Multiscale Sample Entropy of Two-Dimensional Decaying Turbulence

Kim, Ildoo

doi:10.3390/e23020245

Cited by 6 publications

(4 citation statements)

References 35 publications

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“…On the other hand, due to the popularity of SampEn in the analysis of temporary signals, the SampEn2D extension has had some visibility for the analysis of irregularity in images (details in References [24,[30][31][32][33]); some new methods have incorporated into their algorithm the calculation of SampEn2D, generating interesting alternatives, such as multiscale entropy (MSE2D) and its variant ModMSE2D [34]. However, expanding the SampEn1D method into the world of 2D data analysis or imaging should mean additional considerations, such as the number of m points taken as a pattern for comparison in SampEn1D; typically, m = 2 or m = 3 is less than the number of points (pixels) taken as a pattern in the case of SampEn2D.…”

Section: Espinosa Entropy Proposal (Espen) For 2dmentioning

confidence: 99%

Two-Dimensional EspEn: A New Approach to Analyze Image Texture by Irregularity

Espinosa

Bailón

Laguna

2021

Entropy

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Image processing has played a relevant role in various industries, where the main challenge is to extract specific features from images. Specifically, texture characterizes the phenomenon of the occurrence of a pattern along the spatial distribution, taking into account the intensities of the pixels for which it has been applied in classification and segmentation tasks. Therefore, several feature extraction methods have been proposed in recent decades, but few of them rely on entropy, which is a measure of uncertainty. Moreover, entropy algorithms have been little explored in bidimensional data. Nevertheless, there is a growing interest in developing algorithms to solve current limits, since Shannon Entropy does not consider spatial information, and SampEn2D generates unreliable values in small sizes. We introduce a proposed algorithm, EspEn (Espinosa Entropy), to measure the irregularity present in two-dimensional data, where the calculation requires setting the parameters as follows: m (length of square window), r (tolerance threshold), and ρ (percentage of similarity). Three experiments were performed; the first two were on simulated images contaminated with different noise levels. The last experiment was with grayscale images from the Normalized Brodatz Texture database (NBT). First, we compared the performance of EspEn against the entropy of Shannon and SampEn2D. Second, we evaluated the dependence of EspEn on variations of the values of the parameters m, r, and ρ. Third, we evaluated the EspEn algorithm on NBT images. The results revealed that EspEn could discriminate images with different size and degrees of noise. Finally, EspEn provides an alternative algorithm to quantify the irregularity in 2D data; the recommended parameters for better performance are m = 3, r = 20, and ρ = 0.7.

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Section: Espinosa Entropy Proposal (Espen) For 2dmentioning

confidence: 99%

Two-Dimensional EspEn: A New Approach to Analyze Image Texture by Irregularity

Espinosa

Bailón

Laguna

2021

Entropy

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“…The flowing soap film channel [1][2][3] is an attractive tool of experiment in testing the formation, self-organization, and evolution of vortices in two-dimensional flow, because it is easy to handle, convenient to visualize, and inexpensive. Because of these advantages, there are many research papers using soap film channels to investigate various phenomena including the wake structure [5][6][7], fluid-structure interaction [8,9], and turbulence [10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

The effect of menisci on vortex streets on soap film flows

Kim

2023

Physics of Fluids

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In soap film experiments, the insertion of an external object is necessary to produce vorticity. However, this insertion causes local thickness changes, or simply meniscus, near the object. Because the meniscus formation may alter the flow near the object, the characterization of a meniscus is of considerable importance for the accurate interpretation of data. In this study, we insert cylindrical cones made of aluminum, titanium, and glass to measure the size of the menisci by using a long-range microscope. In all material tested, we find that the size of the meniscus is less than 0.2 mm, much shorter than the capillary length. In addition, by comparing the formation of vortex streets behind objects of different materials, we conclude that the meniscus acts as an added length to the size of the object itself. This added length effect can be non-negligible if the size of an object is comparable to the size of a meniscus.

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“…The SamEn method is robust with noises and does not change easily when utilizing different parameters in the calculation process (Yentes et al., 2013). The applicability of SamEn to quantify the chaotic degree and complexity of a nonlinear system has been well examined in broad disciplines including physiological time‐series analysis (Eduardo Virgilio Silva & Otavio Murta, 2012; Lake et al., 2002; Richman & Moorman, 2000), de‐noising for hydrologic signals (Wang et al., 2014; Zhang et al., 2019), turbulent experimental data analysis (Kim, 2021), and even the stock markets study (Shi & Shang, 2013). In this study, the SamEn method was implied to investigate the spatial‐temporal distributions and variations of chaotic degrees within the air‐sea coupled nonlinear system in the tropical Pacific and the connections of SamEn values with the seasonal PB of SSTA.…”

Section: Introductionmentioning

confidence: 99%

A Nonlinear Cause for the Seasonal Predictability Barrier of SST Anomaly in the Tropical Pacific

Zhou

Hang

et al. 2022

JGR Oceans

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The seasonal Predictability Barrier (PB) of Sea Surface Temperature Anomaly (SSTA) is characterized by a rapid loss of prediction skills at a particular season in dynamic models. Here, the connection between seasonal PB and the inherent nonlinearity of SSTA was investigated using a statistical method known as Sample Entropy (SamEn). When the SamEn value is large, the chaotic degree of SSTA is high. In the Niño 3 and Niño 3.4 regions, the chaotic degree of SSTA was high in the spring; in the Niño 4 region, it was high in the summer. This result was consistent with the known PB occurrence season in these regions. The month when the chaotic degree of SSTA peaked moved westward longitudinally from March to June. This spatial‐temporal variation of the chaotic degree was consistent with that of the low SSTA variance and PB occurrence timing. Specifically, when the variance of SSTA was low, the low‐amplitude background SSTA signal was more dominant than the El Niño/Southern Oscillation (ENSO)‐related signal, the chaotic degree of SSTA was high, and the PB phenomenon occurred. Hence, the results indicated that the background SSTA signal was more chaotic than the ENSO‐related signal and the seasonal PB may result from the inherent low predictability of the chaotic background SSTA signal. Furthermore, the correlation coefficient between SSTA and shortwave flux anomaly showed similar variation along the longitude compared with the chaotic degree, which suggested that the seasonal connection between SSTA and atmospheric forcing may be responsible for the spatial‐temporal variation of the chaotic degree.

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Multiscale Sample Entropy of Two-Dimensional Decaying Turbulence

Cited by 6 publications

References 35 publications

Two-Dimensional EspEn: A New Approach to Analyze Image Texture by Irregularity

Two-Dimensional EspEn: A New Approach to Analyze Image Texture by Irregularity

The effect of menisci on vortex streets on soap film flows

A Nonlinear Cause for the Seasonal Predictability Barrier of SST Anomaly in the Tropical Pacific

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