• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.3
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• pp.73-82
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• 2021

Rural roads are the significant infrastructure for developing and managing the rural areas, hence the utilization of the remote sensing datasets for managing the rural roads is necessary for expanding the rural transportation infrastructure and improving the life quality of the rural residents. In this research, the two different methods such as image classification and image segmentation were compared for mapping the rural road based on the given high-resolution satellite image acquired in the rural areas. In the image classification method, the deep learning with the multiple neural networks was employed to the given high-resolution satellite image for generating the object classification map, then the rural roads were mapped by extracting the road objects from the generated object classification map. In the image segmentation method, the multiresolution segmentation was employed to the same satellite image for generating the segment image, then the rural roads were mapped by merging the road objects located on the rural roads on the satellite image. We used the 100 checkpoints for assessing the accuracy of the two rural roads mapped by the different methods and drew the following conclusions. The image segmentation method had the better performance than the image classification method for mapping the rural roads using the give satellite image, because some of the rural roads mapped by the image classification method were not identified due to the miclassification errors occurred in the object classification map, while all of the rural roads mapped by the image segmentation method were identified. However some of the rural roads mapped by the image segmentation method also had the miclassfication errors due to some rural road segments including the non-rural road objects. In future research the object-oriented classification or the convolutional neural networks widely used for detecting the precise objects from the image sources would be used for improving the accuracy of the rural roads using the high-resolution satellite image.

• Journal of KIISE
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• v.41 no.10
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• pp.762-773
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• 2014

As the web of data is increasingly producing large RDFS datasets, it becomes essential in building scalable reasoning engines over large triples. There have been many researches used expensive distributed framework, such as Hadoop, to reason over large RDFS triples. However, in many cases we are required to handle millions of triples. In such cases, it is not necessary to deploy expensive distributed systems because logic program based reasoners in a single machine can produce similar reasoning performances with that of distributed reasoner using Hadoop. In this paper, we propose a scalable RDFS reasoner using logical programming methods in a single machine and compare our empirical results with that of distributed systems. We show that our logic programming based reasoner using a single machine performs as similar as expensive distributed reasoner does up to 200 million RDFS triples. In addition, we designed a meta data structure by decomposing the ontology triples into separate sectors. Instead of loading all the triples into a single model, we selected an appropriate subset of the triples for each ontology reasoning rule. Unification makes it easy to handle conjunctive queries for RDFS schema reasoning, therefore, we have designed and implemented RDFS axioms using logic programming unifications and efficient conjunctive query handling mechanisms. The throughputs of our approach reached to 166K Triples/sec over LUBM1500 with 200 million triples. It is comparable to that of WebPIE, distributed reasoner using Hadoop and Map Reduce, which performs 185K Triples/sec. We show that it is unnecessary to use the distributed system up to 200 million triples and the performance of logic programming based reasoner in a single machine becomes comparable with that of expensive distributed reasoner which employs Hadoop framework.

• Journal of the Korea Society of Computer and Information
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• v.25 no.1
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• pp.63-69
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• 2020

In this paper, we propose an indoor positioning technique using the landmark based on relative Access Point (AP) signal strengths. The absolute values of AP signals are used to conventional indoor positioning technologies, but they may be different because of the difference of the measuring device, the measuring environment, and the timing of the measurements. However, we found the fact that the flow of the AP's RSSI in certain places shows almost constant patterns. Based on theses characteristics, we identify the relative strength between the APs and store the certain places as landmarks where they show certain patterns. Once the deployment of the landmark map is complete, system calculate position of user using the IMU sensor of smartphone and calibrate it with stored landmarks. Our system shows 75.2% improvement over technology that used only sensors, and 39.6% improvement over technology that used landmarks that were selected with absolute values.

• Journal of Korea Multimedia Society
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• v.10 no.8
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• pp.997-1003
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• 2007

H.264/AVC enhances inter-frame coding performance adopting new intra and inter mode prediction in inter-frame motion prediction. H.264 encoder provides performance superior to existing standards with the prediction modes. Instead of enhanced performance, however, both predictions increase complexity of encoder and demand a lot of coding time. This paper proposes a method using local statistics of resultant intra mode, my and mode map to predict inter mode. There are relationship between intra and inter mode, and we can predict inter mode using neighboring macroblocks' resultant mode and motion vector according to the contents of frame. The experimental results show that the proposed algorithm reduces encoding time by 31% on average with a negligible loss of PSNR and bitrate.

• Geophysics and Geophysical Exploration
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• v.17 no.3
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• pp.171-178
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• 2014

Aeromagnetic survey generally require much more pre-processing steps than that of common land survey due to several complex and cumbersome steps included in pre-processing stage. Therefore it is desirable to use specific processing tool especially based on graphic user interface. For this purpose, aeromagnetic pre-processing software based on graphic user interface under the Windows environment, called $KMagLevelling^{TM}$ was developed and briefly introduced. In an aspect of its user-friendliness and originality, three noticeable features of $KMagLevelling^{TM}$ are summarized as the following (1) function of representation and handling for large amount of aeromagnetic data set as a visualization in the form of flight-path (2) function of selective exclusion of unwanted data by using survey area information expressed as polygon, and (3) function of selective removal processing for the irregular flight-path data acquired within the entire survey area by implementing the segmentation of flight-path technique.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.1D
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• pp.153-160
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• 2009

In relation to digital elevation model(DEM) production using high resolution satellite imagery, existing studies present that DEM accuracy differently show according to land cover property. This study therefore proposes auto-selection method of window size for correlation matching according to land cover property of IKONOS Geo-level stereo image. For this, land cover classified image is obtained by IKONOS color image with four bands. In addition, correlation-coefficients are computed at regular intervals in pixels of the window-search area to shorten of matching time. As the results, DEM by the proposed method showed more accurate than DEM using the fixed window-size matching. We estimate that accuracy of the proposed DEM improved more than DEM by digital map and ERDAS in agricultural land.

