• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.4
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• pp.469-475
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• 2021

In order to effectively maintain and manage river facilities, on going data collection of associated objects is important. However, the existing data acquisition methods of using a total station, a global navigation satellite system, or a terrestrial laser scanner have limitations in terms of cost/time/manpower when acquiring spatial information data on river facilities distributed over a wide and long area, unlike general facilities. In contrast, a mobile mapping system (MMS), which acquires data while moving its platform, acquires precise spatial information data for a large area in a short time, so it is suitable for use in the maintenance of linear facilities around rivers. As a result of applying a MMS to a research area of 4 km, 184,646,099 points were acquired during a 20-minute data acquisition period, and 378 cross-sections were extracted. By comparing this with computer-drawn river plans, it was confirmed that efficient levee management using a MMS is possible.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.27 no.4
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• pp.211-228
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• 2022

Recent trends in U.S. ocean policy were briefly reviewed through the keywords in the documents from the three consecutive administrative offices of U.S. government since 2010. Many keywords was unchanged since 2010 implying that the confirm foundation of US ocean policy is not easily shaken. Among the administration-specific main drivers, emphases on Coastal and Marine Spatial Planning in 2010, Ocean Mapping in 2019, and the 2021 goal of conserving 30% by 2030 may profoundly affect the directions of U.S. ocean environment conservation policy. Decadal trends and implications in main key words of U.S. ocean policy as are reflected from the documents produced by the above three administrative offices were also shown to affect future perspectives of global ocean environment conservation policy as well as the corresponding Korean policies.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.1
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• pp.768-773
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• 2020

In large-scale land development for the rational use and management of national land resources, the use of geospatial information is essential for the efficient management of projects. Recently, drone LiDAR (Light Detection And Ranging) has attracted attention as an effective geospatial information construction technique for large-scale development areas, such as housing site construction and open-pit mines. Drone LiDAR can be classified into a method using SLAM (Simultaneous Localization And Mapping) technology and a GNSS (Global Navigation Satellite System)/IMU (Inertial Measurement Unit) method. On the other hand, there is a lack of analytical research on the application of drone LiDAR or the characteristics of each method. Therefore, in this study, data acquisition, processing, and analysis using SLAM and GNSS/IMU type drone LiDAR were performed, and the characteristics and utilization of each were evaluated. As a result, the height direction accuracy of drone LiDAR was -0.052~0.044m, which satisfies the allowable accuracy of geospatial information for mapping. In addition, the characteristics of each method were presented through a comparison of data acquisition and processing. Geospatial information constructed through drone LiDAR can be used in several ways, such as measuring the distance, area, and inclination. Based on such information, it is possible to evaluate the safety of large-scale development areas, and this method is expected to be utilized in the future.

• The Korean Journal of Nuclear Medicine
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• v.32 no.3
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• pp.225-237
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• 1998

Purpose: We investigated the statistical methods to compose the functional brain map of human working memory and the principal factors that have an effect on the methods for localization. Materials and Methods: Repeated PET scans with successive four tasks, which consist of one control and three different activation tasks, were performed on six right-handed normal volunteers for 2 minutes after bolus injections of 925 MBq $H_2^{15}O$ at the intervals of 30 minutes. Image data were analyzed using SPM96 (Statistical Parametric Mapping) implemented with Matlab (Mathworks Inc., U.S.A.). Images from the same subject were spatially registered and were normalized using linear and nonlinear transformation methods. Significant difference between control and each activation state was estimated at every voxel based on the general linear model. Differences of global counts were removed using analysis of covariance (ANCOVA) with global activity as covariate. Using the mean and variance for each condition which was adjusted using ANCOVA, t-statistics was performed on every voxel To interpret the results more easily, t-values were transformed to the standard Gaussian distribution (Z-score). Results: All the subjects carried out the activation and control tests successfully. Average rate of correct answers was 95%. The numbers of activated blobs were 4 for verbal memory I, 9 for verbal memory II, 9 for visual memory, and 6 for conjunctive activation of these three tasks. The verbal working memory activates predominantly left-sided structures, and the visual memory activates the right hemisphere. Conclusion: We conclude that rCBF PET imaging and statistical parametric mapping method were useful in the localization of the brain regions for verbal and visual working memory.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.60-62
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• 2003

