• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.247-253
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• 2023

As climate change problem has recently become serious, studies are being conducted to identify carbon dioxide (CO₂) emission dynamics based on satellite data to reduce emissions. It is also very important to analyze spatial patterns by estimating and mapping CO₂ emissions dynamic. Therefore, in this study, CO₂ emissions in the Korean Peninsula from 2013 to 2020 were estimated and mapped. To spatially estimate and map emissions, we use the enhanced vegetation index adjusted nighttime light index, an index that combines nighttime light (NTL) and vegetation index, to map both areas where NTL is observed and areas where NTL is not observed. In order to spatially estimate and map CO₂ emissions, the total annual emissions of the Korean Peninsula were calculated, resulting in an increase of 11% from 2013 to 2017 and a decrease of 13% from 2017 to 2020. As a result of the mapping, it was confirmed that the spatial pattern of CO₂ emissions in the Korean Peninsula were concentrated in urban areas. After being divided into 17 regions, which included the downtown area, the metropolitan area accounted for roughly 40% of CO₂ emissions in the Korean Peninsula. The region that exhibited the most significant change from 2013 to 2020 was Sejong City, showing a 96% increase.

• Journal of Korean Society for Geospatial Information Science
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• v.19 no.4
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• pp.81-89
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• 2011

Low-carbon Green Growth is highlighted as the main issue from in and outof Korea. Recently Korean government and local goverment constructed a master plan and related database. Considering this as a starting point the carbon gross emission has become an important factor in the city planning and management of local goverment unit. This research was focused on the analysis of carbon gross emission and the environment of carbon occurrence using statistics and digital forest map for the Gyeonggi-do. Further research need to analysis the carbon absorption using satellite image for periodic database. These database will be available basic data for the policy making.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2675-2677
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• 2003

본 논문에서는 공간 해상도 감축이 이루어지는 디지털 동영상 변환 부호화기에서의 적응 움직임 벡터 재추정 기법을 제안한다. 영상의 복잡도를 기반으로 한 에러 추정을 통해 움직임 벡터를 1차 추정하고, 이를 기반으로 정제범위를 효율적으로 조절하여 연산량을 감소시킨다. TMN5를 이용한 실험 결과 제안된 방법에 의한 추정 벡터는 기존 방법들에 의해 추정된 벡터보다 더 좋은 화질을 보였으며 적응적 정제 범위 선택에 의해 연산량도 더 적었다.

• Economic and Environmental Geology
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• v.55 no.4
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• pp.353-366
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• 2022

Spatial estimation of geoscience data (geo-data) is challenging due to spatial heterogeneity, data scarcity, and high dimensionality. A novel spatial estimation method is needed to consider the characteristics of geo-data. In this study, we proposed the application of Gaussian Mixture Model (GMM) among machine learning algorithms with multivariate data for robust spatial predictions. The performance of the proposed approach was tested through soil chemical concentration data from a former smelting area. The concentrations of As and Pb determined by ex-situ ICP-AES were the primary variables to be interpolated, while the other metal concentrations by ICP-AES and all data determined by in-situ portable X-ray fluorescence (PXRF) were used as auxiliary variables in GMM and ordinary cokriging (OCK). Among the multidimensional auxiliary variables, important variables were selected using a variable selection method based on the random forest. The results of GMM with important multivariate auxiliary data decreased the root mean-squared error (RMSE) down to 0.11 for As and 0.33 for Pb and increased the correlations (r) up to 0.31 for As and 0.46 for Pb compared to those from ordinary kriging and OCK using univariate or bivariate data. The use of GMM improved the performance of spatial interpretation of anthropogenic metals in soil. The multivariate spatial approach can be applied to understand complex and heterogeneous geological and geochemical features.

• Journal of Korean Society for Geospatial Information Science
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• v.9 no.1 s.17
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• pp.39-47
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• 2001

DTM(Digital Terrain Model) can play a key rule in a great number of the fields of construction Engineering. One of the most important application fields is to determine volume in that the total construction expenses is usually calculated through this. It therefore is necessary to the study on improving the precise of the determination using DTM on account of saving time and cost. On this study, 1:5000 topographic maps issued by NGI in 15 districts involved in Pusan city was digitalized to generate DTM at first. After this step, not only was the determination of the volume as well as readjusted area and height done for the sake of estimating the changable topography caused by cut & fill volume in future but also provided the model to calculate it as results. In addition, comparison among the interpolations, such as Inverse Distance Method and Nearest Neighbor, was respectively done to look over the differences of the volume estimated from each interpolation and also to find the most suitable method. As a result, the former yielded the largest values of area and the volume while the latter gave the smallest ones. Moreover, the values estimated on this study were closely similar to ones obtained by the government of Pusan.

