On relationships between the Pearson and the distance correlation coefficients

Edelmann, Dominic; Móri, Tamás F.; Székely, Gábor

doi:10.1016/j.spl.2020.108960

Cited by 205 publications

(88 citation statements)

References 3 publications

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“…Meanwhile, values close to −1 means that increasing one variable decreases the other. As for interpretation, the correlation from 0.00 to 0.19 is considered very weak, 0.20 to 0.39 is weak, 0.40 to 0.69 is moderate, 0.70 to 0.89 is strong and 0.90 to 1 is very strong ( Bermudez-Edo et al, 2018 , Edelmann et al, 2021 ). Statistically the study showed high significance among the analyzed variables.…”

Section: Methodsmentioning

confidence: 99%

Use of geospatial tools to predict the risk of contamination by SARS-CoV-2 in urban cemeteries

Toscan

Neckel

Maculan

et al. 2022

Geoscience Frontiers

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Section: Methodsmentioning

confidence: 99%

Use of geospatial tools to predict the risk of contamination by SARS-CoV-2 in urban cemeteries

Toscan

Neckel

Maculan

et al. 2022

Geoscience Frontiers

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“…Therefore, there is an urgent need for a method that can accurately identify filled and unfilled grains. Liu et al 39 designed a method based on image analysis to measure grain plumpness by the grain shadow in four directions. In addition, some methods were proposed based on X-ray and thermal imaging 22,23 , but all these methods were identified in 2D imaging and could not provide more phenotypic information.…”

Section: Discussionmentioning

confidence: 99%

Cereal Grain 3D Point Cloud Analysis Method For Shape Extraction And Filled/Unfilled Grain Identification Based On Structured Light Imaging

Qin

Zhong-fu

Hua

et al. 2021

Preprint

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Cereals are the main food for mankind. The grain shape extraction and filled/unfilled grain recognition are meaningful for crop breeding and genetic analysis. The conventional measuring method is mainly manual, which is inefficient, labor-intensive and subjective. Therefore, a novel method was proposed to extract the phenotypic traits of cereal grains based on point clouds. First, a structured light scanner was used to obtain the grains point cloud data. Then, the single grain segmentation was accomplished by image preprocessing, plane fitting, region growth clustering. The length, width, thickness, surface area and volume was calculated by the specified analysis algorithms for grain point cloud. To demonstrate this method, experimental materials included rice, wheat and corn were tested. Compared with manual measurement results, the average measurement error of grain length, width and thickness was 2.07%, 0.97%, 1.13%, and the average measurement efficiency was about 9.6 seconds per grain. In addition, the grain identification model was conducted with 25 grain phenotypic traits, using 6 machine learning methods. The results showed that the best accuracy for filled/unfilled grain classification was 90.184%.The accuracy for indica and japonica identification was about 99.785%,while for different varieties identification was only 47.252%. Therefore, this method was proved to be an efficient and effective way for crop research.

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“…From the covariance matrix, the Pearson correlation coefficient [ 19 ] could be calculated as shown in Equation ( 7 ):

…”

Section: Methods and Facilitiesmentioning

confidence: 99%

“…whereX andȲ are the mean values of X and Y, respectively. From the covariance matrix, the Pearson correlation coefficient [19] could be calculated as shown in Equation 7:…”

Section: Video-tachometer Working Principlementioning

confidence: 99%

Video-Tachometer Methodology for Wind Turbine Rotor Speed Measurement

Natili

Castellani

Astolfi

et al. 2020

Sensors

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The measurement of the rotational speed of rotating machinery is typically performed based on mechanical adherence; for example, in encoders. Nevertheless, it can be of interest in various types of applications to develop contactless vision-based methodologies to measure the speed of rotating machinery. In particular, contactless rotor speed measurement methods have several potential applications for wind turbine technology, in the context of non-intrusive condition monitoring approaches. The present study is devoted exactly to this problem: a ground level video-tachometer measurement technique and an image analysis algorithm for wind turbine rotor speed estimation are proposed. The methodology is based on the comparison between a reference frame and each frame of the video through the covariance matrix: a covariance time series is thus obtained, from which the rotational speed is estimated by passing to the frequency domain through the spectrogram. This procedure guarantees the robustness of the rotational speed estimation, despite the intrinsic non-stationarity of the system and the possible signal disturbances. The method is tested and discussed based on two experimental environments with different characteristics: the former is a small wind turbine model (with a 0.45 m rotor diameter) in the wind tunnel facility of the University of Perugia, whose critical aspect is the high rotational speed (up to the order of 1500 RPM). The latter test case is a wind turbine with a 44 m rotor diameter which is part of an industrial wind farm: in this case, the critical point regards the fact that measurements are acquired in uncontrolled conditions. It is shown that the method is robust enough to overcome the critical aspects of both test cases and to provide reliable rotational speed estimates.

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On relationships between the Pearson and the distance correlation coefficients

Cited by 205 publications

References 3 publications

Use of geospatial tools to predict the risk of contamination by SARS-CoV-2 in urban cemeteries

Use of geospatial tools to predict the risk of contamination by SARS-CoV-2 in urban cemeteries

Cereal Grain 3D Point Cloud Analysis Method For Shape Extraction And Filled/Unfilled Grain Identification Based On Structured Light Imaging

Video-Tachometer Methodology for Wind Turbine Rotor Speed Measurement

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