• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.32 no.6
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• pp.96-103
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• 2004

Sought a set of component performance lines from experiment data or some data supplied in the engine manufacturer to improve the traditional scaling method and suggested a map scaling method that construct component performance lines newly using polynomial equations of MATLAB program. In this study, applied technique that is proposed newly to PT6A-62 that verified technique that is proposed newly using experiment data of small. size turboshaft engine, and is actuality aircraft engine. In identification of the component maps of the turboprop engine, the simulated performance using the proposed scaling method was compared with the real engine performance data and the performance using the traditional scaling method.

• Journal of Korea Spatial Information System Society
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• v.2 no.2 s.4
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• pp.85-88
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• 2000

GPS/GIS Related Information Technology evolves drastically and a great quantity of spatial information is available to users who require highly qualitative spatial information services. Hence the development of GIS tools that can improve position accuracy and maintenance efficiency is required. To meet various requirements from users. we developed a GPS data processing component based on OLE/COM by utilizing GDK which is a unique domestic GIS product. Also we developed an application package that includes the GPS data processing component to implement GPS into GIS. The development of GPS data processing component will contribute toward the expansion of component-wise GIS softwares.

• The Korean Journal of Applied Statistics
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• v.33 no.1
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• pp.61-74
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• 2020

Interval-valued data, one type of symbolic data, are observed in the form of intervals rather than single values. Each interval-valued observation has an internal variation. Principal component analysis reduces the dimension of data by maximizing the variance of data. Therefore, the principal component analysis of the interval-valued data should account for the variance between observations as well as the variation within the observed intervals. In this paper, three principal component analysis methods for interval-valued data are summarized. In addition, a new method using a truncated normal distribution has been proposed instead of a uniform distribution in the conventional quantile method, because we believe think there is more information near the center point of the interval. Each method is compared using simulations and the relevant data set from the OECD. In the case of the quantile method, we draw a scatter plot of the principal component, and then identify the position and distribution of the quantiles by the arrow line representation method.

• Journal of the Korean Data and Information Science Society
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• v.19 no.3
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• pp.751-759
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• 2008

Mixtures of factor analyzers(MFA) is useful to model the distribution of high-dimensional data on much lower dimensional space where the number of observations is very large relative to their dimension. Mixtures of common factor analyzers(MCFA) can reduce further the number of parameters in the specification of the component covariance matrices as the number of classes is not small. Moreover, the factor scores of MCFA can be displayed in low-dimensional space to distinguish the groups. We propose the factor scores of MCFA as new low-dimensional features for classification of high-dimensional data. Compared with the conventional dimension reduction methods such as principal component analysis(PCA) and canonical covariates(CV), the proposed factor score was shown to have higher correct classification rates for three real data sets when it was used in parametric and nonparametric classifiers.

• Journal of the Korea Society of Computer and Information
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• v.23 no.12
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• pp.21-26
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• 2018

As the large amount of data can be efficiently stored, the methods extracting meaningful features from big data has become important. Especially, the techniques of converting high- to low-dimensional data are crucial for the 'Data visualization'. In this study, principal component analysis (PCA; linear dimensionality reduction technique) and Isomap (non-linear dimensionality reduction technique) are introduced and applied to neural big data obtained by the functional magnetic resonance imaging (fMRI). First, we investigate how much the physical properties of stimuli are maintained after the dimensionality reduction processes. We moreover compared the amount of residual variance to quantitatively compare the amount of information that was not explained. As result, the dimensionality reduction using Isomap contains more information than the principal component analysis. Our results demonstrate that it is necessary to consider not only linear but also nonlinear characteristics in the big data analysis.

• 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.

• Journal of Satellite, Information and Communications
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• v.9 no.1
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• pp.65-69
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• 2014

The Communication, Ocean and Meteorological Satellite(COMS) data processing system(CMDPS) has developed to support the meteorological observation and weather prediction by NMSC(National Meteorological Satellite Center) and it is generating the 16 kind of meteorological data(Level 2 product). Unfortunately, currently CMDPS has some problems in terms of the system maintenance and the integrated software efficiency, and the extension to support the next generation meteorological satellite data processing. To solve this problems, in this paper, we suggest the extensible component-based system architecture for COMS meteorological data processing with consideration of identified issues. Proposed system is adapted the component-based frameworks with extensible architecture. We expects that this system will be provide easy ways to develop new satellite data processing algorithms and to maintain the system.

• Proceedings of the Korean Environmental Sciences Society Conference
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• 2003.11a
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• pp.33-36
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• 2003

Principal component analysis (PCA) was used to improve image classification by the unsupervised classification techniques, the K-means. To do this, I selected a Landsat TM scene of Jeju Island, Korea and proposed two methods for PCA： unstandardized PCA (UPCA) and standardized PCA (SPCA). The estimated accuracy of the image classification of Jeju area was computed by error matrix. The error matrix was derived from three unsupervised classification methods. Error matrices indicated that classifications done on the first three principal components for UPCA and SPCA of the scene were more accurate than those done on the seven bands of TM data and that also the results of UPCA and SPCA were better than those of the raw Landsat TM data. The classification of TM data by the K-means algorithm was particularly poor at distinguishing different land covers on the island. From the classification results, we also found that the principal component based classifications had characteristics independent of the unsupervised techniques (numerical algorithms) while the TM data based classifications were very dependent upon the techniques. This means that PCA data has uniform characteristics for image classification that are less affected by choice of classification scheme. In the results, we also found that UPCA results are better than SPCA since UPCA has wider range of digital number of an image.

• Journal of the Korean Association of Geographic Information Studies
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• v.7 no.4
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• pp.24-33
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• 2004

Advances in remote sensing technologies are resulting in the rapid increase of the number of spectral channels, and thus, growing data volumes. This creates a need for developing faster techniques for processing such data. One application in which such fast processing is needed is the dimension reduction of the multispectral data. Principal component transformation is perhaps the mostpopular dimension reduction technique for multispectral data. In this paper, we discussed the processing procedures of principal component transformation. And we presented and discussed the results of the principal component transformation of the multispectral data. Moreover principal components image data are classified by the Maximum Likelihood method and Multilayer Perceptron method. In addition, the performances of two classification methods and data reduction effects are evaluated and analyzed based on the experimental results.

• The KIPS Transactions:PartD
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• v.8D no.6
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• pp.657-664
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• 2001

The Information Technology has progressed to the open architecture, component, and multimedia services under Internet, ensuring interoperability, reusability, and realtime. The GIS is a system processing geo-spatial information such as natural resources, buildings, roads, and many kinds of facilities in the earth. The spatial information featured by complexity and diversity requires interoperability and reusability of pre-built databases under open architecture. This paper is for the development of component based open GIS Software. The goal of the open GIS component software is a middleware of GIS combining technology of open architecture and component ensuring interoperability of spatial information and reusability of elementary pieces of GIS software. The open GIS component conforms to the distributed open architecture for spatial information proposed by OGC (Open GIS Consortium). The system consists of data provider components, kernel (MapBase) components, clearinghouse components and five kinds of GIS application of local governments. The data provider component places a unique OLE DB interface to connect and access diverse data sources independent of their formats and locations. The MapBase component supports core and common technology of GIS feasible for various applications. The clearinghouse component provides functionality about discovery and access of spatial information under Internet. The system is implemented using ATL/COM and Visual C++ under MicroSoft's Windows environment and consisted of more than 20 components. As we made case study for KSDI (Korea Spatial Data Infrastructure) sharing spatial information between local governments, the advantage of component based open GIS software was proved. Now, we are undertaking another case study for sharing seven kinds of underground facilities using the open GIS component software.