• Journal of the Korea Society for Simulation
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• v.2 no.1
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• pp.125-133
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• 1993

A statistical method is described for estimation of the unknown constants in a theory using both of the computer simulation data and the real experimental data, The best linear unbiased predictor based on a spatial linear model is fitted from the computer simulation data alone. Then nonlinear least squares estimation method is applied to the real experimental data using the fitted prediction model as if it were the true simulation model. An application to the computational nuclear fusion devices is presented, where the nonlinear least squares estimates of four transport coefficients of the theoretical nuclear fusion model are obtained.

• Journal of Korean Society of Rural Planning
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• v.21 no.2
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• pp.115-126
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• 2015

Although the frequency of use for community facilities in rural villages is growing as well as the importance of the facilities for urban-rural exchange is being highlighted, study on spatial location-analysis of the facilities for such multi-purpose is not so much. This study aims to find the spatial distribution forms of community facilities in rural villages such as community center and rural-pocket park through location-analysis, in order to provide available data for selecting location in the future. As the study area, Sojeong-myeon, Sejong Special City was selected. This study conducted GIS analysis for criteria of the location-evaluation model developed in this study. This study introduced the concept of time-distance for accessibility analysis. This paper also used linear-consecutive scoring method(LCSM) as a scoring method of criteria and Analytic Hierarchy Process(AHP) method for weighting values of criteria. The application results showed that the new model can generate the intensity of community facilities according to spatial distribution and accessibility from cities to the facilities.

• Journal of information and communication convergence engineering
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• v.13 no.2
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• pp.105-112
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• 2015

The estimation of missing sensor values is an important problem in sensor network applications, but the existing approaches have some limitations, such as the limitations of application scope and estimation accuracy. Therefore, in this paper, we propose a new estimation model based on a spatial-temporal correlation analysis (STCAM). STCAM can make full use of spatial and temporal correlations and can recognize whether the sensor parameters have a spatial correlation or a temporal correlation, and whether the missing sensor data are continuous. According to the recognition results, STCAM can choose one of the most suitable algorithms from among linear interpolation algorithm of temporal correlation analysis (TCA-LI), multiple regression algorithm of temporal correlation analysis (TCA-MR), spatial correlation analysis (SCA), spatial-temporal correlation analysis (STCA) to estimate the missing sensor data. STCAM was evaluated over Intel lab dataset and a traffic dataset, and the simulation experiment results show that STCAM has good estimation accuracy.

• 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 Korea Water Resources Association
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• v.45 no.9
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• pp.875-885
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• 2012

In this study, the total precipitation amount in Jeju Island was estimated with the simulated precipitation for ungauged stations missing precipitation data using the spatial precipitation analysis. The missing data were generated through the modified multiple linear regression in this study, and the analysis of spatial precipitation was conducted with the PRISM(Parameter-elevation Regression on Independent Slope Model). The generated data with modified multiple linear regression model have similar pattern with original data. Thus, the model in this study shows good applicability to estimate the missing data. The difference of annual average precipitation between Case 1 (original data) and Case 2 (modified data) appears very small ratio which is about 1.5%. However, the difference of annual average precipitation according to elevation shows the large ratio up to 37.4%. As the results, the method of estimating missing data in this study would be useful to calculate the total precipitation amount at the low station density area and the places with the high spatial variation of precipitation.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.4
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• pp.65-76
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• 2017

The purpose of this study was to present a prediction model that reflects crime risk area analysis, including factors and spatial characteristics, as a precursor to preparing an alternative plan for crime prevention and design. This analysis of criminal cases in high-risk areas revealed clusters in which approximately 25% of the cases within the study area occurred, distributed evenly throughout the region. This means that using a multiple linear regression model might overestimate the crime rate in some regions and underestimate in others. It also suggests that the number of deserted houses in an analyzed region has a negative relationship with the dependent variable, based on the multiple linear regression model results, and can also have different influences depending on the region. These results reveal that closure signs in a study area affect the dependent variable differently, depending on the region, rather than a simple or direct relationship with the dependent variable, as indicated by the results of the multiple linear regression model.

• Journal of Korean Association for Spatial Structures
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• v.3 no.4 s.10
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• pp.85-92
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• 2003

In this work, a finite element model is presented for geometrically non-linear analysis of shell structures. Finite element by using a three-node flat triangular shell element is formulated. The non-linear incremental equilibrium equations are formulated by using an updated Lagrangian formulation and the solutions are obtained with the incremental/iterative Newton-Raphson method and arc length method. Some of results are presented for shell structures. The obtained results are in good agreement with the results available in existing literature.

• The Korean Journal of Applied Statistics
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• v.26 no.1
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• pp.93-109
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• 2013

Geostatistical data or point-referenced data have the information on the monitoring stations of interest where the observations are measured. Practical geostatistical data are obtained from a wide variety of observational monitoring networks that are mainly operated by the Korean government. When we analyze geostatistical data and predict the expectations at unobservable locations, we can improve the reliability of the prediction by utilizing some relevant spatial data obtained from different observational monitoring networks and blend them with the measurements of our main interest. In this paper, we consider the hierarchical spatial linear model that enables us to link spatial variables from different resources but with similar patterns and guarantee the precision of the prediction. We compare the proposed model to a classical linear regression model and simple kriging in terms of some information criteria and one-leave-out cross-validation. Real application deals with Sulfur Dioxide($SO_2$) measurements from the urban air pollution monitoring network and wind speed data from the surface observation network.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.75-80
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• 2002

The mainly used technique to rectify satellite images with distortion is to develop a mathematical relationship between the pixel coordinates on the image and the corresponding points on the ground. By defining the relationship between two coordinate systems, a polynomial model is designed and various linear transformations are used. These GCP based geometric correction has performed overall plane to plane mapping. In the overall plane mapping, overall structure of a scene is considered, but local variation is discarded. The highly variant height of region is resampled with distortion in the rectified image. To solve this problem this paper proposed the TIN-based rectification on a satellite image. The TIN based rectification is good to correct local distortion, but insufficient to reflect overall structure of one scene. So, this paper shows the experimental result and the analysis of each rectification model. It also describes the relationship GCP distribution and rectification model. We can choose a geometric correction model as the structural characteristic of a satellite image and the acquired GCP distribution.

• Journal of Korean Society for Geospatial Information Science
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• v.15 no.4
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• pp.81-88
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• 2007

The geometry of satellite image captured by linear CCD sensor is different from that of frame camera image. The fact that the exterior orientation parameters for satellite image with linear CCD sensor varies from scan line by scan line, causes the difference of image geometry between frame and linear CCD sensor. Therefore, we need the epipolar geometry for linear CCD image which differs from that of frame camera image. In this paper, we proposed a method of resampling linear CCD satellite image in epipolar geometry under the assumption that image is not formed in perspective projection but in parallel projection, and the sensor model is a 2D affine sensor model based on parallel projection. For the experiment, IKONOS stereo images, which are high resolution linear CCD images, were used and tested. As results, the spatial accuracy of 2D affine sensor model is investigated and the accuracy of epipolar resampled image with RFM was presented.