The concept of a Spatial Data Infrastructure (SDI) has emerged globally to facilitate the transit of spatial information from data producers to a vast and ever-growing community of users. SDI have become very important in determining the way in which spatial data are used throughout an organization, a nation, different regions, and the world. SDI is an initiative intended to create an environment in which all stakeholders can cooperate with each other and interact with technology, to better achieve their objectives at different political / administrative levels. Islamic Republic of Iran began her participation in the Global map project and SDI activities in 1998. In this related, National Cartographic Center (NCC), as the representative of Iran, started the job with identifying the suitable sources of data for creation of those layers stated in the specifications of Global Mapping. NCC started making GIS Users Councils (National & Provincial Councils) for the making National SDI and Local SDI too. Now, NCC is doing some activities to joining its National SDI to Regional and Global SDI. This paper in first section, discuss about SDI as basic point in Information Technology (IT). In second section, SDI situation in IRAN and National Cartographic Center’s roles in realization of future scope of RSDI and GSDI is discussed. (NCC is one of the greatest Map Producer organizations in IRAN). The way that be applied, is analyzing of fundamental points especially Sustainable development, IT and SDI and their complementing policy in Information Society. These include some applications in National, Regional and Global levels.

• Korean Journal of Radiology
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• v.22 no.10
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• pp.1719-1729
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• 2021

Objective: Emphysema and small-airway disease are the two major components of chronic obstructive pulmonary disease (COPD). We propose a novel method of quantitative computed tomography (CT) emphysema air-trapping composite (EAtC) mapping to assess each COPD component. We analyzed the potential use of this method for assessing lung function in patients with COPD. Materials and Methods: A total of 584 patients with COPD underwent inspiration and expiration CTs. Using pairwise analysis of inspiration and expiration CTs with non-rigid registration, EAtC mapping classified lung parenchyma into three areas: Normal, functional air trapping (fAT), and emphysema (Emph). We defined fAT as the area with a density change of less than 60 Hounsfield units (HU) between inspiration and expiration CTs among areas with a density less than -856 HU on inspiration CT. The volume fraction of each area was compared with clinical parameters and pulmonary function tests (PFTs). The results were compared with those of parametric response mapping (PRM) analysis. Results: The relative volumes of the EAtC classes differed according to the Global Initiative for Chronic Obstructive Lung Disease stages (p < 0.001). Each class showed moderate correlations with forced expiratory volume in 1 second (FEV₁) and FEV₁/forced vital capacity (FVC) (r = -0.659-0.674, p < 0.001). Both fAT and Emph were significant predictors of FEV₁ and FEV₁/FVC (R² = 0.352 and 0.488, respectively; p < 0.001). fAT was a significant predictor of mean forced expiratory flow between 25% and 75% and residual volume/total vital capacity (R² = 0.264 and 0.233, respectively; p < 0.001), while Emph and age were significant predictors of carbon monoxide diffusing capacity (R² = 0.303; p < 0.001). fAT showed better correlations with PFTs than with small-airway disease on PRM. Conclusion: The proposed quantitative CT EAtC mapping provides comprehensive lung functional information on each disease component of COPD, which may serve as an imaging biomarker of lung function.

