• The Transactions of the Korea Information Processing Society
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• v.3 no.1
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• pp.153-159
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• 1996

Linear models for spatial data are proposed by example in the paper. This method was introduced to Korea for the first time in the early part of 1990's. The correlation of spatial patterns is computed by Moran Index., and then correlogram is proposed as the method to identify correlation of spatial patterns. Due to computational difficulties with ML, an alternative estimator has been used as an eigenvalue method.

• Journal of the Korean earth science society
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• v.31 no.2
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• pp.119-128
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• 2010

To make reliable interpretations on the sparse spatial data, the spatial distribution characteristics that are inevitable for spatial estimation should be properly analyzed. Variograms have been widely used for obtaining the spatial characteristics inherent to data in spatial estimation problems. But their applications were limited as the basic information for further data estimation. Therefore, the additional analysis of the meaning of variograms is required for more reliable data processing and interpretations. In this paper, we investigated the proper meaning of variogram values and the specific features of distributions which can be obtained through variogram analysis. Variograms can provide the information on both linearity and the strength changes of interrelationships between the data sets according to the direction and lag distance. First, sill and range values, which are main parameters of variograms, were analyzed. Then a similarity range using spatial auto-correlation values was introduced to verify the applicability of linearity analysis through the comparative study of spatial distribution features of gravity and magnetic data collected in Hwasan caldera. Through these analyses, we were able to identify the dissimilar patterns of gravity and magnetic data that became apparent according to the distribution and variation ranges of the data sets. It is inferred that the gravity and magnetic anomalous bodies are extended to the ground because linearity direction of gravity and magnetic data appear similarly with linearity derection of topography in Hwasan caldera.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.4
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• pp.414-424
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• 2012

Finding or automatically generating problem instance is useful for algorithm analysis/test. The topic has been of interest in the field of hardware/software engineering and theory of computation. We apply objective value-distance correlation analysis to problem spaces, as previous researchers applied it to solution spaces. According to problems, we define the objective function by (1) execution time of tested algorithm or (2) its optimality; this definition is interpreted as difficulty of the problem instance being solved. Our correlation analysis is based on the following aspects: (1) change of correlation when we use different algorithms or different distance functions for the same problem, (2) change of that when we improve the tested algorithm, (3) relation between a problem instance space and the solution space for the same problem. Our research demonstrates the way of problem instance space analysis and will accelerate the problem instance space analysis as an initiative research.

• The Korean Journal of Applied Statistics
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• v.23 no.2
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• pp.335-344
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• 2010

Reliability of the estimation is usually damaged in the situation where a linear regression model without spatial dependencies is employed to the spatial data analysis. In this study, we considered the conditional autoregressive model in order to construct spatial association structures and estimate the parameters via the Bayesian approaches. Finally, we compared the performances of the models with spatial effects and the ones without spatial effects. We analyzed the yearly total crime count data measured from each of 25 districts in Seoul, South Korea in 2007.

• Journal of Advanced Navigation Technology
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• v.26 no.3
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• pp.166-171
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• 2022

In this paper, we propose an algorithm to improve DoA(direction of arrival) estimation performance of the subspace-based method by generating high robustness correlation matrix of the signals incident on the uniformly linear array antenna. The existing subspace-based DoA estimation method estimates the DoA by obtaining a correlation matrix and dividing it into a signal subspace and a noise subspace. However, the component of the correlation matrix obtained from the low SNR and small number of snapshots inaccurately estimates the signal subspace due to the noise component of the antenna, thereby degrading the DoA estimation performance. Therefore a robust correlation matrix is generated by arranging virtual signal vectors obtained from the existing correlation matrix in a sliding manner. As a result of simulation using MUSIC and ESPRIT, which are representative subspace-based methods,, the computational complexity increased by less than 2.5% compared to the existing correlation matrix, but both MUSIC and ESPRIT based on RMSE 1° showed superior DoA estimation performance with an SNR of 3dB or more.

