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Potential of Thermal Imaging to Detect Complications in Diabetes
Published in U. Snekhalatha, K. Palani Thanaraj, Kurt Ammer, Artificial Intelligence-Based Infrared Thermal Image Processing and Its Applications, 2023
U. Snekhalatha, K. Palani Thanaraj, Kurt Ammer
Furthermore, Hernandez-Contreras et al. created a comprehensive plantar thermograms database which is available in public domain for aiding researchers in the development of automated detection of diabetic foot problems based on intelligent systems. The dataset contains 334 individual plantar thermograms from 122 DM and 45 control subjects. The images are pre-processed such that the foot is isolated from the background radiation and manually corrected to work directly with it (D. A. Hernandez-Contreras et al., 2019, D. A. Hernandez-Contreras et al., 2017). Figure 6.8 shows the sample thermal images of plantar regions provided in the dataset. Table 6.3 shows an excerpt of temperature information provided in the original dataset of the control group for different angiosome zones. Table 6.4 shows an excerpt of temperature information provided in the original dataset of the diabetic group for different angiosome zones. Exploratory data analysis of this database is provided here for more information to understand the correlations between different ROI of the foot regions between the two groups. Here, we calculate the Kendall correlation rank coefficient for the two subject groups. Kendall rank correlation coefficient (τ) measures the ordinal association between two variables. The value lies between −1 and +1, −1 indicating total negative correlation, 0 indicating no correlation, and 1 indicating total positive correlation. To calculate τ for two variables “X” and “Y,” one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
Study of the modeling method of wind power time series based on correlation
Published in Rodolfo Dufo-López, Jaroslaw Krzywanski, Jai Singh, Emerging Developments in the Power and Energy Industry, 2019
In addition to the linear correlation coefficient, there are other indicators to measure the correlation coefficient, comparing the Kendall rank correlation coefficient and Spearman rank correlation coefficient between the original data, and finding that the normal Copula function is closer to the original data. The normal Copula function with a linear correlation coefficient of ρ=0.9971better reflects the wind power dependence at adjacent time points.
Curve Fitting and Regression Analysis
Published in Bilal M. Ayyub, Richard H. Mccuen, Numerical Analysis for Engineers, 2015
Bilal M. Ayyub, Richard H. Mccuen
Many indexes of correlation exist. The method that is used most frequently is the Pearson product-moment correlation coefficient. Non-parametric correlation indexes include the contingency coefficient, the Spearman rank correlation coefficient, and the Kendall rank correlation coefficient. Only the Pearson correlation coefficient is considered in this chapter.
Surface drainage evaluation of asphalt pavement using a new analytical water film depth model
Published in Road Materials and Pavement Design, 2020
Wenting Luo, Lin Li, Kelvin C.P. Wang, Changqin Wei
A statistic method, Kendall correlation test, is applied to verify the effectiveness of the proposed new analytical WFD model. Kendall rank correlation coefficient is a statistic used to measure the ordinal association between two measured quantities, which is ranged from 0 to 1. The large value of the correlation coefficient presents a high correlation between two tested quantities, while the small value of the correlation coefficient presents a low correlation between two tested quantities. There are five distinct of levels (α) in scale for significant coefficient: 0–0.001, 0.001–0.01, 0.01–0.05, 0.05–0.1, and 0.1–1. The mostly widely used distinct level is α = 0.05, which is selected as the distinct level in this study. If distinct level is smaller than 0.05, the significant correlation can be considered as ‘existence’.
Probabilistic model-checking based reliability analysis for failure correlation of multi-state systems
Published in Quality Engineering, 2020
Rongxi Wang, Zezhou Tang, Jianmin Gao, Zhiyong Gao, Zhen Wang
The rank correlation measure based on the Copula function reflects the monotonic dependence between variables. This measure is a nonlinear measure of multiple random variables, and it remains constant under nonlinear monotonic transformation. Furthermore, it has good statistical properties and is more widely used than linear correlation coefficients. The most well-known of the rank correlation coefficients is the Kendall rank correlation coefficient which is a consistency-based correlation measure. In data sample processing, indicates the difference between the probabilities that two randomly selected test values are the same or different from
Multi-factor joint return period of rainstorms and its agricultural risk analysis in Liaoning Province, China
Published in Geomatics, Natural Hazards and Risk, 2019
Yu Feng, Ying Li, Zhiru Zhang, Shiyu Gong, Meijiao Liu, Fei Peng
When we use Copula functions to join, the first step is to measure correlation. Commonly used correlation measurement coefficient of Copula functions includes four types, which are Kendall rank correlation coefficient, Spearman rank correlation coefficient, upper tail correlation coefficient and lower tail correlation coefficient. The reviewer proposes that in the extreme analysis, more consideration is given to the upper rank–rank correlation coefficient, so in the study, we take into account the Copula functions with a tail symmetric structure and the Copula functions without a tail symmetry structure. In this article, the correlation coefficient of five common Copula functions is tested. The Spearman rank correlation coefficient of various Copula functions is measured, and then Frank Copula function is selected for joint analysis. We use different Copula functions to combine the rainstorm factors of all sites in Liaoning Province. Taking rainstorm volume amount 250 mm, rainstorm days 3 d and rainstorm intensity 83 mm d−1 as example, the theoretical return recurrence period and actual return period under the given rainstorm combination are verified by comparison and verification (Table 11). Therefore, after considering the tail rank correlation, the suitable Copula functions is selected to combine the multiple elements of the rainstorm in each site. There is a huge difference between the theoretical return period and the actual return period. In comparison, Frank-Copula is the most suitable one for the multiple elements of Dandong station rainstorm combined with the Copula, because the error rate is the smallest.