• Title/Summary/Keyword: correlation analysis

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A New Estimation Model for Wireless Sensor Networks Based on the Spatial-Temporal Correlation Analysis

  • Ren, Xiaojun;Sug, HyonTai;Lee, HoonJae
    • Journal of information and communication convergence engineering
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    • v.13 no.2
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    • pp.105-112
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    • 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.

Higher-order solutions for generalized canonical correlation analysis

  • Kang, Hyuncheol
    • Communications for Statistical Applications and Methods
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    • v.26 no.3
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    • pp.305-313
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    • 2019
  • Generalized canonical correlation analysis (GCCA) extends the canonical correlation analysis (CCA) to the case of more than two sets of variables and there have been many studies on how two-set canonical solutions can be generalized. In this paper, we derive certain stationary equations which can lead the higher-order solutions of several GCCA methods and suggest a type of iterative procedure to obtain the canonical coefficients. In addition, with some numerical examples we present the methods for graphical display, which are useful to interpret the GCCA results obtained.

Nonlinear Canonical Correlation Analysis for Paralysis Disease Data

  • Shin, Yang-Kyu
    • Journal of the Korean Data and Information Science Society
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    • v.15 no.3
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    • pp.515-521
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    • 2004
  • Categorical data are mostly found in oriental medical research. The nonlinear canonical correlation analysis does not assume an interval level of measurement. In this paper, we apply nonlinear canonical correlation analysis to quantification and explain how similar sets of variables are to one another for paralysis disease data.

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Relations of School Organizational Climate and Teachers' Job Stresses (학교조직풍토와 교사의 직무스트레스의 관계)

  • LEE, Kyeong-Hwa;JUNG, Hye-Young
    • Journal of Fisheries and Marine Sciences Education
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    • v.21 no.1
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    • pp.121-133
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    • 2009
  • This study tested the relations of schools organizational climate and teachers' job stresses, perceived by 913 teachers from 45 elementary, junior- and senior-high schools. Pearson's correlation analysis for the relations between the sub-factors of both organizational climate and job stresses and cannonical correlation analysis for the relative contribution of individual variable of organizational climate upon job stress were applied for the test. The results of Pearson's correlation analysis showed that while 'intimacy', 'esprit', 'considerations', and 'production emphasis' climate had negative correlations with job stress sub-factors, 'disengagement' and 'aloofness' climate had positive correlation. 'Student guidance', a sub-factor of job stresses, did not have statistically significant correlation with any sub-factors of organizational climate. Findings from cannonical correlation analysis showed 2 significant cannonical functions to explain the relations between the sets of variables. 'Disengagement' from organizational climate positively contributed with 'authority forfeiture' and 'dissention and conflict' of the job stresses variables.

WAVENUMBER CORRELATION ANALYSIS OF RADAR INTERFEROGRAM

  • Won, Joong-Sun;Kim, Jeong-Woo
    • Proceedings of the KSRS Conference
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    • pp.425-428
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    • 1999
  • The radar interferogram represents phase differences between the two synthetic aperture radar observations acquired in slightly different angle. The success of the radar interferometric application largely depends on the quality of the interferogram generated from two or more synthetic aperture radar data sets. We propose here to apply the wavenumber correlation analysis to the in-phase and quadrature phase of the radar interferogram. The wavenumber correlation analysis is to resolve the highly correlated components from the low correlation components by estimating correlation coefficients for each wavenumber component. Through this approach, one can easily distinguish the signal components from the noise components in the wavenumber domain. Therefore, the wavenumber correlation analysis of the radar interferogram can be utilized to design post filter and to estimate the quality of interferogram. We have tested the wavenumber correlation analysis using a Radarsat SAR data pair to demonstrated the effectiveness of

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A Study on the Analysis of Correlation Decay Distance(CoDecDist) Model for Enhancing Spatial Prediction Outputs of Spatially Distributed Wind Farms (풍력발전출력의 공간예측 향상을 위한 상관관계감소거리(CoDecDist) 모형 분석에 관한 연구)

  • Hur, Jin
    • Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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    • v.29 no.7
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    • pp.80-86
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    • 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.

Multivariate Volatility Analysis via Canonical Correlations for Financial Time Series (정준상관분석을 통한 다변량 금융시계열의 변동성 분석)

  • Lee, Seung Yeon;Hwang, S.Y.
    • The Korean Journal of Applied Statistics
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    • v.27 no.7
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    • pp.1139-1149
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    • 2014
  • Multivariate volatility is summarized through canonical correlation analysis (CCA). Along with the standard CCA, non-negative and sparse canonical correlation analysis (NSCCA) is introduced to make sure that volatility coefficients are non-negative and the number of coefficients in the volatility CCA is as small as possible. Various multivariate financial time series are analyzed to illustrate the main contribution of the paper.

Evaluation of Panel Performance by Analysis of Variance, Correlation Analysis and Principal Component Analysis (패널요원 수행능력 평가에 사용된 분산분석, 상관분석, 주성분분석 결과의 비교)

  • Kim, Sang-Sook;Hong, Sung-Hie;Min, Bong-Kee;Shin, Myung-Gon
    • Korean Journal of Food Science and Technology
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    • v.26 no.1
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    • pp.57-61
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    • 1994
  • Performance of panelists trained for cooked rice quality was evaluated using analysis of variance, correlation analysis, and principal component analysis. Each method offered different information. Results showed that panleists with high F ratios (p=0.05) did not always have high correlation coefficient (p=0.05) with mean values pooled from whole panel. The results of analysis of variance for the panelists whose performance were extremely good or extremely poor were consistent with those of correlation analysis. Outliers designated by principal component analysis were different from the panelists whose performance was defined as extremely good or extremely poor by analysis of variance and correlation analysis. The results of principal component analysis descriminated the panelists with different scoring range more than different scoring trends depending on the treatments. Our study suggested combination of analysis of variance and correlation analysis provided valid basis for screening panelists.

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Underwater Target Analysis Using Canonical Correlation Analysis (정준상관분석을 이용한 수중표적 분석)

  • Seok, Jong-Won;Kim, Tae-Hwan;Bae, Keun-Sung
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.16 no.9
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    • pp.1878-1883
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    • 2012
  • Generally, in the underwater target recognition, feature vectors are extracted from the target signal utilizing spatial information according to target shape/material characteristics. And, various signal processing techniques have been studied to extract feature vectors which is less sensitive to the location of the receiver. In this paper, we analyzed the characteristics of synthesized underwater objects using canonical correlation analysis method which is relatively less sensitive to the location of receiver. Canonical correlation analysis is applied to two consecutive backscattered sonar returns at different aspect angles to analyze the correlation characteristics in multi-aspect environment.

Correlation analysis of solar radiation and meteorological parameters on high ozone concentration (태양복사 및 기상요소의 고농도 오존형성에 대한 상관성 분석)

  • An, Jae Ho
    • KIEAE Journal
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    • v.12 no.6
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    • pp.93-98
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    • 2012
  • The concerns on high ozone concentration phenomenon is significantly growing in Seoul metropolitan area including the industry complex area, like Shiwha Banwol area. The aims of this research is the analysis of relationship between high concentrations of $O_3$ and solar radiation parameters in atmosphere. The understanding of the effects of solar radiation intensity, humidity, high air temperature on ozone concentration in a day is very useful to provide a direction for reducing of the high ozone concentration to a local government or a metropolitan government. The correlation analysis between maximum ozone concentration and various meteorological parameters in 2009 - 2011 carried out using IBM's SPSS program. The results showed that the mean correlations coefficient (R) between daily Ozone maximum and solar radiation resulted R = 0.64 during 2011. May - September in 10 air pollution stations. In case of correlations between daily ozone maximum and relative humidity showed negative correlation R = -0.61. The correlation analysis with mean air temperature during 1-3 PM resulted R = 0.29. This low correlation coefficient could be corrected by using of categorized data of ozone concentration. The daily maximum ozone concentration is more dependent on peak solar radiation and high air temperature during 1-3 PM than its simple daily maximum values. The results of this research would be used to develop the high ozone alert system around Seoul metropolitan area. This correlation analysis could be partially integrated to prediction of ozone peak concentration in connection with other methods like classification and regression tree(CART).