• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.8B
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• pp.1518-1531
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• 1999

The reconstructed images from highly compressed MPEG or H.263 data have noticeable image degradations, such as blocking artifacts near the block boundaries, corner outliers at cross points of blocks, and ringing noise near image edges, because the MPEG or H.263 quantizes the transformed coefficients of 8$\times$8 pixel blocks. A post-processing algorithm has been proposed by authors to reduce quantization effects, such as blocking artifacts, corner outliers, and ringing noise, in MPEG-decompressed images. Our signal-adaptive post-processing algorithm reduces the quantization effects adaptively by using both spatial frequency and temporal information extracted from the compressed data. The blocking artifacts are reduced by one-dimensional (1-D) horizontal and vertical low pass filtering (LPF), and the ringing noise is reduced by two-dimensional (2-D) signal-adaptive filtering (SAF). A comparison study of the subjective quality evaluation using modified single stimulus method (MSSM), the objective quality evaluation (PSNR) and the computation complexity analysis between the signal-adaptive post-processing algorithm and the MPEG-4 VM (Verification Model) post-processing algorithm is performed by computer simulation with several MPEG-4 image sequences. According to the comparison study, the subjective image qualities of both algorithms are similar, whereas the PSNR and the comparison complexity analysis of the signal-adaptive post-processing algorithm shows better performance than the VM post-processing algorithm.

• Journal of the Korea Computer Graphics Society
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• v.24 no.2
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• pp.21-28
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• 2018

We present interactive methods for visualizing the cloud height data and the backscatter data collected from ceilometers in the three-dimensional virtual space. Because ceilometer data is high-dimensional, large-size data associated with both spatial and temporal information, it is highly improbable to exhibit the whole aspects of ceilometer data simply with static, two-dimensional images. Based on the three-dimensional rendering technology, our visualization methods allow the user to observe both the global variations and the local features of the three-dimensional representations of ceilometer data from various angles by interactively manipulating the timing and the view as desired. The cloud height data, coupled with the terrain data, is visualized as a realistic cloud animation in which many clouds are formed and dissipated over the terrain. The backscatter data is visualized as a three-dimensional terrain which effectively represents how the amount of backscatter changes according to the time and the altitude. Our system facilitates the multivariate analysis of ceilometer data by enabling the user to select the date to be examined, the level-of-detail of the terrain, and the additional data such as the planetary boundary layer height. We demonstrate the usefulness of our methods through various experiments with real ceilometer data collected from 93 sites scattered over the country.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2010.06a
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• pp.185-185
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• 2010

문명 발전에 의해 생활공간이 더욱더 복잡해짐에 따라 현실 세계의 3차원 공간으로서의 이해가 필요해졌고, 3차원 공간을 효율적으로 관리 및 분석하기 위하여 3차원 GIS에 대한 중요성이 대두되기 시작하였다. 현재까지의 3차원 GIS는 1990년대에 들어서 3차원 가상도시의 등장으로 도시 모델의 3차원 가시화에 대해 많은 연구가 진행되어 왔다. 이러한 3차원 가상도시는 도시 관리 차원에서 도시공간의 표현, 분석, 모델링, 시뮬레이션 등을 통하여 도시정보의 체계적이고 효율적인 관리를 가능하게 한다. 3D GIS 기술의 활용 사례로는 3차원 도시 경관 분석 및 도시 계획, 3차원 시뮬레이션, 3차원 위치기반 서비스, 도시 행정 관리 등이 있다. 3차원 도시 경관 분석 및 도시 계획 활용 분야에서는 3차원 GIS를 활용하여 새롭게 건설되는 건물들이나 토지 이용 변경에 따른 경관 분석을 가상공간에서 시뮬레이션 분석함으로써 경관 변화 예측 및 적합한 경관 설계에 대한 분석을 한다. 3차원 시뮬레이션의 경우 환경오염도 분석, 최적의 입지선정, 개발 전후의 경관 차이, 각종 재난에 따른 위험도 분석, 경관과 일조량 분석 등을 미리 가상도시에 시뮬레이션 함으로서 불필요한 업무를 줄일 수 있고 발생할 수 있는 결과 값을 예상할 수 있다. 3차원 위치기반 서비스의 경우 자신의 위치를 비교적 정확히 측정함으로서 생소한 도시를 방문하거나 위치를 찾아갈 경우 최단 경로 및 최적 경로를 탐색할 수 있다. 앞으로의 3차원 GIS 기술 동향으로 유비쿼터스 환경에서의 시공간분석(Temporal GIS)과 3차원 실내공간인지 기술 개발 등으로 볼수있다. 시공간분석은 시공간 질의를 통한 공간정보의 시간에 다른 변화 계산 또는 예측된 결과를 분석할 수 있고, 시공간 분석에 사용되는 공간적, 시간적 정보의 조합은 적어도 3차원 이상의 정보를 나타내기 때문에 필요에 따라 다양한 결과를 산출 할 수 있다. 종합적으로 시공간분석은 GIS의 분석 범위를 넓혀 주며 실시간 GIS 응용시스템 개발을 위한 주요 과업이다. 3차원 실내공간정보 표현기법과 공간분석 및 공간인지 기법을 이용한 통한 유비쿼터스 환경에서의 3차원 실내공간인지 기술 개발을 통해 최근 공간정보를 기반으로 한 위치기반서비스내 u-서비스 즉, 인간 행태의 분석, 관리 및 응용을 지원하도록 한다 이는 위치기반서비스 사용자의 주변상황에 대한 인지능력을 향상시켜주고 최근 사회적 이슈가 되고 있는 U-City의 성공여부에 직접적인 영향을 미칠것이다. 이를 위해 다음과 같은 기술개발이 수행되어야 할것이다. 첫째로, 공간인지 기술을 위한 3차원 실내공간표현 방법 개발, 둘째로, 3차원 실내공간정보를 이용한 공간분석 및 공간인지 기법 개발, 마지막으로 유비쿼터스 환경에서 3차원 실내공간인지를 위한 미들웨어 개발을 통해서 공간정보의 활용을 macro-scale 공간에서 micro-scale로 확산될 것이다. 그러므로, 본 세미나를 통해서, 3차원 GIS의 활용의 보편화와 대중화를 위해서는 3차원 데이터 획득 및 처리 방법, 3차원 공간데이타베이스, 공간 분석 및 가시화에 대한 기술적 이슈들을 논하고자 한다.

