차 건조 공정의 히트펌프 건조기 소모 전력 분석)

  • Lee, Chang-Yong;Song, Gensoo;Kim, Jinho
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.38 no.1
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    • pp.143-151
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    • 2015
  • In this paper, we investigate how the power consumption of a heat pump dryer depends on various factors in the drying process by analyzing variables that affect the power consumption. Since there are in general many variables that affect the power consumption, for a feasible analysis, we utilize the principal component analysis to reduce the number of variables (or dimensionality) to two or three. We find that the first component is correlated positively to the entrance temperature of various devices such as compressor, expander, evaporator, and the second, negatively to condenser. We then model the power consumption as a multiple regression with two and/or three transformed variables of the selected principal components. We find that fitted value from the multiple regression explains 80~90% of the observed value of the power consumption. This results can be applied to a more elaborate control of the power consumption in the heat pump dryer.

A study on principal component analysis using penalty method (페널티 방법을 이용한 주성분분석 연구)

  • Park, Cheolyong
    • Journal of the Korean Data and Information Science Society
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    • v.28 no.4
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    • pp.721-731
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    • 2017
  • In this study, principal component analysis methods using Lasso penalty are introduced. There are two popular methods that apply Lasso penalty to principal component analysis. The first method is to find an optimal vector of linear combination as the regression coefficient vector of regressing for each principal component on the original data matrix with Lasso penalty (elastic net penalty in general). The second method is to find an optimal vector of linear combination by minimizing the residual matrix obtained from approximating the original matrix by the singular value decomposition with Lasso penalty. In this study, we have reviewed two methods of principal components using Lasso penalty in detail, and shown that these methods have an advantage especially in applying to data sets that have more variables than cases. Also, these methods are compared in an application to a real data set using R program. More specifically, these methods are applied to the crime data in Ahamad (1967), which has more variables than cases.

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.

Identification the Key Odorants in Different Parts of Hyla Rabbit Meat via Solid Phase Microextraction Using Gas Chromatography Mass Spectrometry

  • Xie, Yuejie;He, Zhifei;Lv, Jingzhi;Zhang, En;Li, Hongjun
    • Food Science of Animal Resources
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    • v.36 no.6
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    • pp.719-728
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    • 2016
  • The aim of this study was to explore the volatile compounds of hind leg, foreleg, abdomen and Longissimus dorsi in both male and female Hyla rabbit meat by solid phase microextraction tandem with gas chromatography mass spectrometry, and to seek out the key odorants via calculating the odor activity value and principal component analysis. Cluster analysis is used to study the flavor pattern differences in four edible parts. Sixty three volatile compounds were detected, including 23 aldehydes, 4 alcohols, 5 ketones, 11 esters, 5 aromatics, 8 acids and 7 hydrocarbons. Among them, 6 aldehydes and 3 acids were identified as the potential key odorants according to the ratio of concentration and threshold. The contents of volatile compounds in male Hyla rabbit meat were significantly higher than those in female one (p<0.05). The results of principal component analysis showed that the first two principal component cumulative variance contributions reach 87.69%; Hexanal, octanal, 2-nonenal, 2-decenal and decanal were regard as the key odorants of Hyla rabbit meat by combining odor activity value and principal component analysis. Therefore volatile compounds of rabbit meat can be effectively characterized. Cluster analysis indicated that volatile chemical compounds of Longissimus dorsi were significantly different from other three parts, which provide reliable information for rabbit processing industry and for possible future sale.