• Proceedings of the Korean Statistical Society Conference
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• 2003.10a
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• pp.295-300
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• 2003

공간적으로 영향을 받는 위치에서의 상호 연관성을 고려한 예측모형 중에서 크리깅 (kriging) 방법은 관측된 데이터를 보간(interpolation)하고, 부드럽게 연결(smoothing)하며, 새로운 데이터를 예측(prediction)하는 통계적 모형으로서 많이 활용되고 있다. 크리깅 모형을 적용하기 위해서는 먼저 주어진 두 위치에서의 비연관성을 나타내는 세미베리오그램 (semivariogram)의 3가지 모수(nugget, sill, range)를 추정해야 한다. 본 연구에서는 전역 적 최적화 방법인 유전자 알고리즘(genetic algorithm)을 도입하여 세미베리오그램 모수들을 추정하였고, 이를 통해 강우량(rainfall)에 대한 크리깅 추정량을 산출하고 효과성을 판단하였다.

• The Korean Journal of Applied Statistics
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• v.22 no.4
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• pp.677-686
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• 2009

Stratification of population is widely used to improve the efficiency of the estimation in a sample survey. However, it causes several problems when there are some variables containing outliers. To overcome these problems, Park and Yun (2008) proposed a rather subjective method, which finds outliers before $\kappa$-means clustering for stratification. In this study, we propose the $\kappa$-spatial medians clustering method which is more robust than $\kappa$-means clustering method and also does not need the process of finding outliers in advance. We investigate the characteristics of the proposed method through a case study used in Park and Yun (2008) and confirm the efficiency of the proposed method.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.6
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• pp.3879-3887
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• 2014

This paper presents a model to estimate the amount of $CO_2$ emitted by cars in cities. Based on the spatial configuration of street networks, building masses and usages, it first develops a deductive model to combine them in a way to account for $CO_2$ emission amount by cars. It then proceed to validate model behaviours through a series of simulations on some ideal urban settings and finally calibrate it following its real application to the five case study cities in Korea. In contrast to the conventional 'top-down' approaches, we expect our model to have high utilities, particularly in the field of urban planning and design, where we cannot but deal directly with the spatial configuration of urban components and microscopic human activities.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.36-36
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• 2016

하천정비나 유역종합 치수계획 등 수자원계획을 수립하는 과정에 있어 하천의 설계홍수량 추정은 필수적이며, 하천의 수공구조물의 안전성과 수문학적 위험도를 산정하는데도 활용되고 있다. 그러나 매년 관측되는 강우량 자료에 비해 유출량 자료의 길이가 비교적 짧아 신뢰성 있는 홍수량자료의 구축이 어려운 실정이며, 미계측 유역에 위치한 중소규모 하천의 설계홍수량과 같은 수문학적 자료는 매우 제한적이다. 이러한 이유로 본 연구에서는 기 수립된 하천정비기본계획의 자료들을 활용하여 유역의 특성(면적, 경사, 고도)이 고려되는 새로운 홍수량 산정식을 개발하였으며, Bayesian GLM(generalized linear method) 기법을 활용하여 미계측 유역의 지역화를 통한 홍수량의 추정이 가능하도록 하였다. 또한 Hierarchical Bayesian 기법을 활용하여 개발된 공식에 활용되는 매개변수의 불확실성을 구간을 산정하였다. Bayesian 기법의 도입으로 산정되는 홍수량의 불확실성 구간을 정량적으로 제시할 수 있었으며, 제안된 연구 결과는 미계측 유역의 홍수량을 추정하는 도구로서 활용성이 높을 것으로 기대된다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.7A
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• pp.939-946
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• 1999

A new blind multi-user channel estimation algorithm for the mobile communication systems is proposed. The proposed algorithm exploits the second-order statistics of a received signal and the subspace concept, and requires much less computational complexity than the existing algorithms. The algorithm can reduce the comptational load by estimating the physical channels excluding the spreading codes. We formulate the algorithm using the multi-channel model for asynchronous DS-CDMA systems and perform numerical experiments to evaluate the performance of the proposed algorithm.