• Journal of Korea Spatial Information System Society
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• v.8 no.2 s.17
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• pp.75-89
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• 2006

LBS (Location-Based Service) systems have become a serious subject for research and development since recent rapid advances in wireless communication technologies and position measurement technologies such as global positioning system. The architecture named the GALIS (Gracefully Aging Location Information System) has been suggested which is a cluster-based distributed computing system architecture to overcome performance losses and to efficiently handle a large volume of data, at least millions. The GALIS consists of SLDS and LLDS. The SLDS manages current location information of moving objects and the LLDS manages past location information of moving objects. In this thesis, we implement a monitoring technique for the GALIS prototype, to allow dynamic load balancing among multiple computing nodes by keeping track of the load of each node in real-time during the location data management and spatio-temporal query processing. We also propose a buffering technique which efficiently manages the query results having overlapped query regions to improve query processing performance of the GALIS. The proposed scheme reduces query processing time by eliminating unnecessary query execution on the overlapped regions with the previous queries.

• Journal of the HCI Society of Korea
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• v.1 no.1
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• pp.53-61
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• 2006

In this paper, we propose a novel method for 3D reconstruction of an indoor scene using a multi-view camera. Until now, numerous disparity estimation algorithms have been developed with their own pros and cons. Thus, we may be given various sorts of depth images. In this paper, we deal with the generation of a 3D surface using several 3D point clouds acquired from a generic multi-view camera. Firstly, a 3D point cloud is estimated based on spatio-temporal property of several 3D point clouds. Secondly, the evaluated 3D point clouds, acquired from two viewpoints, are projected onto the same image plane to find correspondences, and registration is conducted through minimizing errors. Finally, a surface is created by fine-tuning 3D coordinates of point clouds, acquired from several viewpoints. The proposed method reduces the computational complexity by searching for corresponding points in 2D image plane, and is carried out effectively even if the precision of 3D point cloud is relatively low by exploiting the correlation with the neighborhood. Furthermore, it is possible to reconstruct an indoor environment by depth and color images on several position by using the multi-view camera. The reconstructed model can be adopted for interaction with as well as navigation in a virtual environment, and Mediated Reality (MR) applications.

• Journal of Korea Water Resources Association
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• v.41 no.9
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• pp.959-967
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• 2008

Disaggregation techniques are widely used to transform observed daily rainfall values into hourly ones, which serve as important inputs for flood forecasting purposes. However, an important limitation with most of the existing disaggregation techniques is that they treat the rainfall process as a realization of a stochastic process, thus raising questions on the lack of connection between the structure of the models on one hand and the underlying physics of the rainfall process on the other. The present study introduces a nonlinear deterministic (and specifically chaotic) framework to study the dynamic characteristics of rainfall distributions across different temporal scales (i.e. weights between scales), and thus the possibility of rainfall disaggregation. Rainfall data from the Seoul station (recorded by the Korea Meteorological Administration) are considered for the present investigation, and weights between only successively doubled resolutions (i.e., 24-hr to 12-hr, 12-hr to 6-hr, 6-hr to 3-hr) are analyzed. The correlation dimension method is employed to investigate the presence of chaotic behavior in the time series of weights, and a local approximation technique is employed for rainfall disaggregation. The results indicate the presence of chaotic behavior in the dynamics of weights between the successively doubled scales studied. The modeled (disaggregated) rainfall values are found to be in good agreement with the observed ones in their overall matching (e.g. correlation coefficient and low mean square error). While the general trend (rainfall amount and time of occurrence) is clearly captured, an underestimation of the maximum values are found.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.10
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• pp.397-406
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• 2014

