• Geophysics and Geophysical Exploration
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• v.10 no.3
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• pp.191-202
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• 2007

To identify the detailed attenuation structure in the southern Korean Peninsula, a numerical test was conducted for the Q tomography inversion to be applied to the accumulated dataset until 2005. In particular, the stochastic pointsource ground-motion model (STGM model; Boore, 2003) was adopted for the 2D Q tomography inversion for direct application to simulating the strong ground-motion. Simultaneous inversion of the STGM model parameters with a regional single Q model was performed to evaluate the source and site effects which were necessary to generate an artificial dataset for the numerical test. The artificial dataset consists of simulated Fourier spectra that resemble the real data in the magnitude-distance-frequency-error distribution except replacement of the regional single Q model with a checkerboard type of high and low values of laterally varying Q models. The total number of Q blocks used for the checkerboard test was 75 (grid size of $35{\times}44km^2$ for Q blocks); Q functional form of $Q_0f^{\eta}$ ($Q_0$=100 or 500, 0.0 < ${\eta}$ < 1.0) was assigned to each Q block for the checkerboard test. The checkerboard test has been implemented in three steps. At the first step, the initial values of Q-values for 75 blocks were estimated. At the second step, the site amplification function was estimated by using the initial guess of A(f) which is the mean site amplification functions (Yun and Suh, 2007) for the site class. The last step is to invert the tomographic Q-values of 75 blocks based on the results of the first and second steps. As a result of the checkerboard test, it was demonstrated that Q-values could be robustly estimated by using the 2D Q tomography inversion method even in the presence of perturbed source and site effects from the true input model.

• Korean Journal of Environmental Biology
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• v.39 no.4
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• pp.526-535
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• 2021

Spatial sampling design plays an important role in GIS-based modeling studies because it increases modeling efficiency while reducing the cost of sampling. In the field of agricultural systems, research demand for high-resolution spatial databased modeling to predict and evaluate climate change impacts is growing rapidly. Accordingly, the need and importance of spatial sampling design are increasing. The purpose of this study was to design spatial sampling of paddy fields (11,386 grids with 1 km spatial resolution) in Korea for use in agricultural spatial modeling. A stratified random sampling design was developed and applied in 2030s, 2050s, and 2080s under two RCP scenarios of 4.5 and 8.5. Twenty-five weather and four soil characteristics were used as stratification variables. Stratification and sample allocation were optimized to ensure minimum sample size under given precision constraints for 16 target variables such as crop yield, greenhouse gas emission, and pest distribution. Precision and accuracy of the sampling were evaluated through sampling simulations based on coefficient of variation (CV) and relative bias, respectively. As a result, the paddy field could be optimized in the range of 5 to 21 strata and 46 to 69 samples. Evaluation results showed that target variables were within precision constraints (CV<0.05 except for crop yield) with low bias values (below 3%). These results can contribute to reducing sampling cost and computation time while having high predictive power. It is expected to be widely used as a representative sample grid in various agriculture spatial modeling studies.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.1-21
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• 2012

In recent years, mobile phone has experienced an extremely fast evolution. It is equipped with high-quality color displays, high resolution cameras, and real-time accelerated 3D graphics. In addition, some other features are includes GPS sensor and Digital Compass, etc. This evolution advent significantly helps the application developers to use the power of smart-phones, to create a rich environment that offers a wide range of services and exciting possibilities. To date mobile AR in outdoor research there are many popular location-based AR services, such Layar and Wikitude. These systems have big limitation the AR contents hardly overlaid on the real target. Another research is context-based AR services using image recognition and tracking. The AR contents are precisely overlaid on the real target. But the real-time performance is restricted by the retrieval time and hardly implement in large scale area. In our work, we exploit to combine advantages of location-based AR with context-based AR. The system can easily find out surrounding landmarks first and then do the recognition and tracking with them. The proposed system mainly consists of two major parts-landmark browsing module and annotation module. In landmark browsing module, user can view an augmented virtual information (information media), such as text, picture and video on their smart-phone viewfinder, when they pointing out their smart-phone to a certain building or landmark. For this, landmark recognition technique is applied in this work. SURF point-based features are used in the matching process due to their robustness. To ensure the image retrieval and matching processes is fast enough for real time tracking, we exploit the contextual device (GPS and digital compass) information. This is necessary to select the nearest and pointed orientation landmarks from the database. The queried image is only matched with this selected data. Therefore, the speed for matching will be significantly increased. Secondly is the annotation module. Instead of viewing only the augmented information media, user can create virtual annotation based on linked data. Having to know a full knowledge about the landmark, are not necessary required. They can simply look for the appropriate topic by searching it with a keyword in linked data. With this, it helps the system to find out target URI in order to generate correct AR contents. On the other hand, in order to recognize target landmarks, images of selected building or landmark are captured from different angle and distance. This procedure looks like a similar processing of building a connection between the real building and the virtual information existed in the Linked Open Data. In our experiments, search range in the database is reduced by clustering images into groups according to their coordinates. A Grid-base clustering method and user location information are used to restrict the retrieval range. Comparing the existed research using cluster and GPS information the retrieval time is around 70~80ms. Experiment results show our approach the retrieval time reduces to around 18~20ms in average. Therefore the totally processing time is reduced from 490~540ms to 438~480ms. The performance improvement will be more obvious when the database growing. It demonstrates the proposed system is efficient and robust in many cases.