• Title, Summary, Keyword: Component analysis

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Component Identification using Domain Analysis based on Clustering (클러스터링에 기반 도메인 분석을 통한 컴포넌트 식별)

  • Haeng-Kon Kim;Jeon-Geun Kang
    • Journal of the Korea Computer Industry Society
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    • v.4 no.4
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    • pp.479-490
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    • 2003
  • CBD is a software development approach based on reusable component and supports easy modification and evolution of software. For the success of this approach, a component must be developed with high cohesion and low coupling. In this paper, we propose the two types of clustering analysis technique based on affinity between use-cases and classes and propose component identification method applying to this technique. We also propose component reference model and CBD methodology framework and perform a ease study to demonstrate how the affinity-based clustering technique is used in component identification method. Component identification method contains three tasks such as component extraction, component specification and component architecting. This method uses object-oriented concept for identifying component, which improves traceability from analysis to implementation and can automatically extract component. This method reflects the low coupling-high cohesion principle for good modularization about reusable component.

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A Study of Automatic Medical Image Segmentation using Independent Component Analysis (Independent Component Analysis를 이용한 의료영상의 자동 분할에 관한 연구)

  • Bae, Soo-Hyun;Yoo, Sun-Kook;Kim, Nam-Hyun
    • The Transactions of the Korean Institute of Electrical Engineers D
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    • v.52 no.1
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    • pp.64-75
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    • 2003
  • Medical image segmentation is the process by which an original image is partitioned into some homogeneous regions like bones, soft tissues, etc. This study demonstrates an automatic medical image segmentation technique based on independent component analysis. Independent component analysis is a generalization of principal component analysis which encodes the higher-order dependencies in the input in addition to the correlations. It extracts statistically independent components from input data. Use of automatic medical image segmentation technique using independent component analysis under the assumption that medical image consists of some statistically independent parts leads to a method that allows for more accurate segmentation of bones from CT data. The result of automatic segmentation using independent component analysis with square test data was evaluated using probability of error(PE) and ultimate measurement accuracy(UMA) value. It was also compared to a general segmentation method using threshold based on sensitivity(True Positive Rate), specificity(False Positive Rate) and mislabelling rate. The evaluation result was done statistical Paired-t test. Most of the results show that the automatic segmentation using independent component analysis has better result than general segmentation using threshold.

System model reduction by weighted component cost analysis

  • Kim, Jae-Hoon;Skelton, Robert-E.
    • 제어로봇시스템학회:학술대회논문집
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    • pp.524-529
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    • 1993
  • Component Cost Analysis considers any given system driven by a white noise process as an interconnection of different components, and assigns a metric called "component cost" to each component. These component costs measure the contribution of each component to a predefined quadratic cost function. One possible use of component costs is for model reduction by deleting those components that have the smallest component cost. The theory of Component Cost Analysis is extended to include finite-bandwidth colored noises. The results also apply when actuators have dynamics of their own. When the dynamics of this input are added to the plant, which is to be reduced by CCA, the algorithm for model reduction process will be called Weighted Component Cost Analysis (WCCA). Closed-form analytical expressions of component costs for continuous time case, are also derived for a mechanical system described by its modal data. This is very useful to compute the modal costs of very high order systems beyond Lyapunov solvable dimension. A numerical example for NASA's MINIMAST system is presented.presented.

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Risk Evaluation of Slope Using Principal Component Analysis (PCA) (주성분분석을 이용한 사면의 위험성 평가)

  • Jung, Soo-Jung;Kim, -Yong-Soo;Kim, Tae-Hyung
    • Journal of the Korean Geotechnical Society
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    • v.26 no.10
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    • pp.69-79
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    • 2010
  • To detect abnormal events in slopes, Principal Component Analysis (PCA) is applied to the slope that was collapsed during monitoring. Principal component analysis is a kind of statical methods and is called non-parametric modeling. In this analysis, principal component score indicates an abnormal behavior of slope. In an abnormal event, principal component score is relatively higher or lower compared to a normal situation so that there is a big score change in the case of abnormal. The results confirm that the abnormal events and collapses of slope were detected by using principal component analysis. It could be possible to predict quantitatively the slope behavior and abnormal events using principal component analysis.

A Comparison on Independent Component Analysis and Principal Component Analysis -for Classification Analysis-

  • Kim, Dae-Hak;Lee, Ki-Lak
    • Journal of the Korean Data and Information Science Society
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    • v.16 no.4
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    • pp.717-724
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    • 2005
  • We often extract a new feature from the original features for the purpose of reducing the dimensions of feature space and better classification. In this paper, we show feature extraction method based on independent component analysis can be used for classification. Entropy and mutual information are used for the selection of ordered features. Performance of classification based on independent component analysis is compared with principal component analysis for three real data sets.

