• Journal of information and communication convergence engineering
• /
• 제13권2호
• /
• pp.105-112
• /
• 2015

The estimation of missing sensor values is an important problem in sensor network applications, but the existing approaches have some limitations, such as the limitations of application scope and estimation accuracy. Therefore, in this paper, we propose a new estimation model based on a spatial-temporal correlation analysis (STCAM). STCAM can make full use of spatial and temporal correlations and can recognize whether the sensor parameters have a spatial correlation or a temporal correlation, and whether the missing sensor data are continuous. According to the recognition results, STCAM can choose one of the most suitable algorithms from among linear interpolation algorithm of temporal correlation analysis (TCA-LI), multiple regression algorithm of temporal correlation analysis (TCA-MR), spatial correlation analysis (SCA), spatial-temporal correlation analysis (STCA) to estimate the missing sensor data. STCAM was evaluated over Intel lab dataset and a traffic dataset, and the simulation experiment results show that STCAM has good estimation accuracy.

• 조명전기설비학회논문지
• /
• 제29권7호
• /
• pp.80-86
• /
• 2015

As wind farm outputs depend on natural wind resources that vary over space and time, spatial correlation analysis is needed to estimate power outputs of wind generation resources. As a result, geographic information such as latitude and longitude plays a key role to estimate power outputs of spatially distributed wind farms. In this paper, we introduce spatial correlation analysis to estimate the power outputs produced by wind farms that are geographically distributed. We present spatial correlation analysis of empirical power output data for the JEJU Island and ERCOT ISO (Texas) wind farms and propose the Correlation Decay Distance (CoDecDist) model based on geographic correlation analysis to enhance the estimation of wind power outputs.

• 대한원격탐사학회지
• /
• 제22권5호
• /
• pp.337-350
• /
• 2006

The characteristics of neighborhood correlation images for change detection were explored at different spatial resolution scales. Bi-temporal QuickBird datasets of Las Vegas, NV were used for the high spatial resolution image analysis, while bi-temporal Landsat $TM/ETM^{+}$ datasets of Suwon, South Korea were used for the mid spatial resolution analysis. The neighborhood correlation images consisting of three variables (correlation, slope, and intercept) were evaluated and compared between the two scales for change detection. The neighborhood correlation images created using the Landsat datasets resulted in somewhat different patterns from those using the QuickBird high spatial resolution imagery due to several reasons such as the impact of mixed pixels. Then, automated binary change detection was also performed using the single and multiple neighborhood correlation image variables for both spatial resolution image scales.

• 한국수자원학회:학술대회논문집
• /
• 한국수자원학회 2005년도 학술발표회 논문집
• /
• pp.943-947
• /
• 2005

This study was intended to interpose an objection about the analysis of rainfall spatial distribution without a proper standard, and offer the improved approach using 1,he geostatistical analysis method to analyze it. For this, spatially distributed daily rainfall data sets were collected for 41 weather stations in study area, and variogram and correlation analysis were conducted. In the results of correlation analysis, it was found that the longer distance between the stations reduces the correlation of the rainfall data, and maltes the characteristics of the rainfall spatial distribution. The variogram analysis shows that correlation range was less than 50 km for the 17 daily rainfall data sets of total 91 sets. It says that it involves some rike, to determine the application method for rainfall spatial distribution without some qualifications, hence the Application standards of the Rainfall Spatial Distribution Analysis Technique, were essential and that was contingent on characteristics of rainfall and landscape.

• 대한공간정보학회지
• /
• 제17권1호
• /
• pp.141-147
• /
• 2009

상점의 정보, 서비스업 등을 영위하기 위한 공간입지에 대한 정보(인구생태학적 변수, 사회생태학적 변수)의 탐색적 자료 분석을 위해 공간 특성분석이 필요하다. 따라서 본 연구에서는 지리적 공간상에서 공간객체간의 상호의존성과 상호작용과 통계적 상관분석을 이용하여 서비스업종간의 상관분석법을 제시하고자 하며, 또한 근린상권의 업종 간 상관관계분석의 도출을 통하여 공간특성에 대한 분석을 하기 위함이다.

