• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.150-152
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• 2003

Laser scanned lidar data record 3D surface information in detail. Exploring valuable spatial information from lidar data is a prerequisite task for its applications, such as DEM generation and 3D building model reconstruction. However, the inherent spatial information is implicit in the abundant, densely and randomly distributed point cloud. This paper proposes a novel method to organize point cloud data, so that further analysis or feature extraction can proceed based on a well organized data model. The principle of the proposed algorithm is to segment point cloud into 3D planes. A split and merge segmentation based on the octree structure is developed for the implementation. Some practical airborne and ground lidar data are tested for demonstration and discussion. We expect this data organization could provide a stepping stone for extracting spatial information from lidar data.

• Journal of Korea Spatial Information System Society
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• v.7 no.3 s.15
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• pp.55-65
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• 2005

This paper presents a bulk insertion technique for efficiently inserting data items. Traditional moving object database focused on efficient query processing that happens mainly after index building. Traditional index structures rarely considered disk I/O overhead for index rebuilding by inserting data items. This paper, to solve this problem, describes a new bulk insertion technique which efficiently induces the current positions of moving objects and reduces update cost greatly. This technique uses buffering technique for bulk insertion in spatial index structures such as R-tree. To analyze split or merge node, we add a secondary index for information management on leaf node of primary index. And operations are classified to reduce unnecessary insertion and deletion. This technique decides processing order of moving objects, which minimize split and merge cost as a result of update operations. Experimental results show that this technique reduces insertion cost as compared with existing insertion techniques.

• Journal of Korea Spatial Information System Society
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• v.8 no.3
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• pp.105-115
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• 2006

A sensor network which uses DCS(Data-Centric Storage) stores the same data into the same sensor node. Thus it has a hot spot problem when the sensor network grows and the same data arise frequently. In the past researches of the sensor network using DCS, the hot spot problem caused by growing the sensor network was ignored because they only concentrated on managing stored sensor data efficiently. In this paper, we proposed a non-equal region split method that supports efficient scalability on storing multi-dimensional sensor data. This method can reduce the storing cost, as the sensor network is growing, by dividing whole space into regions which have the same number of sensor nodes according to the distribution of sensor nodes, and storing and managing sensor data within each region. Moreover, this method can distribute the energy consumption of sensor nodes by increasing the number of regions according to the size of the sensor network, the number of sensor nodes within the sensor network, and the quantity of sensor data. Therefore it can help to increase the life time and the scalability of the sensor network.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.50 no.4
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• pp.551-555
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• 2014

ADCPs have been widely used to estimate the dynamic characteristics and biomass of sound scattering layers (SSLs), and swimming speed of fish schools for analyzing SSLs spatial distribution and/or various behavior patterns. This result showed that the verification of the mean volume backscattering strength (MVBS or averaged SV, dB) acquired by the ADCP would be necessary for a quantitative analysis on the spatial distribution and the biomass estimation of the SSLs or fish school when ADCP is used for estimating their biomass. In addition, the calibrated sphere method was used to verify values of each MVBS obtained from 4 beams of ADCP (153.6 kHz) on the base of 3 frequencies (38, 120, 200 kHz) of Scientific echo sounder's split beam system. Then, the measured SV values were compared and analyzed in its Target Strength (TS, dB) values estimated by a theoretical acoustic scattering model.

• Journal of Environmental Health Sciences
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• v.31 no.6
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• pp.489-497
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• 2005

A numerical method for the mathematical water modeling in 2-dimensional flow has been developed. The model based on a split operator technique, in which, the advection term is calculated using the upwind scheme. The diffusion term is one- dimensionalized and calculated using Crank-Nicholson's implicit finite difference scheme to reduce the numerical errors from large time steps and variable spacings. It also provides a relatively simple and economic method for more accurate simulation of pollutant dispersion. Water depths and flow velocities in the Boreyong reservoir during the normal water periods were predicted by numerical experiments with a 2-dimensional flow model so as to provide current field data for the study of advection and diffusion of pollutants. Developed 2-dimensional water quality model is applied to Boreyong reservoir to simulate a spatial and periodical changes of water quality.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.4
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• pp.405-412
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• 2012

After the spatial discrepancy between SLI(Street-Level Imagery) and vector map is removed by their conflation, the corresponding building regions can be found based on SLI parameters. The building region correspondence, however, is not perfect even after the conflation. This paper aims to improve the correspondence of building regions by region splitting of an SLI. Regions are initialized by the seed lines, projection of building objects onto SLI scene. First, sky images are generated by filtering, segmentation, and sky region detection. Candidates for split lines are detected by edge detector, and then images are splitted into building regions by optimal split lines based on color difference and sky existence. The experiments demonstrated that the proposed region splitting method had improved the accuracy of building region correspondence from 83.3% to 89.7%. The result can be utilized effectively for enhancement of SLI services.

• Journal of Korea Spatial Information System Society
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• v.9 no.3
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• pp.35-50
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• 2007

In a data centric sensor network, a sensor node to store data is determined by the measured data value of each sensor node. Therefore, if the same data occur frequently, the energy of the sensor node to store the data is exhausted quickly due to the concentration of loads. And if the sensor network is extended, the communication cost for storing data and processing queries is increased, since the length of the routing path for them is usually in the distance. However, the existing researches that generally focus on the efficient management of data storing can not solve these problems efficiently. In this paper, we propose a NUNS(Non-Uniform Network Split) method that can distribute loads of sensor nodes and decrease the communication cost caused by the sensor network extension. By dividing the sensor network into non-uniform partitions that have the minimum difference in the number of sensor nodes and the splitted area size and storing the data which is occurred in a partition at the sensor nodes within the partition, the NUNS can distribute loads of sensor nodes and decrease the communication cost efficiently. In addition, by dividing each partition into non-uniform zones that have the minimum difference in the splitted area size as many as the number of the sensor nodes in the partition and allocating each of them as the processing area of each sensor node, the NUNS can protect a specific sensor node from the load concentration and decrease the unnecessary routing cost.

• Ocean and Polar Research
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• v.28 no.3
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• pp.225-236
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• 2006

Seagrass meadows are considered as critical habitats for a wide variety of marine organisms in coastal and estuarine ecosystems. In many cases, studies on the spatial/temporal distribution of seagrass have depended on direct observations using SCUBA diving. As an alternative method fur studying seagrass distribution, an application of hydroacoustic technique has been assessed for mapping seagrass distribution in Dongdae Bay, on the south coast of Korea, in September 2005. Data were collected using high frequency transducer (420 kHz split-beam), which was installed with towed body system. The system was linked to DGPS to make goo-referenced data. Additionally, in situ seagrass distribution has been observed using underwater cameras and SCUBA diving at four stations in order to compare with acoustic data. Acoustic survey was conducted along 23 transects with 3-4 blot ship speed. Seagrass beds were vertically limited to depths less than 3.5m and seagrass height ranged between 55 and 90cm at the study sites. Dense seagmss beds were mainly found at the entrance of the bay and at a flat area around the center of the bay. Although the study area was a relatively small, the vertical and spatial distributions of the seagrass were highly variable with bathymetry and region. Considering dominant species, Zostera marina L., preliminary estimation of seagrass biomass with acoustic and direct sampling data was approximately $56.55g/m^2$, and total biomass of 104 tones (coefficient variation: 25.77%) was estimated at the study area. Hydroacoustic method provided valuable information to understand distribution pattern and to estimate seagrass biomass.

• Journal of Korea Spatial Information System Society
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• v.6 no.1 s.11
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• pp.19-29
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• 2004

Recently, studies have been performed to improve the cache performance of the R-Tree in main memory. A general mothed to improve the cache performance of the R-Tree is to reduce size of an entry so that a node can store more entries and fanout of it can increase. However, this method generally requites additional process to reduce information of entries and do not support incremental updates. In addition, the cache miss always occurs on moving between a parent node and a child node. To solve these problems efficiently, this paper proposes and evaluates the PR-Tree that is an extended R-Tree indexing method using prefetching in main memory. The PR-Tree can produce a wider node to optimize prefetching without additional modifications on the R-Tree. Moreover, the PR-Tree reduces cache miss rates that occur in moving between a parent node and a child node. In our simulation, the search performance, the update performance, and the node split performance of the PR-Tree improve up to 38%. 30%, and 67% respectively, compared with the original R-Tree.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2003.04a
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• pp.291-296
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• 2003

This study estimated surface temperature by using split-window technique and NOAA/AVHRR data was used. For surface monitoring, cloud masking procedure was carried out using threshold algorithm. The daily maximum air temperature is estimated by multiple regression method using independent variables such as satellite-derived surface temperature, EDD, and latitude. When the EDD data added, the highest correlation shown. This indicates that EDD data is the necessary element for estimation of the daily maximum air temperature. We derived correlation and experience equation by three approaching method to estimate daily maximum air temperature. 1) non-considering landcover method as season, 2) considering landcover method as season, and 3) just method as landcover. The last approaching method shows the highest correlation. So cross-validation procedure was used in third method for validation of the estimated value. For all landcover type 5, the results using the cross-validation procedure show reasonable agreement with measured values(slope=0.97, intercept=-0.30, R$^2$=0.84, RMSE=4.24$^{\circ}C$). Also, for all landcover type 7, the results using the cross-validation procedure show reasonable agreement with measured values(slope=0.993, Intercept=0.062, R$^2$=0.84, RMSE=4.43$^{\circ}C$).