• Korean Journal of Remote Sensing
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• v.29 no.2
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• pp.263-274
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• 2013

In this study we evaluated the hydrological applicability of multi-satellite precipitation estimates. Three high-resolution global multi-satellite precipitation products, the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), the Global Satellite Mapping of Precipitation (GSMaP), and the Climate Precipitation Center (CPC) Morphing technique (CMORPH), were applied to the Coupled Routing and Excess Storage (CREST) model for the evaluation of their hydrological utility. The CREST model was calibrated from 2002 to 2005 and validated from 2006 to 2009 in the Chungju Dam watershed, including two years of warm-up periods (2002-2003 and 2006-2007). Areal-averaged precipitation time series of the multi-satellite data were compared with those of the ground records. The results indicate that the multi-satellite precipitation can reflect the seasonal variation of precipitation in the Chungju Dam watershed. However, TMPA overestimates the amount of annual and monthly precipitation while GSMaP and CMORPH underestimate the precipitation during the period from 2002 to 2009. These biases of multi-satellite precipitation products induce poor performances in hydrological simulation, although TMPA is better than both of GSMaP and CMORPH. Our results indicate that advanced rainfall algorithms may be required to improve its hydrological applicability in South Korea.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.5
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• pp.1198-1212
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• 2013

Network virtualization enables autonomous and heterogeneous Virtual Networks (VNs) to co-exist on a shared physical substrate. In a Network Virtualization Environment (NVE), the fact that individual VNs are underpinned by diverse naming mechanisms brings about an obvious challenge for transparent communication across multiple VN domains due to the complexity of uniquely identifying users. Existing solutions were mainly proposed compatible to Internet paradigm with little consideration of their applications in a virtualized environment. This calls for a scalable and efficient naming framework to enable consistent communication across a large user population (fixed or mobile) hosted by multiple VNs. This paper highlights the underlying technical requirements and presents a scalable Global Naming Framework (GNF), which (1) enables transparent communication across multiple VNs owned by the same or different SPs; (2) supports communication in the presence of dynamics induced from both VN and end users; and (3) greatly reduces the network operational complexity (space and time). The suggested approach is assessed through extensive simulation experiments for a range of network scenarios. The numerical result clearly verifies its effectiveness and scalability which enables its application in a large-scale NVE without significant deployment and management hurdles.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.5
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• pp.423-430
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• 2017

Recently, data analysis research has been carried out using the deep learning technique in various fields such as image interpretation and/or classification. Various types of algorithms are being developed for many applications. In this paper, we propose a precipitation prediction algorithm based on deep learning with high accuracy in order to take care of the possible severe damage caused by climate change. Since the geographical and seasonal characteristics of Korea are clearly distinct, the meteorological factors have repetitive patterns in a time series. Since the LSTM (Long Short-Term Memory) is a powerful algorithm for consecutive data, it was used to predict precipitation in this study. For the numerical test, we calculated the PWV (Precipitable Water Vapor) based on the tropospheric delay of the GNSS (Global Navigation Satellite System) signals, and then applied the deep learning technique to the precipitation prediction. The GNSS data was processed by scientific software with the troposphere model of Saastamoinen and the Niell mapping function. The RMSE (Root Mean Squared Error) of the precipitation prediction based on LSTM performs better than that of ANN (Artificial Neural Network). By adding GNSS-based PWV as a feature, the over-fitting that is a latent problem of deep learning was prevented considerably as discussed in this study.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1934-1935
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• 2011

In order for a service robot to perform several tasks, basically autonomous navigation technique such as localization, mapping, and path planning is required. The localization (estimation robot's pose) is fundamental ability for service robot to navigate autonomously. In this paper, we propose a new system for point pattern matching based visual global localization using spot lightings in ceiling. The proposed algorithm us suitable for system that demands high accuracy and fast update rate such a guide robot in the exhibition. A single camera looking upward direction (called ceiling vision system) is mounted on the head of the mobile robot and image features such as lightings are detected and tracked through the image sequence. For detecting more spot lightings, we choose wide FOV lens, and inevitably there is serious image distortion. But by applying correction calculation only for the position of spot lightings not whole image pixels, we can decrease the processing time. And then using point pattern matching and least square estimation, finally we can get the precise position and orientation of the mobile robot. Experimental results demonstrate the accuracy and update rate of the proposed algorithm in real environments.