scholarly journals The Classification of Inertinite Macerals in Coal Based on the Multifractal Spectrum Method

2019 ◽  
Vol 9 (24) ◽  
pp. 5509 ◽  
Author(s):  
Man Liu ◽  
Peizhen Wang ◽  
Simin Chen ◽  
Dailin Zhang
Keyword(s):  
Multifractal Spectrum ◽  
Classification Model ◽  
Support Vector ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Microscopic Images ◽  
Heterogeneous Nature ◽  
Occurrence Matrix ◽  
Detrended Fluctuation

Considering the heterogeneous nature and non-stationary property of inertinite components, we propose a texture description method with a set of multifractal descriptors to identify different macerals with few but effective features. This method is based on the multifractal spectrum calculated from the method of multifractal detrended fluctuation analysis (MF-DFA). Additionally, microscopic images of inertinite macerals were analyzed, which were verified to possess the property of multifractal. Simultaneously, we made an attempt to assess the influences of noise and blur on multifractal descriptors; the multifractal analysis was proven to be robust and immune to image quality. Finally, a classification model with a support vector machine (SVM) was built to distinguish different inertinite macerals from microscopic images of coal. The performance evaluation proves that the proposed descriptors based on multifractal spectrum can be successfully applied in the classification of inertinite macerals. The average classification precision can reach 95.33%, higher than that of description method with gray level co-occurrence matrix (GLCM; about 7.99%).

scholarly journals Analysis and classification of droplet characteristics from atomizers using multifractal analysis

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
V. Godavarthi ◽  
K. Dhivyaraja ◽  
R. I. Sujith ◽  
M. V. Panchagnula
Keyword(s):  
Single Point ◽  
Multifractal Spectrum ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Arrival Times ◽  
Log Normal ◽  
Pressure Swirl ◽  
Detrended Fluctuation ◽  
Log Normal Distribution

Abstract Atomizers find applications in diverse fields such as agriculture, pharmaceutics and combustion. Among the most commonly found atomizer classes of designs are pressure swirl, airblast and ultrasonic atomizers. However, it has thus far not been possible to identify the class of an atomizer from spray characteristics. We perform multifractal detrended fluctuation analysis on the droplet inter-arrival times, diameters and axial velocities of pressure swirl, airblast and ultrasonic nebulizer sprays to quantify the differences in complexity in the respective signals. We show that the width of the multifractal spectrum of the signals of droplet diameters and the inter-arrival times, measured at the edge of the spray are robust atomizer identifiers. Further, we show the presence of correlations among the droplet diameters which are otherwise considered as random or derived from a log-normal distribution. This study can be further generalized to classify fluid mechanical systems or biological sprays using an appropriately chosen single point measurement in the flow field.

Fish Sound Characterization Using Multifractal Detrended Fluctuation Analysis

2020 ◽  
Vol 19 (01) ◽  
pp. 2050009 ◽  
Author(s):  
Kranthikumar Chanda ◽  
Shubham Shet ◽  
Bishwajit Chakraborty ◽  
Arvind K. Saran ◽  
William Fernandes ◽  
...  
Keyword(s):  
Hurst Exponent ◽  
Detrended Fluctuation Analysis ◽  
Linear Method ◽  
Multifractal Spectrum ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Generalized Hurst Exponent ◽  
Estuarine Systems ◽  
Detrended Fluctuation ◽  
Sound Data

This work involves the application of a non-linear method, multifractal detrended fluctuation analysis (MFDFA), to describe fish sound data recorded from the open waters of two major estuarine systems. Applying MFDFA, the second-order Hurst exponent [Formula: see text] values are found to be [Formula: see text] and [Formula: see text] for the fish families Batrachoididae (common name: Toadfish) and Sciaenidae (common name: Croakers, drums), respectively. The generalized Hurst exponent [Formula: see text]-related width parameters [Formula: see text] are found to be [Formula: see text] and [Formula: see text], respectively, for toadfish and Sciaenidae vocalizations, implying greater heterogeneity and multifractal characteristics. The results suggest that the Sciaenidae fish calls are smoother in comparison with Batrachoididae. Clustering of multifractal spectrum-related parameters with respect to toadfish and Sciaenidae vocalization characteristics is observed in this analyses.

