• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.6 no.6
• /
• pp.1649-1667
• /
• 2012

In order to improve the accuracy and universality of the flow metric correlation analysis, this paper firstly analyzes the characteristics of Internet flow metrics as random variables, points out the disadvantages of Pearson Correlation Coefficient which is used to measure the correlation between two flow metrics by current researches. Then a method based on Symmetrical Uncertainty is proposed to measure the correlation between two flow metrics, and is extended to measure the correlation among multi-variables. Meanwhile, the simulation and polynomial fitting method are used to reveal the threshold value between different correlation degrees for SU method. The statistical analysis results on the common flow metrics using several traces show that Symmetrical Uncertainty can not only represent the correct aspects of Pearson Correlation Coefficient, but also make up for its shortcomings, thus achieve the purpose of measuring flow metric correlation quantitatively and accurately. On the other hand, reveal the actual relationship among fourteen common flow metrics.

• Journal of Fisheries and Marine Sciences Education
• /
• v.21 no.1
• /
• pp.121-133
• /
• 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.

• Journal of Yeungnam Medical Science
• /
• v.35 no.1
• /
• pp.63-69
• /
• 2018

Background: Kinovea software that tracking semi-automatically the motion in video screen has been used to study motion-related tasks in several studies. However, the validation of this software in open field test to assess locomotor activity have not been studied yet. Therefore, this study aimed to examine the reliability and validity of this software in analyzing locomotor activities. Methods: Thirty male Institute Cancer Research mice were subjected in this study. The results examined by this software and the classical method were compared. Test-retest reliability and inter-rater reliability were analyzed with Pearson's correlation coefficient and intraclass correlation coefficient (ICC). The validity of this software was analyzed with Pearson's correlation coefficient. Results: This software showed good test-retest reliability (ICC=0.997, 95% confidence interval [CI]=0.975-0.994, p<0.001). This software also showed good inter-rater reliability (ICC=0.987, 95% CI=0.973-0.994, p<0.001). Furthermore, in three analyses for the validity of this software, there were significant correlations between two methods (Pearson's correlation coefficient=0.928-0.972, p<0.001). In addition, this software showed good reliability and validity in the analysis locomotor activity according to time interval. Conclusion: This study showed that this software in analyzing drug-induced locomotor activity has good reliability and validity. This software can be effectively used in animal study using the analysis of locomotor activity.

• Korean Journal of Food Science and Technology
• /
• v.50 no.2
• /
• pp.244-247
• /
• 2018

Quantitative analysis for non-host infection bacteriophage was conducted for their enumeration. Flow cytometry and epifluorescence microscopy (EPM) were selected as counting methods. Correlation analysis was performed based on the plaque assay method on the existing host infection and consisted of Pearson correlation statistical analysis, regression analysis, and difference analysis. Analyses of 12 samples with flow cytometry and plaque assay methods showed that there was a correlation of 96.7% with Pearson correlation value r=0.967, $R^2$ 0.9352, and difference value of 1.063. Analyses of 12 samples with EPM and plaque assay methods showed that there was a correlation of 99.0% with Pearson correlation value r=0.990, $R^2$ 0.9811, and difference value of 1.605. Therefore, flow cytometry and epifluorescence microscopy would be effective for enumeration of Weissella cibaria bacteriophage with plaque assay.

• Journal of KIISE
• /
• v.41 no.10
• /
• pp.799-809
• /
• 2014

In this paper, we address the problem of computing Pearson correlation coefficients and Spearman's rank correlation coefficients in a secure manner while data providers preserve privacy of their own data in distributed environment. For a data mining or data analysis in the distributed environment, data providers(data owners) need to share their original data with each other. However, the original data may often contain very sensitive information, and thus, data providers do not prefer to disclose their original data for preserving privacy. In this paper, we formally define the secure correlation computation, SCC in short, as the problem of computing correlation coefficients in the distributed computing environment while preserving the data privacy (i.e., not disclosing the sensitive data) of multiple data providers. We then present SCC solutions for Pearson and Spearman's correlation coefficients using secure scalar product. We show the correctness and secure property of the proposed solutions by presenting theorems and proving them formally. We also empirically show that the proposed solutions can be used for practical applications in the performance aspect.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.39 no.12
• /
• pp.1199-1206
• /
• 2015

In this study, the exhaust gas of a diesel engine operating on biodiesel(BD) fuel(a mixture of diesel and soybean oil) was investigated for different fuel mixing ratios in the range of BD3 to BD100. The experiments were conducted using injection pressures of 400, 600, 800, 1000, and 1200 bar. The Pearson correlation coefficient and Spearman rank-order correlation coefficient were used to quantify the NOx and Soot emissions based on the fuel mixing ratio and injection pressure. Consequently, the Pearson correlation coefficient obtained for NOx and Soot emissions according to the mixing ratio and injection pressure was -0.811 and the corresponding Spearman rank-order correlation coefficient was -0.884, which indicated that the correlation of the NOx and Soot emissions was linear. Thus, the NOx and Soot have a trade-off relationship. Moreover, at each injection pressure, the Pearson correlation coefficient was a negative number, which indicated an inversely proportional relationship between NOx and Soot.