In order to answer the questions successfully on behalf of the human in DeepQA environments such as Jeopardy! of the American quiz show, the computer is required to have the capability of fast temporal and spatial reasoning on a large-scale commonsense knowledge base. In this paper, we present a scalable spatial reasoning algorithm for deriving efficiently new directional and topological relations using the MapReduce framework, one of well-known parallel distributed computing environments. The proposed reasoning algorithm assumes as input a large-scale spatial knowledge base including CSD-9 directional relations and RCC-8 topological relations. To infer new directional and topological relations from the given spatial knowledge base, it performs the cross-consistency checks as well as the path-consistency checks on the knowledge base. To maximize the parallelism of reasoning computations according to the principle of the MapReduce framework, we design the algorithm to partition effectively the large knowledge base into smaller ones and distribute them over multiple computing nodes at the map phase. And then, at the reduce phase, the algorithm infers the new knowledge from distributed spatial knowledge bases. Through experiments performed on the sample knowledge base with the MapReduce-based implementation of our algorithm, we proved the high performance of our large-scale spatial reasoner.

• Korean Journal of Agricultural and Forest Meteorology
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• v.2 no.1
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• pp.9-15
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• 2000

The objective of this study is to evaluate the use of RADARSAT and Landsat TM data for the monitoring of rice growth. The relationships between backscatter coefficients($\sigma$$^{0}$ ) of RADARSAT data and digital numbers (DN) of Landsat TM and rice growth parameters were investigated. Radar backscatter coefficients were calculated by calibration process and then compared with rice growth parameters; plant height, leaf area index (LAI), and fresh and dry biomass. When radar backscatter coefficient ($\sigma$$^{0}$ ) of rice was expressed as a function of time, it is shown that the increasing trend ranged from -22--20dB to -9--8dB as growth advances. The temporal variation of backscatter coefficient was significant to interpret rice growth. According to the relationship between leaf area index and backscatter coefficient, backscatter coefficient underestimated leaf area index at the beginning of life history and overestimated, at the reproductive stage. The same increasing trend between biomass and backscatter coefficient was shown. From these results, RADARSAT data appear positive to the monitoring of rice growth. Each band of time-series Landsat TM data had a significant trend as a rice crop grows during its life cycle. Spectral indices, NDVI[(TM4-TM3)/(TM4+TM3)] and RVI(TM4/TM2), derived from Landsat TM equivalent bands had the same trend as leaf area index.

• Journal of Korea Water Resources Association
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• v.39 no.10 s.171
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• pp.867-880
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• 2006

Hydrological models with many parameters and complex model structures require a powerful and detailed model calibration/validation scheme. In this study, we proposed a multi-variable and multi-site calibration and validation framework for the Soil Water Assessment Tool (SWAT) model applied in the Gap-cheon catchment located downstream of the Geum river basin. The sensitivity analysis conducted before main calibration helped understand various hydrological processes and the characteristics of subcatchments by identifying sensitive parameters in the model. In addition, the model's parameters were estimated based on existing data prior to calibration in order to increase the validity of model. The Nash-Sutcliffe coefficients and correlation coefficient were used to estimate compare model output with the observed streamflow data: $R_{eff}\;and\;R^2$ ranged 0.41-0.84 and 0.5-0.86, respectively, at the Heuduck station. Model reproduced baseflow estimated using recursive digital filter except for 2-5% overestimation at the Sindae and Boksu stations. Model also reproduced the temporal variability and fluctuation magnitude of observed groundwater levels with $R^2$ of 0.71 except for certain periods. Therefore, it was concluded that the use of multi-variable and multi-site method provided high confidence for the structure and estimated parameter values of the model.

• Ecology and Resilient Infrastructure
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• v.7 no.2
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• pp.97-113
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• 2020

Soil is the foundation of human life and the basis for food security. Considering this it is prioritized in the UN's Sustainable Development Goals (SDG). Therefore, research on soil resilience in the agricultural environment is crucial for sound and sustainable soil management, especially in highly uncertain and unpredictable conditions. Soil resilience is defined in different ways by several researchers; however, its definition typically includes the concepts of recovery and resistance to stress. The physical, chemical, and biological characteristics of soils that are used to assess the soil resilience, i.e., the response of soil to various types of stress are summarized in this study. In addition, various statistical processing techniques and quantification methods are summarized considering the wide spatial and temporal scope of soil resilience research. Several soil resilience studies typically conduct the following five steps: (1) soil and site selection (2) stress (independent variable) setting (3) soil characteristics and indicator (dependent variable) setting (4) performing various spatiotemporal scale experiments (5) statistical analysis. The previous and present studies present a general introduction of soil resilience, based on which, further practical research considering domestic agricultural environment should be conducted. The extensive range of soil resilience measurements will require collaboration between researchers in various fields.