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Fuzzy Relational Calculus based Component Analysis Methods and their Application to Image Processing

  • Nobuhara, Hajime;Hirota, Kaoru
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • pp.395-398
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    • 2003
  • Two component analysis methods based on the fuzzy relational calculus are proposed in the setting of the ordered structure. First component analysis is based on a decomposition of fuzzy relation into fuzzy bases, using gradient method. Second one is a component analysis based on the eigen fuzzy sets of fuzzy relation. Through experiments using the test images extracted from SIDBA and View Sphere Database, the effectiveness of the proposed component analysis methods is confirmed. Furthermore, improvements of the image compression/reconstruction and image retrieval based on ordered structure are also indicated.

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Asymptotic Test for Dimensionality in Probabilistic Principal Component Analysis with Missing Values

  • Park, Chong-sun
    • Communications for Statistical Applications and Methods
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    • v.11 no.1
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    • pp.49-58
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    • 2004
  • In this talk we proposed an asymptotic test for dimensionality in the latent variable model for probabilistic principal component analysis with missing values at random. Proposed algorithm is a sequential likelihood ratio test for an appropriate Normal latent variable model for the principal component analysis. Modified EM-algorithm is used to find MLE for the model parameters. Results from simulations and real data sets give us promising evidences that the proposed method is useful in finding necessary number of components in the principal component analysis with missing values at random.

Arrow Diagrams for Kernel Principal Component Analysis

  • Huh, Myung-Hoe
    • Communications for Statistical Applications and Methods
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    • v.20 no.3
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    • pp.175-184
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    • 2013
  • Kernel principal component analysis(PCA) maps observations in nonlinear feature space to a reduced dimensional plane of principal components. We do not need to specify the feature space explicitly because the procedure uses the kernel trick. In this paper, we propose a graphical scheme to represent variables in the kernel principal component analysis. In addition, we propose an index for individual variables to measure the importance in the principal component plane.

Interoperability of OpenGIS Component and Spatial Analysis Component (개방형 GIS 컴포넌트에서의 공간분석 컴포넌트 연동)

  • Min, Kyoung-Wook;Jang, In-Sung;Lee, Jong-Hun
    • Journal of Korea Spatial Information System Society
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    • v.3 no.1
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    • pp.49-62
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    • 2001
  • Recently, component-based software has become main trends in designing and developing computer software products. This component-based software has advantage of the interoperability on distributed computing environment and the reusability of pre-developed components. Also, GIS is designed and implemented with this component-based methodology, called Open GIS Component. OGC(OpenGIS Consortium) have announced various implementation and design specification and topic in GIS. In GIS, Spatial analysis functions like network analysis, TIN analysis are very important function and basically, estimate system functionality and performance using this analysis methods. The simple feature geometry specification is announced by OGC to increase the full interoperability of various spatial data. This specification includes just geometry spatial data model. However, in GIS which manages spatial data, not only geometric data but also topological data and various analysis functions have been used. The performance of GIS depends on how this geometric and topological data is managed well and how various spatial analyses are executed efficiently. So it requires integrated spatial data model between geometry and topology and extended data model of topology for spatial analysis, in case network analysis and TIN analysis in open GIS component. In this paper, we design analysis component like network analysis component and TIN analysis component. To manage topological information for spatial analysis in open GIS component, we design extended data model of simple feature geometry for spatial analysis. In addition to, we design the overall system architecture of open GIS component contained this topology model for spatial analysis.

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Independent Component Biplot (독립성분 행렬도)

  • Lee, Su Jin;Choi, Yong-Seok
    • The Korean Journal of Applied Statistics
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    • v.27 no.1
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    • pp.31-41
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    • 2014
  • Biplot is a useful graphical method to simultaneously explore the rows and columns of a two-way data matrix. In particular, principal component factor biplot is a graphical method to describe the interrelationship among many variables in terms of a few underlying but unobservable random variables called factors. If we consider the unobservable variables (which are mutually independent and also non-Gaussian), we can apply the independent component analysis decomposing a mixture of non-Gaussian in its independent components. In this case, if we apply the principal component factor analysis, we cannot clearly describe the interrelationship among many variables. Therefore, in this study, we apply the independent component analysis of Jutten and Herault (1991) decomposing a mixture of non-Gaussian in its independent components. We suggest an independent component biplot to interpret the independent component analysis graphically.