• Journal of Astronomy and Space Sciences
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• v.29 no.3
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• pp.269-274
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• 2012

Kinematic global positioning system precise point positioning (GPS PPP) technology is widely used to the several area such as monitoring of crustal movement and precise orbit determination (POD) using the dual-frequency GPS observations. In this study we developed a kinematic PPP technology and applied 3-pass (forward/backward/forward) filter for the stabilization of the initial state of the parameters to be estimated. For verification of results, we obtained GPS data sets from six international GPS reference stations (ALGO, AMC2, BJFS, GRAZ, IENG and TSKB) and processed in daily basis by using the developed software. As a result, the mean position errors by kinematic PPP showed 0.51 cm in the east-west direction, 0.31 cm in the north-south direction and 1.02 cm in the up-down direction. The root mean square values produced from them were 1.59 cm for the east-west component, 1.26 cm for the south-west component and 2.95 cm for the up-down component.

• Journal of the Korean Mathematical Society
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• v.33 no.1
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• pp.101-119
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• 1996

A complex $n(\geq 2)$-dimensional Kaehlerian manifold of constant holomorphic sectional curvature c is called a complex space form, which is denoted by $M_n(c)$.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.11a
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• pp.173-179
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• 2006

In order to explain a U-shape pattern of stock returns, Fama and French(1988) suggested the state-space model consisting of I(1) permanent component and AR(1) stationary component. They concluded the autoregression coefficient induced from the state-space model follow the U-shape pattern and the U-shape pattern of stock returns was due to both negative autocorrelation in returns beyond a year and substantial mean-reversion in stock market prices. However, we found negative autocorrelation is induced under the assumption that permanent and stationary noise component are independent in the state-space model. In this paper, we derive the autoregression coefficient based on ARIMA process equivalent to the state-space model without the assumption of independency. Based on the estimated parameters, we investigate the pattern of the time-varying autoregression coefficient and conclude the autoregression coefficient from the state-space model of ARIMA(1,1,1) process does not follow a U-shape pattern, but has always positive sign. We applied this result on the data of 1 month retums for all NYSE stocks for the 1926-85 period from the Center for Research in Security Prices.

• KIEAE Journal
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• v.9 no.1
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• pp.31-38
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• 2009

The purpose of this study is to present direction in outdoors space planning and design after direction through user characteristic analysis through spectacle component establishment of culture art center outdoors space through on-the-site analysis and literature investigation to culture art center of Seoul city and capital region 17 places in this research. The data was collected from classification and bisection kind, subdivision kind, and great classification composed to 17 items. User satisfaction side and Variable that is looked below satisfaction than average appeared to bench, pergola, sculpture facilities, pavement facilities, border facilities. And these facilities were analyzed dissatisfaction. When see satisfaction model, when make up culture art center or similar facilities in local government hereafter because parking facilities and rest area cause big effect in satisfaction, is judged that is item to consider most preferentially. In most case, parking lot security from outdoors space, resting place security, security of field performance facilities etc. taking a serious view because tendency that users see performance or use most vehicles except neighborhood walking area for a rest, a walk etc.. is trend. But, is judged that physical side so that can feel satisfaction as space security of quantitative side is important but users utilize substantially and side that is the program are more important in hereafter.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.2
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• pp.67.4-68
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• 2021

In studying the dynamical evolution of galaxy clusters, one intriguing approach is to compare the spatial distributions of various components, such as the dark matter, the member galaxies, the gas, and the intracluster light (ICL; the diffuse light from stars, which are not bound any individual cluster galaxy). If we find a visible component whose spatial distribution coincides with the dark matter distribution, then we could draw a dark matter map without requiring laborious weak lensing analysis. Furthermore, if the component traces the dark matter distribution better for more relaxed galaxy cluster, we could use the similarity as a dynamical stage estimator of the galaxy cluster. We present a novel new methodology to quantify the similarity of two or more 2-dimensional spatial distributions. We apply the method to a sample of galaxy clusters at different dynamical stages simulated within N-cluster Run, which is an N-body simulation using the galaxy replacement technique. Among the various components (stellar particles, galaxies, ICL), the velocity defined ICL+ brightest cluster galaxy (BCG) component traces the dark matter best. Between the sample galaxy clusters, the relaxed clusters show stronger similarity of the spatial distribution between the dark matter and ICL+BCG than the dynamically young clusters.

• Journal of the Korean housing association
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• v.13 no.3
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• pp.11-20
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• 2002

The purpose of this study is to find out the meanings of water and water spaces in the Western architecture. The meaning of water is investigated by means of mythology and literature studies. It is found that water has ambivalent meanings, i.e. life and death, creation and destruction, chastity and sensuality. The meanings of water spaces in the Western architecture are dramatic, secret and dynamic, and represent publicity, verticality, formality in addition to desire for authority and realization(embodiment) of paradise. Water space is an essential component of beautiful and dynamic spaces and is used to revitalize dreary spaces.

• Journal of The Korean Astronomical Society
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• v.32 no.1
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• pp.17-39
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• 1999

We investigate dynamical evolution of globular clusters with multi-mass component under the Galactic tidal field. We compare the results with our previous work which considered the cases of single-mass component m the globular clusters. We find the followings: 1) The general evolutions are similar to the cases of single-mass component. 2) There is no evidence for dependence on the orbital phase of the cluster as in the case of single-mass component. 3) The escape rate in multi-mass models is larger than that in the single-mass models. 4) The mass-function depends on radius more sensitively in anisotropic models than in isotropic models.

• International Journal of Aeronautical and Space Sciences
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• v.13 no.3
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• pp.398-404
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• 2012

A fault detection process is necessary for high integrity systems like satellites, missiles and aircrafts. Especially, the satellite has to be expected to detect faults autonomously because it cannot be fixed by an expert in the space. Faults can cause critical errors to the entire system and the satellite does not have sufficient computation power to operate a large scale fault management system. Thus, a fault detection method, which has less computational burden, is required. In this paper, we proposed a modified PCA (Principal Component Analysis) as a powerful fault detection method of redundant IMU (Inertial Measurement Unit). The proposed method combines PCA with the parity space approach and it is much more efficient than the others. The proposed fault detection algorithm, modified PCA, is shown to outperform fault detection through a simulation example.

• 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.

• Phonetics and Speech Sciences
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• v.1 no.1
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• pp.69-73
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• 2009

This paper proposes an efficient global covariance-based principal component analysis (GCPCA) for speaker identification. Principal component analysis (PCA) is a feature extraction method which reduces the dimension of the feature vectors and the correlation among the feature vectors by projecting the original feature space into a small subspace through a transformation. However, it requires a larger amount of training data when performing PCA to find the eigenvalue and eigenvector matrix using the full covariance matrix by each speaker. The proposed method first calculates the global covariance matrix using training data of all speakers. It then finds the eigenvalue matrix and the corresponding eigenvector matrix from the global covariance matrix. Compared to conventional PCA and Gaussian mixture model (GMM) methods, the proposed method shows better performance while requiring less storage space and complexity in speaker identification.