• Journal of KIISE:Software and Applications
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• v.29 no.10
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• pp.747-754
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• 2002

In this paper, We propose a document thematic words extraction by using principal component analysis(PCA) which is one of the multivariate statistical methods. The proposed PCA model understands the flow of words in the document by using an eigenvalue and an eigenvector, and extracts thematic words. The proposed model is estimated by applying to document summarization. Experimental results using newspaper articles show that the proposed model is superior to the model using either word frequency or information retrieval thesaurus. We expect that the Proposed model can be applied to information retrieval , information extraction and document summarization.

• Smart Structures and Systems
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• v.17 no.6
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• pp.1017-1030
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• 2016

This study focuses on the system identification of reinforced concrete bridges using vector autoregressive model (VAR). First, the time series output response from a bridge establishes the autoregressive (AR) models. AR models are one of the most accurate methods for stationary time series. Burg's algorithm estimates the autoregressive coefficients (ARCs) at p-lag by reducing the sum of the forward and the backward errors. The computed ARCs are assembled in the state system matrix and the eigen-system realization algorithm (ERA) computes: the eigenvector matrix that contains the vectors of the mode shapes, and the eigenvalue matrix that contains the associated natural frequencies. By taking advantage of the characteristic of the AR model with ERA (ARMERA), civil engineering can address problems related to damage detection. Operational modal analysis using ARMERA is applied to three experiments. One experiment is coupled with an artificial neural network algorithm and it can detect damage locations and extension. The neural network uses a specific number of ARCs as input and multiple submatrix scaling factors of the structural stiffness matrix as output to represent the damage.

• Journal of Advanced Navigation Technology
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• v.5 no.2
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• pp.111-122
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• 2001

In this paper, we study the performance of eigencanceller which use a eigenvector and eigenvalue in order to update a weighter vector. Eigencanceller can suppress directional interferences and noise effectively while maintaining specified beam pattern constraints. The constraints and optimal weight vector of eigencanceller vary by using interference and noise or desired signal, interference signal and noise as array input signal. From the analysis results in the steady state, We show that weight vectors in each case are simplified the form of projection equation that belongs to desired subspace orthogonal to interference subspace and eigencanceller has the better performance than RLS method through mathematical analysis and simulation.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.22 no.6
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• pp.495-501
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• 2012

In this study, the natural frequencies and mode shape of an external mounting pod were verified using the modal analysis and modal testing technique for a pod mounted on an aircraft. The procedure associated with the FE model building of an external mounted pod to predict the dynamic behavior of aircraft structures is described. The simplified FE model reflecting the results of the modal testing of a pod is built through the optimization and will be applied to the structural dynamic model of an aircraft which is used to verified the stability of vibration and flutter of an aircraft.

• Journal of the Korean earth science society
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• v.38 no.3
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• pp.173-181
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• 2017

The stability of the steady Rossby-Haurwitz wave (R-H wave) in the nondivergent barotropic model (NBM) on the sphere was investigated with the normal mode method. The linearized NBM equation with respect to the R-H wave was formulated into the eigenvalue-eigenvector problem consisting of the huge sparse matrix by expanding the variables with the spherical harmonic functions. It was shown that the definite threshold R-H wave amplitude for instability could be obtained by the normal mode method. It was revealed that some unstable modes were stationary, which tend to amplify without the time change of the spatial structure. The maximum growth rate of the most unstable mode turned out to be in almost linear proportion to the R-H wave amplitude. As a whole, the growth rate of the unstable mode was found to increase with the zonal- and total-wavenumber. The most unstable mode turned out to consist of more-than-one zonal wavenumber, and in some cases, the mode exhibited a discontinuity over the local domain of weak or vanishing flow. The normal mode method developed here could be readily extended to the basic state comprised of multiple zonalwavenumber components as far as the same total wavenumber is given.