• Tribology and Lubricants
• /
• v.18 no.4
• /
• pp.285-290
• /
• 2002

Integrated condition monitoring is required to monitor effectively the machine conditions since machine failures could not be monitored accurately by any single measurement parameter. Application of various condition monitoring techniques is therefore preferred in many cases in order to diagnosis the machine condition. However it inevitably requires lots of maintenance cost and sometimes it could be proved to over-maintenance unnecessarily. This could happen especially when one measurement parameter closely correlates to another. Therefore correlation analysis of various monitoring parameters has to be performed to improve the reliability of diagnosis. In this work, Pearson correlation coefficient was used to analyze the correlation between condition monitoring parameters of an over-loaded machine system where the vibration, wear and temperature were monitored simultaneously. The result showed that Pearson correlation coefficient could be regarded as a good measure for evaluating the availability of condition monitoring technology.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
• /
• 2001.11a
• /
• pp.252-259
• /
• 2001

Integrated condition monitoring is required to monitor effectively the machine conditions since machine failures could not be monitored accurately by any single measurement parameter. Application of various condition monitoring techniques is therefore preferred in many cases in order to diagnosis the machine condition. However it inevitably requires lots of maintenance cost and sometimes it could be proved to over-maintenance unnecessarily. This could happen especially when one measurement parameter closely correlates to another. Therefore correlation analysis of various monitoring parameters has to be performed to improve the reliability of diagnosis. In this work, Pearson correlation coefficient was used to analyze the correlation between condition monitoring parameters of an over-loaded machine system where the vibration, wear and temperature were monitored simultaneously. The result showed that Pearson correlation coefficient could be regarded as a good measure for evaluating the availability of condition monitoring technology.

• Journal of Oriental Neuropsychiatry
• /
• v.27 no.3
• /
• pp.131-146
• /
• 2016

Objectives: This study was performed to determine the correlation between Instrument on Pattern Identification for Depression and Psychological tests by Pearson Correlation Coefficient and Regression analysis.Methods: Two assessors carried out the evaluation using the instrument on pattern identification for depression. They also performed the following psychological tests: Beck Depression Inventory (BDI), State-Trait Anxiety Inventory (STAI), State-Trait Anger Expression Inventory (STAXI), the Temperament and Character Inventory (TCI), Insomnia Severity Index (ISI_Total), Self-disclosure Inventory, subjective well-being Inventory, Health perception Inventory, and Personality Assessment Inventory (PAI). A total of 167 participants who got HAM-D score over 12 were targeted for the evaluation. Our research team carried out Pearson correlation coefficient analyses and regression analysis between pattern identification for depression and Psychological tests. We listed the results by descending order and interpreted the results.Results: Pearson correlation analysis revealed the following results: 1) Stagnation of Liver Gi was associated with BDI (0.60) and STAI (0.55); 2) Dual Deficiency of the Heart and Spleen was associated with BDI (0.60), ISI_Total (0.52), and STAI (0.42); 3) Relieving stagnation of Phlegm-Gi was associated with BDI (0.65), STAI (0.54), and Subjective well-being inventory (−0.52); 4) Gi-deficiency Mingled with sputum was associated with BDI (0.50), ISI_Total (0.40), and STAI (0.395); 5) Stagnant Gi transforming into fire was associated with BDI (0.56), STAI_TR (0.51), and Health perception Inventory (−0.458); 6) Yin-Deficiency with Effulgent Fire was associated with BDI (0.55), ISI_total (0.54), and STAI (0.41).Conclusions: Through correlation analysis between Instrument on Pattern Identification for Depression and Psychological tests, we could suggest a System for Oriental Medical Diagnosis for Depression.

• International Journal of Contents
• /
• v.18 no.1
• /
• pp.17-26
• /
• 2022

With the acceleration of urbanization and industrialization, air pollution has become increasingly serious, and the pollution control situation is not optimistic. Climate change has become a major global challenge faced by mankind. To actively respond to climate change, China has proposed carbon peak and carbon neutral goals. However, atmospheric pollutants and meteorological factors that affect air quality are complex and changeable, and the complex relationship and correlation between them must be further clarified. This paper uses China's 2013-2018 high-resolution air pollution reanalysis open data set, as well as statistical methods of the Pearson Correlation Coefficient (PCC) to calculate and visualize the design and analysis of environmental monitoring big data, which is intuitive and it quickly demonstrated the correlation between pollutants and meteorological factors in the temporal and spatial sequence, and provided convenience for environmental management departments to use air quality routine monitoring data to enable dynamic decision-making, and promote global climate governance. The experimental results show that, apart from ozone, which is negatively correlated, the other pollutants are positively correlated; meteorological factors have a greater impact on pollutants, temperature and pollutants are negatively correlated, air pressure is positively correlated, and the correlation between humidity is insignificant. The wind speed has a significant negative correlation with the six pollutants, which has a greater impact on the diffusion of pollutants.