• Korean Journal of Remote Sensing
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• v.31 no.5
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• pp.449-459
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• 2015

The conventional National Forest Inventory(NFI)-based forest carbon stock estimation method is suitable for national-scale estimation, but is not for regional-scale estimation due to the lack of NFI plots. In this study, for the purpose of regional-scale carbon stock estimation, we created grid-based forest carbon stock maps using spatial ancillary data and two types of up-scaling methods. Chungnam province was chosen to represent the study area and for which the $5^{th}$ NFI (2006~2009) data was collected. The first method (method 1) selects forest type map as ancillary data and uses regression model for forest carbon stock estimation, whereas the second method (method 2) uses satellite imagery and k-Nearest Neighbor(k-NN) algorithm. Additionally, in order to consider uncertainty effects, the final AGB carbon stock maps were generated by performing 200 iterative processes with Monte Carlo simulation. As a result, compared to the NFI-based estimation(21,136,911 tonC), the total carbon stock was over-estimated by method 1(22,948,151 tonC), but was under-estimated by method 2(19,750,315 tonC). In the paired T-test with 186 independent data, the average carbon stock estimation by the NFI-based method was statistically different from method2(p<0.01), but was not different from method1(p>0.01). In particular, by means of Monte Carlo simulation, it was found that the smoothing effect of k-NN algorithm and mis-registration error between NFI plots and satellite image can lead to large uncertainty in carbon stock estimation. Although method 1 was found suitable for carbon stock estimation of forest stands that feature heterogeneous trees in Korea, satellite-based method is still in demand to provide periodic estimates of un-investigated, large forest area. In these respects, future work will focus on spatial and temporal extent of study area and robust carbon stock estimation with various satellite images and estimation methods.

• Investigative Magnetic Resonance Imaging
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• v.16 no.1
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• pp.6-15
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• 2012

Purpose : The objective of this study was to develop background gradient correction method using excitation pulse profile compensation for accurate fat and $T_2{^*}$ quantification in the liver. Materials and Methods: In liver imaging using gradient echo, signal decay induced by linear background gradient is weighted by an excitation pulse profile and therefore hinders accurate quantification of $T_2{^*}$and fat. To correct this, a linear background gradient in the slice-selection direction was estimated from a $B_0$ field map and signal decays were corrected using the excitation pulse profile. Improved estimation of fat fraction and $T_2{^*}$ from the corrected data were demonstrated by phantom and in vivo experiments at 3 Tesla magnetic field. Results: After correction, in the phantom experiments, the estimated $T_2{^*}$ and fat fractions were changed close to that of a well-shimmed condition while, for in vivo experiments, the background gradients were estimated to be up to approximately 120 ${\mu}T/m$ with increased homogeneity in $T_2{^*}$ and fat fractions obtained. Conclusion: The background gradient correction method using excitation pulse profile can reduce the effect of macroscopic field inhomogeneity in signal decay and can be applied for simultaneous fat and iron quantification in 2D gradient echo liver imaging.

• Spatial Information Research
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• v.10 no.1
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• pp.45-59
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• 2002

The author argues that the current Government Information System for river water quality appears to be non-user friendly due to lack of the cartographic representation for the field monitoring data. Acknowledging these constraints, an operational, user-friendly information system has been developed by combining Internet technology with GIS. A digital map for water quality has been generated by overlaying monitoring data on existing cartographic data such as road, topography and administrative boundary etc. A user interface was designed to address the need to querry the large spatial databases by non-GIS and non-environmental experts. The system has been checked experimentally and enabled the users to querry data required simply. And detailed visual maps for water quality can be generated over large areas quickly and easily. A visual mapping system for water quality was developed by reframing the monitoring data as graphic symbols and it was ideally suited to exploring area-wide water quality at a user-friendly manner due to extensibility and scalability along the various survey points. This system based on Web GIS could be accessed anywhere if internet is available. It would play a crucial role in improving the quality of public information service if it is operationally introduced into the Government since the highly user-friendly interface provides a completely new means for disseminating information far water pollution in a visual and interactive manner to the general public.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.24 no.5
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• pp.417-424
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• 2006

Despite of many researches related to create 3D digital maps, it is still time-consuming and costly because a large part of 3D digital mapping is conducted manually. To circumvent this limitation, we proposed methodologies to create 3D digital maps with 3D symbols automatically. For this purpose, firstly, the 3D symbol library to represent 3D objects as 3D symbols was constructed. In this library, we stored the attribute and geometry information of 3D objects which define types and shapes of symbols respectively. These information were used to match 3D objects with 3D symbols and extracted from 2D digital maps and LiDAR(Light Detection and Ranging) data. Then, to locate 3D symbols into a base map automatically, we used predefined parameters such as the size, the height, the rotation angle and the center of gravity of 3D objects which are extracted from LiDAR data. Finally, the 3D digital map in urban area was constructed and the results were tested. Through this research, we can identify that the developed algorithms can be used as effective techniques for 3D digital mapping.