• The Korean Journal of Applied Statistics
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• v.23 no.3
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• pp.609-619
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• 2010

Geostatistical data among spatial data is analyzed in three stages: (1) variogram estimation, (2) model fitting for the estimated variograms and (3) spatial prediction using the fitted variogram model. It is very important to estimate the variograms properly as the first stage(i.e., variogram estimation) affects the next two stages. In general, the variogram is estimated with the moment estimator. To estimate the variogram, we have to decide the 'lag increment' or the 'number of lags'. However, there is no established rule for selecting the number of lags in estimating the variogram. The present paper proposes a method of choosing the optimal number of lags based on the PRESS statistic. To show the usefulness of the proposed method, we perform a small simulation study and show an empirical example with with air pollution data from Korea.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.8B
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• pp.1438-1443
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• 1999

In this paper, we propose detection methods for gradual scene changes such as dissolve, pan, and zoom. The proposal method to detect a dissolve region uses scene features based on spatial statistics of the image. The spatial statistics to define shot boundaries are derived from squared means within each local area. We also propose a method of the camera motion detection using four representative motion vectors in the background. Representative motion vectors are derived from macroblock motion vectors which are directly extracted from MPEG streams. To reduce the implementation time, we use DC sequences rather than fully decoded MPEG video. In addition, to detect the gradual scene change region precisely, we use all types of the MPEG frames(I, P, B frame). Simulation results show that the proposed detection methods perform better than existing methods.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.79.1-79.1
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• 2019

We present an algorithm for the fast computation of the general N-point spatial correlation functions of any discrete point set embedded within an Euclidean space of ${\mathbb{R}}n$. Utilizing the concepts of kd-trees and graph databases, we describe how to count all possible N-tuples in binned configurations within a given length scale, e.g. all pairs of points or all triplets of points with side lengths < rmax. Through benchmarking we show the computational advantage of our new graph-based algorithm over more traditional methods. We show that all 3-point configurations up to and beyond the Baryon Acoustic Oscillation scale (~200 Mpc in physical units) can be performed on current Sloan Digital Sky Survey (SDSS) data in reasonable time. Finally we present the first measurements of the 4-point correlation function of ~0.5 million SDSS galaxies over the redshift range 0.43< z <0.7. We present the publicly available code GRAMSCI (GRAph Made Statistics for Cosmological Information; bitbucket.org/csabiu/gramsci), under a GNU General Public License.

• The Korean Journal of Applied Statistics
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• v.25 no.2
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• pp.293-300
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• 2012

When Binomial data are obtained, logit and logit mixed models are commonly used for small area estimation. Those models are known to have good statistical properties through the use of unit level information; however, data should be obtained as area level in order to use area level information such as spatial correlation or auto-correlation. In this research, we suggested a new small area estimator obtained through the combination of unit level information with area level information.

• The Korean Journal of Applied Statistics
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• v.27 no.4
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• pp.655-665
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• 2014

An adequate understanding and response to natural hazards such as heat wave, heavy rainfall and severe drought is required. We apply extreme value theory to analyze these abnormal weather phenomena. It is common for extremes in climatic data to be nonstationary in space and time. In this paper, we analyze summer rainfall data in South Korea using exceedance values over thresholds estimated by quantile regression with location information and time as covariates. We group weather stations in South Korea into 5 clusters and t extreme value models to threshold exceedances for each cluster under the assumption of independence in space and time as well as estimates of uncertainty for spatial dependence as proposed in Northrop and Jonathan (2011).

• The Bulletin of The Korean Astronomical Society
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• v.46 no.2
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• pp.72.3-73
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• 2021

Baryon Acoustic Oscillations (BAO) are caused by acoustic density waves in the early universe and act as a standard ruler in the clustering pattern of galaxies in the late Universe. Measuring the BAO feature in the 2-point correlation function of a sample of galaxies allows us to estimate cosmological distances to the galaxies mean redshift, , which is important for testing and constraining the cosmology model. The BAO feature is also expected to appear in the higher order statistics. In this work we measure the generalized spatial N-point point correlation functions up to 4th order. We made measurements of the 2, 3, and 4-point correlation functions in the SDSS-III DR12 CMASS data, comprising of 777,202 galaxies. The errors and covariances matrices were estimated from 500 mock catalogues. We created a theoretical model for these statistics by measuring the N-point functions in halo catalogues produced by the approximate Lagrangian perturbation theory based simulation code, PINOCCHIO. We created simulations using initial conditions with and without the BAO feature. We find that the BAO is detected to high significance up to the 4-point correlation function.

• Journal of the Korean Geotechnical Society
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• v.20 no.7
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• pp.33-44
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• 2004

This paper is focused on the spatial variability of measured strong motion data during earthquake and its relationship with the performance of water distribution pipelines and residential buildings. Analyses of strong motion and the correlations of peak ground velocity (PGV) and pipeline and building damage were conducted with a very large geographical information system (GIS) database including the relationship of time and earthquake intensity and the measured location, and Kriging spatial statistics. Kriging was used to develop regressions of pipeline repair rate (RR) and residential building damage ratio (DR) associated with $90\%$ confidence peak ground velocity (PGV). Such regressions using Kriging provide an explicit means of characterizing the uncertainty embodied in the strong motion data compared with other spacial statistics such as inverse distance method.

• The Korean Journal of Applied Statistics
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• v.33 no.5
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• pp.603-614
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• 2020

This study focuses on the prediction and factors of apartment prices in Seoul using a convolutional neural networks (CNN) model that has shown excellent performance as a predictive model of image data. To do this, we consider natural environmental factors, infrastructure factors, and social economic factors of the apartments as input variables of the CNN model. The natural environmental factors include rivers, green areas, and altitudes of apartments. The infrastructure factors have bus stops, subway stations, commercial districts, schools, and the social economic factors are the number of jobs and criminal rates, etc. We predict apartment prices and interpret the factors for the prices by converting the values of these input variables to play the same role as pixel values of image channels for the input layer in the CNN model. In addition, the CNN model used in this study takes into account the spatial characteristics of each apartment by describing the natural environmental and infrastructure factors variables as binary images centered on each apartment in each input layer.

• The Bulletin of The Korean Astronomical Society
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• v.24 no.1
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• pp.38-38
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• 1999

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.8
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• pp.95-104
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• 2016

Subspace-based direction-of-arrival (DOA) estimation algorithms have a difficulty in dealing with coherent signals caused by multi-path environment. As one of attempts to solve this problem, a spatial differencing method is known to be useful for not only estimating DOAs of the coherent signals but also improving the number of resolvable wavefronts even more than the number of antenna elements. However, since the conventional spatial differencing method uses only the partial statistics of the observed data, this method suffers from the performance degradation in estimation accuracy caused by the residual correlation between the uncorrelated signals. To cope with this problem, in this paper, a generalized spatial differencing method is proposed. Unlike the conventional method, the proposed method utilizes the entire statistics of the received signals. Therefore, the additional performance enhancement in both estimation accuracy and the number of resolvable wavefronts can be achieved. The performance analyses with computer simulations show that the proposed method outperforms the conventional method in terms of the estimation accuracy and the number of resolvable wavefronts.