• 천문학회보
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• 제39권2호
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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.

• 한국임상수의학회지
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• 제27권4호
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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.

• 천문학회보
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• 제39권2호
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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.

• 전기학회논문지
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• 제59권4호
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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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• 제40권5호
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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.

• 한국통신학회논문지
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• 제33권7C호
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• pp.534-539
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• 2008

최근에 홀수인 소수 p, n=4k, 그리고 $d=((p^{2k}+1)/2)^2$에 대해서 Seo, Kim, No, Shin이 H-sequence와 d로 decimation한 부분 수열들 사이의 상관 분포를 유도하였다. 하지만 이러한 상관 분포로부터 수열군이 자명하게 결정되지는 않는다. 본 논문에서는 우선 위의 상관 특성을 유지하는 수열군을 선택하는 방법을 제시한다. 더 나아가서 이 수열군과 동일한 상관 특성을 가지면서도 더 큰 선형 복잡도를 갖는 수열군을 새롭게 생성할 것이다. 끝으로 3진 수열의 선형 복잡도를 특정 경우에 대해서 유도하고 이 경우 원래의 수열군보다 더 큰 선형 복잡도를 가짐을 보일 것이다.

• 천문학회보
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• 제41권1호
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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.

• 한국전자통신학회논문지
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• 제7권1호
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• pp.61-67
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• 2012

코드분할 다중접속(CDMA) 시스템에서 대역확산 기법으로 사용되는 확산수열은 다중접속 간섭을 줄이고 높은 보안성을 위하여 상호상관관계가 낮고 선형복잡도가 큰 것으로 채택하는 것이 바람직하다. 그러나 수열군의 개수를 늘리고 선형복잡도를 높이기 위해서는 상호상관관계를 어느 정도 높이는 것은 불가피하다. 본 논문에서는 수열군의 개수가 크고 선형복잡도가 높은 확장 Zeng 수열을 제안하고 그 상호상관관계를 분석한다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2009년도 학술발표회 초록집
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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.

• 산업기술연구
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• 제29권B호
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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.