• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.84-88
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• 1999

Recently, the necessity of accessing spatial data from remote computer via network has been increased as distributed spatial data have been increased due to their size and cost. Many methods have been used in recent years for transferring spatial data, such as socket, CORBA, HTTP, RPC, FTP, etc. In this paper, we propose spatial clearinghouse components to access distributed spatial data sources via CORBA and Internet. The spatial clearinghouse components are defined as OLE/COM components that enable users to access spatial data that meet their requests from remote computer. For reusability, we design the spatial clearinghouse with UML and implement it as a set of components. In order to enhance interoperability among different platforms in distributed computing environment, we adopt international standards and open architecture such as CORBA, HTTB, and OpenGIS Simple Features Specifications. There are two kinds of spatial clearinghouse: CORBA-based spatial clearinghouse and Internet-based spatial clearinghouse. The CORBA-based spatial clearinghouse supports COM-CORBA bridge to access spatial data from remote data providers that satisfy the OpenGIS Simple Features Specification for OLE/COM using COM and CORBA interfaces. The Internet-based spatial clearinghouse provides Web-service components to access spatial data from remote data providers using Web-browser.

• ETRI Journal
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• v.45 no.1
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• pp.131-137
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• 2023

The phase reconstruction process in digital holographic microscopy involves a trade-off between the phase error and the high-spatial-frequency components. In this reconstruction process, if the narrow region of the sideband is windowed in the Fourier domain, the phase error from the DC component will be reduced, but the high-spatial-frequency components will be lost. However, if the wide region is windowed, the 3D profile will include the high-spatial-frequency components, but the phase error will increase. To solve this trade-off, we propose the high-variance pixel averaging method, which uses the variance map of the reconstructed depth profiles of the windowed sidebands of different sizes in the Fourier domain to classify the phase error and the high-spatial-frequency components. Our proposed method calculates the average of the high-variance pixels because they include the noise from the DC component. In addition, for the nonaveraged pixels, the reconstructed phase data created by the spatial frequency components of the widest window are used to include the high-spatialfrequency components. We explain the mathematical algorithm of our proposed method and compare it with conventional methods to verify its advantages.

• Journal of Biosystems Engineering
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• v.39 no.4
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• pp.377-388
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• 2014

Purpose: Determining the spatial structure of data is important in understanding within-field variability for site-specific crop management. An understanding of the spatial structures present in the data may help illuminate interrelationships that are important in subsequent explanatory analyses, especially when site variables are correlated or are a combined response to multiple causative factors. Methods: In this study, correlation, principal component analysis, and single and nested variogram models were applied to soil electrical conductivity and chemical property data of two fields in central Missouri, USA. Results: Some variables that were highly correlated, or were strongly expressed in the same principal component, exhibited similar spatial ranges when fitted with a single variogram model. However, single variogram results were dependent on the active lag distance used, with short distances (30 m) required to fit short-range variability. Longer active lag distances only revealed long-range spatial components. Nested models generally yielded a better fit than single models for sensor-based conductivity data, where multiple scales of spatial structure were apparent. Gaussian-spherical nested models fit well to the data at both short (30 m) and long (300 m) active lag distances, generally capturing both short-range and long-range spatial components. As soil conductivity relates strongly to profile texture, we hypothesize that the short-range components may relate to the scale of erosion processes, while the long-range components are indicative of the scale of landscape morphology. Conclusion: In this study, we investigated the effect of changing active lag distance on the calculation of the range parameter. Future work investigating scale effects on other variogram parameters, including nugget and sill variances, may lead to better model selection and interpretation. Once this is achieved, separation of nested spatial components by factorial kriging may help to better define the correlations existing between spatial datasets.

• Journal of the Korean Statistical Society
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• v.29 no.2
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• pp.231-245
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• 2000

This paper considers a linear regression model with space and time data in where the disturbances follow spatially correlated error components. We provide the best linear unbiased predictor for the one way error components. We provide the best linear unbiased predictor for the one way error component model with spatial autocorrelation. Further, we derive two diagnostic test statistics for the assessment of model specification due to spatial dependence and random effects as an application of the Lagrange Multiplier principle.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.24 no.4
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• pp.348-356
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• 2014

To understand the relationship between road-traffic noise and urban components such as population, building, road-traffic and land-use, the city of Cheongju that already has road-traffic noise maps of daytime and nighttime was selected for this study. The whole area of the city is divided into square cells of a uniform size and for each cell, the urban components are estimated. A spatial representative noise level for each cell is determined by averaging out population-weighted facade noise levels for noise exposure population within the cell during nighttime. The relationship between the representative noise level and the urban components is statistically modeled at the cell level. Specially, we introduce a spatial auto regressive model and a spatial error model that turns out to explain above 85 % of the noise level. These findings and modeling methods can be used as a preliminary tool for environmental planning and urban design in modern cities in consideration of noise exposure.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1131-1133
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• 2003

Spatial image processing software requires various user interactions to make a plan, prepare necessary data such as images, vectors, ancillary data and user-defined data, execute functions according to pre-defined procedures, analyze and store the results. In this manner, overall processes are controlled by user interactions. In this paper, we propose visual modeler which has the automated spatial image processing technique to minimize user interactions and re -use repeatable procedure. The proposed visual modeler is designed to use inter-operable components proposed by OpenGIS consortium as well as conventional COM components.