Classification of Epileptic EEG Based on Detrended Fluctuation Analysis and Support Vector Machine

2011 ◽  
Vol 27 (2) ◽  
pp. 175-182 ◽  
Author(s):  
Dongmei CAI ◽  
Weidong ZHOU ◽  
Shufang LI ◽  
Jiwen WANG ◽  
Guijuan JIA ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Detrended Fluctuation Analysis ◽  
Support Vector ◽  
Fluctuation Analysis ◽  
Detrended Fluctuation

scholarly journals Different Multifractal Scaling of the 0 cm Average Ground Surface Temperature of Four Representative Weather Stations over China

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Lei Jiang ◽  
Xia Zhao ◽  
Nana Li ◽  
Fei Li ◽  
Ziqi Guo
Keyword(s):  
Surface Temperature ◽  
Ground Surface ◽  
Multifractal Spectrum ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Scaling Properties ◽  
Ground Surface Temperature ◽  
Multifractal Scaling ◽  
Weather Stations ◽  
Detrended Fluctuation

The temporal scaling properties of the daily 0 cm average ground surface temperature (AGST) records obtained from four selected sites over China are investigated using multifractal detrended fluctuation analysis (MF-DFA) method. Results show that the AGST records at all four locations exhibit strong persistence features and different scaling behaviors. The differences of the generalized Hurst exponents are very different for the AGST series of each site reflecting the different scaling behaviors of the fluctuation. Furthermore, the strengths of multifractal spectrum are different for different weather stations and indicate that the multifractal behaviors vary from station to station over China.

ABOUT THE EFFECTIVENESS OF DIFFERENT METHODS FOR THE ESTIMATION OF THE MULTIFRACTAL SPECTRUM OF NATURAL SERIES

2010 ◽  
Vol 20 (02) ◽  
pp. 331-339 ◽  
Author(s):  
ALEJANDRA FIGLIOLA ◽  
EDUARDO SERRANO ◽  
GUSTAVO PACCOSI ◽  
MARIEL ROSENBLATT
Keyword(s):  
Wavelet Analysis ◽  
Multifractal Spectrum ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Natural Systems ◽  
Fractal Properties ◽  
Wavelet Leaders ◽  
Self Similar ◽  
Detrended Fluctuation ◽  
Natural Series

Complex natural systems present characteristics of scalar invariance. This behavior has been experimentally verified and a large related bibliography has been reported. Multifractal Formalism is a way to evaluate this kind of behavior. In the past years, different numerical methods to estimate the multifractal spectrum have been proposed. These methods could be classified into those that originated from the wavelet analysis and others from numerical approximations like the Multifractal Detrended Fluctuation Analysis (MFDFA), proposed by Kantelhardt and Stanley. Recently, S. Jaffard and co-workers proposed the Wavelet Leaders (WL) method that exploits the potential of wavelet analysis and the efficiency of the Multiresolution Wavelet Schema. In a previous work, we checked that both methods are equivalent for estimating fractal properties in a series from singular measures. Now, we apply MFDFA and WL to natural signals with self-similar structures, but unknown multifractal spectrum. We observe that some differences appear in their respective estimations, particularly when the signals are corrupted with fractional Gaussian noise.

scholarly journals Multifractal Detrended Fluctuation Analysis of Insole Pressure Sensor Data to Diagnose Vestibular System Disorders

2021 ◽  
Author(s):  
Batuhan Günaydın ◽  
Serhat İkizoğlu
Keyword(s):  
Vestibular System ◽  
Pressure Sensor ◽  
Detrended Fluctuation Analysis ◽  
Human Life ◽  
Sensor Data ