• Journal of the Korean Data and Information Science Society
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• v.23 no.5
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• pp.1037-1044
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• 2012

Multivariate control charts for effectively monitoring every component in the dispersion matrix of multivariate normal process are considered. Through the numerical results, we noticed that the multivariate control charts based on sample statistic $V_i$ by Hotelling or $W_i$ by Alt do not work effectively when the correlation coefficient components in dispersion matrix are increased. We propose a combined procedure monitoring every component of dispersion matrix, which operates simultaneously both control charts, a chart controlling variance components and a chart controlling correlation coefficients. Our numerical results show that the proposed combined procedure is efficient for detecting changes in both variances and correlation coefficients of dispersion matrix.

• Asian Journal of Innovation and Policy
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• v.5 no.3
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• pp.251-274
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• 2016

Practical methodology for categorizing collaborative disciplines or research in a quantitative manner is presented by developing a Correlation Matrix of Major Disciplines (CMMD) using bibliometric data collected between 2009 and 2014. First, 21 major disciplines in science and engineering are defined based on journal publication frequency. Second, major disciplines using a comparing discipline correlation matrix is created and correlation score using CMMD is calculated based on an analyzer function that is given to the matrix elements. Third, a correlation between the major disciplines and 14 research fields using CMMD is calculated for validation. Collaborative researches are classified into three groups by partially accepting the definition of pluri-discipline from peer review manual, European Science Foundation, inner-discipline, inter-discipline and cross-discipline. Applying simple categorization criteria identifies three groups of collaborative research and also those results can be visualized. Overall, the proposed methodology supports the categorization for each research field.

• International journal of advanced smart convergence
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• v.9 no.4
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• pp.42-51
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• 2020

In this paper, the authors propose the pseudo complex correlation coefficient (PCCC) of the two complex random variables (RV), because the four real correlation coefficients (RCC) of the corresponding four real RVs cannot be obtained only from the complex correlation coefficient (CCC) of given two complex RV. Such observation is motivated by the general statement; "The complex jointly-Gaussian random M-vector cannot be completely described by the complex covariance matrix, even though the real Gaussian random 2M-vector can be completely descried by the real covariance matrix. Therefore, in order to describe completely the complex jointly-Gaussian random M-vector, we need an additional matrix, namely the complex pseudo-covariance matrix, along with the complex covariance matrix." Then, we apply PCCC to correlated information sources (CIS) for non-orthogonal multiple access (NOMA) in 5G system, and investigate impact of the proposed PCCC on the achievable data rate of the stronger channel user in the conventional successive interference cancellation (SIC) NOMA with CIS. It is shown that for the given same CCC, the achievable data rates with the different PCCC are different, because the corresponding RCC are different. We also show that as the absolute value of the same CCC increases, the impact of the different PCCC becomes more significant.

• Journal of information and communication convergence engineering
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• v.3 no.3
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• pp.115-118
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• 2005

In this study, the proposed algorithm is a correlativity signal in a mobile wireless channel that has estimated the direction of arrival. The proposed algorithm applied the space average method in a MUSIC algorithm. The diagonal matrix of the space average method was changed to inverse the matrix and to obtain a new signal correlation matrix. The existing algorithm was analyzed and compared by applying a proposed signal correlation matrix to estimate the direction of arrival in a MUSIC algorithm. The experiment resulted in a proposed algorithm with a min-norm method resolution at more than $5^{\circ}$. It improved more than $2^{\circ}$ in a MUSIC algorithm.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.7
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• pp.491-499
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• 2018

The conventional calibration method for a noise figure analyzer is to use a noise source. This method is accompanied by a significant irregular ripple in the measurement results, because it does not consider the mismatch of the noise source and noise figure analyzer during calibration. A novel calibration method of the noise figure analyzer is proposed that considers the mismatch between the noise power and noise figure analyzer. A novel noise correlation matrix measurement technique using this method is also proposed. The method determines the noise correlation matrix and the gain of the uncorrected noise figure analyzer using uncorrected noise powers. Then, having determined the gain and noise correlation matrix, the effects of noise figure analyzers were corrected in the measurement results of the noise correlation matrix for the device under test (DUT). Through the proposed method, the measured noise parameters of a DUT showed the same degree of irregular ripples as the result of using the relative noise ratio.

