• The Bulletin of The Korean Astronomical Society
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• v.39 no.2
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• pp.105.1-105.1
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• 2014

The study on linear correlation between auxiliary channels in LIGO data has been performed by using Pearson's correlation coefficient, generating a correlation map.

• Journal of Veterinary Clinics
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• v.27 no.4
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• pp.427-434
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• 2010

Correlation is a technique used to measure the strength or the degree of closeness of the linear association between two quantitative variables. Common misuses of this technique are highlighted. Linear regression is a technique used to identify a relationship between two continuous variables in mathematical equations, which could be used for comparison or estimation purposes. Specifically, regression analysis can provide answers for questions such as how much does one variable change for a given change in the other, how accurately can the value of one variable be predicted from the knowledge of the other. Regression does not give any indication of how good the association is while correlation provides a measure of how well a least-squares regression line fits the given set of data. The better the correlation, the closer the data points are to the regression line. In this tutorial article, the process of obtaining a linear regression relationship for a given set of bivariate data was described. The least square method to obtain the line which minimizes the total error between the data points and the regression line was employed and illustrated. The coefficient of determination, the ratio of the explained variation of the values of the independent variable to total variation, was described. Finally, the process of calculating confidence and prediction interval was reviewed and demonstrated.

• The Bulletin of The Korean Astronomical Society
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• v.39 no.2
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• pp.107.2-107.2
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• 2014

The study on non-linear correlation between auxiliary channels in LIGO data has been performed by using mutual information coefficient, generating a correlation map.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.4
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• pp.693-699
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• 2010

General transformer's life is known as paper insulation' life. If a transformer is degraded by these aging factors, it is known that electrical, mechanical and chemical characteristics for transformer's oil-paper are changed. When the kraft paper is aged, the cellulose polymer chains break down into shorter lengths. It causes decrease in both tensile strength and degree of polymerization of paper insulation. The paper breakdown is accompanied by an increase in the content of furanic compounds within the dielectric liquid. In this paper it is aimed at analysis on correlation between aging characteristics for insulating diagnosis of thermally aged paper. For investigating the accelerated aging process of oil-paper samples accelerating aging cell was manufactured for estimating variation of paper insulation during 500 hours at $140^{\circ}C$ temperature. To derive the results, it was performed analysis such as tensile strength(TS), depolymerization(DP), dielectric strength(DS), relative permittivity, water content(WC) and furan compound(FC) for aged paper. Also for analyzing correlation between insulating degradation characteristics, we used linear regression method. As as results of linear regression analysis, there was a close correlation between TS and DP. WC, FC. But dielectric strength was a weak correlation with aging time.

• ETRI Journal
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• v.40 no.5
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• pp.570-581
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• 2018

This work analyzes the performance of implementable detectors for the multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) technique under specific and realistic operation system conditions, including antenna correlation and array configuration. A time-domain channel model was used to evaluate the system performance under realistic communication channel and system scenarios, including different channel correlation, modulation order, and antenna array configurations. Several MIMO-OFDM detectors were analyzed for the purpose of achieving high performance combined with high capacity systems and manageable computational complexity. Numerical Monte Carlo simulations demonstrate the channel selectivity effect, while the impact of the number of antennas, adoption of linear against heuristic-based detection schemes, and the spatial correlation effect under linear and planar antenna arrays are analyzed in the MIMO-OFDM context.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.7C
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• pp.534-539
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• 2008

For an odd prime p, n=4k, and $d=((p^{2k}+1)/2)^2$, Seo, Kim, No, and Shin derived the correlation distribution of p-ary m-sequence of period $p^n-1$ and its decimated sequences by d. In this paper, two new families of p-ary sequences with family size $p^{2k}$ and maximum correlation magnitude $[2]sqrt{p^n}-1$ are constructed. The linear complexity of new p-ary sequences in the families are derived in the some cases and the upper and lower bounds of their linear complexity for general cases are presented.

• The Bulletin of The Korean Astronomical Society
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• v.41 no.1
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• pp.79.3-80
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• 2016

We have investigated the correlation analysis between global temperature anomaly and two main factors: geomagnetic activity (aa index) of Earth external factor and CO2 of Earth internal factor. For this, we used NOAA Global Surface Temperature anomaly (Ta) data from 1868 to 2015. The aa index indicates the geomagnetic activity measured at two anti-podal subauroral stations (Canberra Australia and Hartland England) and the CO2 data come from historical ice core records and NOAA/ESRL data. From the comparison between (Ta) and aa index, we found several interesting things, First, the linear correlation coefficient between two parameters increases until 1985 and then decreases rapidly. Second, the scattered plot between two parameters shows a boundary of the correlation tendency (positive and negative correlation) near 1985. A partial correlation of (Ta) and two main factors (aa index, CO2) also shows that the geomagnetic effect (aa index) is dominant until about 1985 and the CO2 effect becomes much more important after then. These results indicate that the CO2 effect become very an important factor since at least 1985. For a further analysis, we simply assume that Ta = Ta(aa)+Ta(CO2) and made a linear regression between (Ta) and aa index from 1868 to 2015. A linear model is then made from the linear regression between energy consumption (a proxy of CO2 effect) and Ta-Ta(aa) since 1985. Our results will be discussed in view of the prediction of global warming.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.1
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• pp.61-67
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• 2012

Spreading sequence is used for spreading spectrum in CDMA. For the purpose of minimizing multiple access interference and expanding linear span of the sequences, it is desirable to use such sequences with low correlation and high linear span. To obtain large family size and high linear span, the values of the correlation function of the sequences is more complex. In this paper, we propose the extended Zeng sequences with large family size and high linear span and analyze the correlation of the sequences.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.1306-1310
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• 2009

The roughness coefficients were estimated by the Manning's equation for the measured stage and flow velocity of Bocheong stream basin in Kum river. The relationships between the estimated roughness coefficients and the geomorphologic factors were formulated by the linear, logarithmic, exponential and power type function, thereafter correlation equations were presented. The correlation analysis was performed between the measured stream length and the basin area of Bocheong stream basin by the linear, logarithmic, exponential and power type function, and correlation equation for the stream length was given. The roughness coefficient has strong correlationship with stream slope, but low correlation coefficients with stream length and basin area. For the correlationship with the roughness coefficients and the stream slope, the logarithmic type function has the smallest correlation coefficient, on the other hand, the exponential type function has the largest correlation coefficient. For the relationship between the stream length and the basin area, the correlation coefficient of the logarithmic type function shows the smallest value, linear type function shows the largest value.

• Journal of Industrial Technology
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• v.29 no.B
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• pp.17-23
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• 2009

In many cases, clustering algorithms have been used for extracting and discovering useful information from non-linear data. They have made a great effect on performances of the systems dealing with non-linear data. Thus, this paper presents a new approach called hierarchically structured clustering algorithm, and it is applied to the prediction system for non-linear time series data. The proposed hierarchically structured clustering algorithm (called HCKA: Hierarchical Cross-correlation and K-means clustering Algorithms) in which the cross-correlation and k-means clustering algorithm are combined can accept the correlationship of non-linear time series as well as statistical characteristics. First, the optimal differences of data are generated, which can suitably reveal the characteristics of non-linear time series. Second, the generated differences are classified into the upper clusters for their predictors by the cross-correlation clustering algorithm, and then each classified differences are classified again into the lower fuzzy sets by the k-means clustering algorithm. As a result, the proposed method can give an efficient classification and improve the performance. Finally, we demonstrates the effectiveness of the proposed HCKA via typical time series examples.