• 대한전자공학회논문지TC
• /
• 제48권8호
• /
• pp.36-41
• /
• 2011

다중 안테나 통신 시스템에서 채널의 correlation은 심각한 성능 열화를 일으킨다. 본 논문에서는 공간 자원을 양방향으로 활용함으로써 채널 correlation에 대한 민감도를 줄일 수 있는 방법을 다룬다. 먼저 공간 자원의 양방향 활용에 대한 개념을 제시한 후, 사용 가능한 eigenmode를 분석함으로써 이 기술이 채널 correlation에 대한 민감도를 어떻게 줄이는지에 대해 다루도록 한다. 마지막으로 시뮬레이션 결과를 통해 이 기술이 채널 correlation에 대한 민감도를 효과적으로 줄임을 보인다.

• 대한건축학회논문집:계획계
• /
• 제34권4호
• /
• pp.69-76
• /
• 2018

The purpose of this study is to propose and verify a spatial network construction method that integrated roads and subway lines to improve the predictability of the urban spatial analysis model. The existing axial map for urban spatial analysis did not reflect the subway line that serves as an important moving space in modern cities. To improve this axial map, proposed a Ground-Underground Spatial Network by integrating the underground spatial network with the axial map. As a result of the integration analysis, the Ground-Underground Spatial Network(GUSN) were similar to the movement frequency. Correlation of GUSN was 0.723, which showed higher explanatory power than correlation coefficient of 0.575 in axial map. The result of this study is expected to be a theoretical basis for constructing spatial network in urban space analysis with subway.

• 한국수자원학회:학술대회논문집
• /
• 한국수자원학회 2007년도 학술발표회 논문집
• /
• pp.178-182
• /
• 2007

In this study, we evaluated current raingauge network of Soyanggang dam region applying spatial-correlation analysis and Entropy theory to recommend an optimized raingauge network. In the process of analysis, correlation distance of raingauge stations is estimated and evaluated via spatial-correlation method and entropy method. From this correlation distances, respective influencing radii of each dataset and each methods is assessed. The result of correlation and entropy analysis has estimated correlation distance of 25.546km and influence radius of 7.206km, deducing a decrease of network density from $224.53km^2$ to $122.47km^2$ which satisfy the recommended minimum densities of $250km^2$ in mountainous regions(WMO, 1994) and an increase of basin coverage from 59.3% to 86.8%. As for the elevation analysis the relative evaluation ratio increased from 0.59(current) to 0.92(optimized) resulting an obvious improvement.

• Communications for Statistical Applications and Methods
• /
• 제12권1호
• /
• pp.241-252
• /
• 2005

In this study we suggest that the spatial correlation structure of the brain fMRI data be used to characterize the functional connectivity of the brain. For some concussion and recovery data, we examine how the correlation structure changes from one step to another in the data analyses, which will allow us to see the effect of each analysis to the spatial correlation or the functional connectivity of the brain. This will lead us to spot the processes which cause significant changes in the spatial correlation structure of the brain. We discuss whether or not we can decompose correlation matrices in terms of its causes of variations in the data.

• 자원ㆍ환경경제연구
• /
• 제28권3호
• /
• pp.327-354
• /
• 2019

본 논문에서는 한국의 미세먼지 발생원인을 분석하는 과정에서 기존 논문에서는 고려하지 않았던 지역 간 공간상관성(Spatial correlation)을 고려한 패널계량분석을 진행하였다. 기존 환경쿠즈네츠곡선(EKC, Environmental Kuznets Curve)에 대한 연구들에서, 인접한 국가 및 지역 간에 오염물질의 상호영향이 존재할 가능성이 있음에도 각 유닛이 독립이라고 가정한다. 본 논문에서는 한국의 미세먼지농도에 대한 지역 패널데이터를 이용하여 기존 EKC가 지역의 상호상관성을 고려하는 때도 성립할 수 있으며, 이러한 영향을 고려하지 않았을 때 미세먼지농도의 원인에 대해 과소 혹은 과대 추정될 수 있음을 규명하였다.