• Korean Journal of Remote Sensing
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• v.33 no.1
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• pp.25-35
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• 2017

Spatial downscaling with fine resolution auxiliary variables has been widely applied to predict precipitation at fine resolution from coarse resolution satellite-based precipitation products. The spatial downscaling framework is usually based on the decomposition of precipitation values into trend and residual components. The fine resolution auxiliary variables contribute to the estimation of the trend components. The main focus of this study is on quantitative analysis of impacts of trend component estimates on predictive performance in spatial downscaling. Two regression models were considered to estimate the trend components: multiple linear regression (MLR) and geographically weighted regression (GWR). After estimating the trend components using the two models,residual components were predicted at fine resolution grids using area-to-point kriging. Finally, the sum of the trend and residual components were considered as downscaling results. From the downscaling experiments with time-series Tropical Rainfall Measuring Mission (TRMM) 3B43 precipitation data, MLR-based downscaling showed the similar or even better predictive performance, compared with GWR-based downscaling with very high explanatory power. Despite very high explanatory power of GWR, the relationships quantified from TRMM precipitation data with errors and the auxiliary variables at coarse resolution may exaggerate the errors in the trend components at fine resolution. As a result, the errors attached to the trend estimates greatly affected the predictive performance. These results indicate that any regression model with high explanatory power does not always improve predictive performance due to intrinsic errors of the input coarse resolution data. Thus, it is suggested that the explanatory power of trend estimation models alone cannot be always used for the selection of an optimal model in spatial downscaling with fine resolution auxiliary variables.

• Korean Journal of Remote Sensing
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• v.21 no.3
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• pp.213-219
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• 2005

In the Korean seas, Sea Surface Temperature (SST) and Thermal ronts (TF) were analyzed temporally and spatially during 8 years from 1993 to 2000 using NOAA/AVHRR MCSST. In the application of EOF analysis for SST, the variance of the 1st mode was 97.6%. Temporal components showed annual variations, and spatial components showed that where it is closer to continents, the SST variations are higher. Temporal components of the 2nd mode presented higher values of 1993, 94 and 95 than those of other years. Although these phenomena were not remarkable, they could be considered ELNI . NO effects to the Korean seas as the time was when ELNI . NO occurred. The Sobel Edge Detection Method (SEDM) delineated four fronts: the Subpolar Front (SPF) separating the northern and southern parts of the East Sea; the Kuroshio Front (KF) in the East China Sea, the South Sea Coastal Front (SSCF) in the South Sea, and the Tidal Front (TDF) in the West Sea. TF generally occurred over steep bathymetry slopes, and spatial components of the 1st mode in SST were bounded within these frontal areas. EOF analysis of SST gradient values revealed the temporal and spatial variations of the TF. The SPF and SSCF were most intense in March and October; the KF was most significant in March and May.

• Journal of Korea Spatial Information System Society
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• v.1 no.2 s.2
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• pp.33-46
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• 1999

In distributed computing platforms like CORBA, wrappers are used to integrate heterogeneous systems or databases. A spatial data provider is one of the wrappers because it provides clients with uniform access interfaces to diverse data sources. The individual implementation of spatial data providers for each of different data sources is not efficient because of redundant coding of the wrapper modules. This paper presents a new architecture of the spatial data provider which consists of two layered objects : independent wrapper components and dependent wrapper components. Independent wrapper components would be reused for implementing a new data provider for a new data source, which dependent wrapper components should be newly coded for every data source. This paper furthermore discussed the issues of implementing the representation of query results in the middleware. There are two methods of keeping query results in the middleware. One is to keep query results as non-CORBA objects and the other is to transform query results into CORBA objects. The evaluation of the above two methods shows that the cost of making CORBA objects is very expensive.

• ETRI Journal
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• v.41 no.1
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• pp.23-31
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• 2019

Since the late 20th century, there has been rapid development in the display industry. Only 30 years ago, we used big cathode ray tube displays with poor resolution, but now most people use televisions or smartphones with very high-quality displays. People now want images that are more realistic, beyond the two-dimensional images that exist on the flat screen, and digital holography-one of the next-generation displaysis expected to meet that need. The most important parameter that determines the performance of a digital hologram is the pixel pitch. The smaller the pixel pitch, the higher the level of hologram implementation possible. In this study, we fabricated the world-smallest $3-{\mu}m$-pixel-pitch holographic backplane based on the spatial light modulator technology. This panel could display images with a viewing angle of more than $10^{\circ}$. Furthermore, a comparative study was conducted on the fabrication processes and the corresponding holographic results from the large to the small pixel-pitch panels.