• Structural Engineering and Mechanics
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• v.52 no.5
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• pp.903-918
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• 2014

Large space structures may have resonant low eigenvalues and often these appear with closely-spaced natural frequencies. Owing to the coupling among modes with closely-spaced natural frequencies, each eigenvector corresponding to closely-spaced eigenvalues is ill-conditioned that may cause structural instability. The subspace to an invariant subspace corresponding to closely-spaced eigenvalues is well-conditioned, so a method is presented to design the feedback control law of intelligent structures with closely-spaced eigenvalues in this paper. The main steps are as follows: firstly, the system with closely-spaced eigenvalues is transformed into that with repeated eigenvalues by the spectral decomposition method; secondly, the computation for the linear combination of eigenvectors corresponding to repeated eigenvalues is obtained; thirdly, the feedback control law is designed on the basis of the system with repeated eigenvalues; fourthly, the system with closely-spaced eigenvalues is regarded as perturbed system on the basis of the system with repeated eigenvalues; finally, the feedback control law is applied to the original system, the first order perturbations of eigenvalues are discussed when the parameter modifications of the system are introduced. Numerical examples are given to demonstrate the application of the present method.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.3
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• pp.79-84
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• 2021

A feature extraction method capable of reflecting features well while mainaining the properties of data is required in order to process high-dimensional data. The principal component analysis method that converts high-level data into low-dimensional data and express high-dimensional data with fewer variables than the original data is a representative method for feature extraction of data. In this study, we propose a principal component analysis method based on adaptive correlation when selecting principal component variables in principal component analysis for data feature extraction when the data is high-dimensional. The proposed method analyzes the principal components of the data by adaptively reflecting the correlation based on the correlation between the input data. I want to exclude them from the candidate list. It is intended to analyze the principal component hierarchy by the eigen-vector coefficient value, to prevent the selection of the principal component with a low hierarchy, and to minimize the occurrence of data duplication inducing data bias through correlation analysis. Through this, we propose a method of selecting a well-presented principal component variable that represents the characteristics of actual data by reducing the influence of data bias when selecting the principal component variable.

• Journal of KIISE:Software and Applications
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• v.31 no.3
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• pp.361-368
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• 2004

The purpose of this study is to propose a new on-line nonlinear PCA(OL-NPCA) method for a nonlinear feature extraction from the incremental data. Kernel PCA(KPCA) is widely used for nonlinear feature extraction, however, it has been pointed out that KPCA has the following problems. First, applying KPCA to N patterns requires storing and finding the eigenvectors of a N${\times}$N kernel matrix, which is infeasible for a large number of data N. Second problem is that in order to update the eigenvectors with an another data, the whole eigenspace should be recomputed. OL-NPCA overcomes these problems by incremental eigenspace update method with a feature mapping function. According to the experimental results, which comes from applying OL-NPCA to a toy and a large data problem, OL-NPCA shows following advantages. First, OL-NPCA is more efficient in memory requirement than KPCA. Second advantage is that OL-NPCA is comparable in performance to KPCA. Furthermore, performance of OL-NPCA can be easily improved by re-learning the data.

• Journal of the Korean Society of Groundwater Environment
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• v.6 no.2
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• pp.87-94
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• 1999

This study is providing Modified DRASTIC Model to which lineament density and land use are added as additional factors using geographic infomation system(GIS). and then to evaluate groundwater contamination potential of Pyungtek-Gun area in Kyunggi-Do. In this study. the reason for using additional factors is because. in case of lineament density. when we consider that most of aquifer is bedrock aquifer in hydrogeologic environment of the Korea, lineament is very important to flow of groundwater and contamination material. and because land use can reflect indirectly impact of point or non_point source in study area. For statistical analysis. vector coverage per each factor is converted to grid layer and after each correlation coefficient between factors, covariance, variance. eigenvalue and eigenvector by principal component analysis of 3 direction. are calculated. correlation between factors is analyzed. Also after correlation coefficients between general DRASTIC layer and rated lineament density layer and between general DRASTIC layer and rated land use layer are calculated. final modified DRASTIC Model is constructed by using them with each weighting. when modified DRASTIC Model was compared with general DRASTIC Model, comtamination potential in modified DRASTIC Model is fairly detailed and consequently. vulnerable area which has high contamination potential could be presented concretly.

• Journal of the Korean Data and Information Science Society
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• v.23 no.2
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• pp.285-298
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• 2012

This research deals with a relationship between the performance of Korean professional baseball players and their annual salaries. It is based on the sabermetrics, which measures the performance of baseball batters in a refined way. We collect the records of batters of eight professional baseball clubs during the season 2009 and 2010. Then, we calculate every index of the sabermetrics. Principal component analysis is used for examining the relationship between those indexes of sabermetrics and annual salary for the next year. In general, batters who show higher performance get more salary. The result of this research can be useful in order to reach an agreement on salary between a player and his club partner.