• Journal of Korea Water Resources Association
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• v.40 no.12
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• pp.943-956
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• 2007

Among various statistics, the spatial correlation function, that is "correlogram", is frequently used to evaluate or design the rain gauge network and to model the rainfall field. The spatial correlation structure of rainfall has the significant variation due to many factors. Thus, the variation of spatial correlation structure of rainfall causes serious problems when deciding the spatial correlation function of rainfall within the basin. In this study, the spatial rainfall structure was modeled using bivariate mixed distributions to derive monthly spatial correlograms, based on Gaussian and lognormal distributions. This study derived the correlograms using hourly data of 28 rain gauge stations in the Keum river basin. From the results, we concluded as following; (1) Among three cases (Case A, Case B, Case C) considered, the Case A(+,+) seems to be the most relevant as it is not distorted much by zero measurements. (2) The spatial correlograms based on the lognormal distribution, which is theoretically as well as practically adequate, is better than that based on the Gaussian distribution. (3) The spatial correlation in July exponentially decrease more obviously than those in other months. (4) The spatial correlograms should be derived considering the temporal resolution(hourly, daily, etc) of interest.

• Journal of KIISE:Software and Applications
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• v.34 no.6
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• pp.503-518
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• 2007

Normalization transforms one parent genotype to be consistent with the other before crossover. In this paper, we explain how normalization alleviates the difficulties caused by non-synonymously redundant encodings in genetic algorithms. We define the encodings with maximally non-synonymous property and prove that the encodings induce uncorrelated search spaces. Extensive experiments for a number of problems show that normalization transforms the uncorrelated search spaces to correlated ones and leads to significant improvement in performance.

• Journal of the Korean Society of Radiology
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• v.7 no.5
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• pp.347-352
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• 2013

In this study, we analyze the cross correlation between Gamma exposure rates and Rainfall, Hours of daylight, Average wind speed using cross-correlation coefficient ${\rho}_{DCCA}$ and DCCA cross-correlation coefficient(DCCA ${\rho}$) method. Our data are measured simultaneous in Gangneung regional. First, we find the ${\rho}_{DCCA}$ between Gamma exposure rates and Rainfall is Day(3~7days) 0.57~0.48, Month(30days) 0.39, Season(90days) 0.34, Year(360days) 0.26, between Gamma exposure rates and Hours of daylight is Day -0.20~-0.23, Month -0.22, Season -0.17, Year -0.13, between Gamma exposure rates and Average wind speed is Day -0.10~-0.12, Month -0.11, Season -0.05, Year -0.05. Second, our finding is cross- correlation between Gamma exposure rates and Rainfall, is no cross-correlation between Gamma exposure rates and Hours of daylight, Average wind speed.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.2
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• pp.69-78
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• 2010

The hydrological-geomorphic character is closely related with runoff in basin. A development of GIS greatly helps investigating about mechanism between theirs. We analyze local slope and hillslope length which are related with hydrological response. But variation of these geomorphic variables has very wide range at each pixel. So there's a limit as to use directly. Therefore we investigate a relation between hydrological response and distributed geomorphic variables according to statistical character of distributed map considering spatial correlation. As a result, the local slope affects peak discharge, and the hillslope length affects peak discharge and time, mean and variance of hydrological response. Henceforth these hydrological-geomorphic analyze methods can be improved that hydrology response is directly analogized with DEM data.

• Journal of Korean Tunnelling and Underground Space Association
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• v.15 no.1
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• pp.13-24
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• 2013

Various elastic wave-based site investigation methods have been used to characterize subsurface ground because the dynamic properties can be correlated with various geotechnical parameters. Although the inherent spatial variability of the geotechnical parameters affects the P-wave propagation characteristics, ground heterogeneity has not been considered as an influential factor. Thus, the effect of heterogeneous ground on the travel-time shift and wavefront characteristics of elastic waves through stochastic numerical analyses is investigated in this study. The effects of the relative correlation lengths and relative propagation distances on the travel-time shift of P-waves considering various intensities of ground heterogeneity were investigated. Heterogeneous ground fields of stiffness (e.g., the coefficient of variation = 10 ~ 40%) were repeatedly realized in numerical finite difference grids using the turning band method. Monte Carlo simulations were undertaken to simulate P-wave propagation in heterogeneous ground using a finite difference method-based numerical approach. The results show that the disturbance of the wavefront becomes more significant with stronger heterogeneity and induces travel-time delays. The relative correlation lengths and propagation distances are systematically related to the travel-time shift.