• Bulletin of the Korean Chemical Society
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• v.24 no.9
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• pp.1345-1350
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• 2003

Principal component analysis based two-dimensional (PCA-2D) correlation analysis is applied to FTIR spectra of polystyrene/methyl ethyl ketone/toluene solution mixture during the solvent evaporation. Substantial amount of artificial noise were added to the experimental data to demonstrate the practical noise-suppressing benefit of PCA-2D technique. 2D correlation analysis of the reconstructed data matrix from PCA loading vectors and scores successfully extracted only the most important features of synchronicity and asynchronicity without interference from noise or insignificant minor components. 2D correlation spectra constructed with only one principal component yield strictly synchronous response with no discernible a asynchronous features, while those involving at least two or more principal components generated meaningful asynchronous 2D correlation spectra. Deliberate manipulation of the rank of the reconstructed data matrix, by choosing the appropriate number and type of PCs, yields potentially more refined 2D correlation spectra.

• The Korean Journal of Applied Statistics
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• v.33 no.3
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• pp.281-296
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• 2020

Repeated outcomes from the same subjects are referred to as longitudinal data. Analysis of the data requires different methods unlike cross-sectional data analysis. It is important to model the covariance matrix because the correlation between the repeated outcomes must be considered when estimating the effects of covariates on the mean response. However, the modeling of the covariance matrix is tricky because there are many parameters to be estimated, and the estimated covariance matrix should be positive definite. In this paper, we consider analysis of multivariate longitudinal data via two modeling methodologies for the covariance matrix for multivariate longitudinal data. Both methods describe serial correlations of multivariate longitudinal outcomes using a modified Cholesky decomposition. However, the two methods consider different decompositions to explain the correlation between simultaneous responses. The first method uses enhanced linear covariance models so that the covariance matrix satisfies a positive definiteness condition; in addition, and principal component analysis and maximization-minimization algorithm (MM algorithm) were used to estimate model parameters. The second method considers variance-correlation decomposition and hypersphere decomposition to model covariance matrix. Simulations are used to compare the performance of the two methodologies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.11
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• pp.2657-2675
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• 2013

A novel neighbor selection-based fingerprinting algorithm using matrix correlation (MC) for Wi-Fi localization is presented in this paper. Compared with classic fingerprinting algorithms that usually employ a single received signal strength (RSS) sample, the presented algorithm uses multiple on-line RSS samples in the form of a matrix and measures correlations between the on-line RSS matrix and RSS matrices in the radio-map. The algorithm makes efficient use of on-line RSS information and considers RSS variations of reference points (RPs) for localization, so it offers more accurate localization results than classic neighbor selection-based algorithms. Based on the MC algorithm, an error estimation method using artificial neural network is also presented to fuse available information that includes RSS samples and localization results computed by the MC algorithm and model the nonlinear relationship between the available information and localization errors. In the on-line phase, localization errors are estimated and then used to correct the localization results to reduce negative influences caused by a static radio-map and RP distribution. Experimental results demonstrate that the MC algorithm outperforms the other neighbor selection-based algorithms and the error estimation method can reduce the mean of localization errors by nearly half.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.4A
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• pp.423-428
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• 2004

High-data rate and high speed communication techniques are required for wireless mobile communication systems to provide multimedia services. A multiple antenna technology may be used to meet this demand. In this paper, a method for performance improvement of a WCDMA downlink system using space time block coding is proposed in quasi-static Rayleigh fading channels. The proposed receiver uses the cross correlation matrix obtained by each finger corresponding to multi paths. To obtain maximum diversity gain, the inverse of cross correlation matrix and the Hermitian matrix of the channel matrix for each path arc computed, and then applied to received signals. Various simulation results show that the proposed receiver outperforms a conventional receiver in Rayleigh fading channels.

• Journal of Periodontal and Implant Science
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• v.43 no.1
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• pp.24-29
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• 2013

Purpose: Matrix metalloproteinases (MMPs) are capable of degrading extracellular matrix, and they are inducible enzymes depending on an inflammatory environment such as periodontitis and bacterial infection in periodontal tissue. Gingival inflammation has been postulated to be correlated with the production of MMP-2 and MMP-9. The objective of this study was to quantify the expression and activity of MMP-9 and -2, and to determine the correlation between activity and expression of these MMPs in human gingival tissues with periodontitis. Methods: The gingival tissues of 13 patients were homogenized in $500{\mu}L$ of phosphate buffered saline with a protease inhibitor cocktail. The expression and activity of MMP-2 and -9 were measured by enzyme-linked immunosorbent assay and Western blot analysis, and quantified by a densitometer. For the correlation line, statistical analysis was performed using the Systat software package. Results: MMP-9 was highly expressed in all gingival tissue samples, whereas MMP-2 was underexpressed compared with MMP-9. MMP-9 activity increased together with the MMP-9 expression level, with a positive correlation (r=0.793, P=0.01). The correlation was not observed in MMP-2. Conclusions: The expression of MMP-2 and -9 might contribute to periodontal physiological and pathological processes, and the degree of MMP-9 expression and activity are predictive indicators relevant to the progression of periodontitis.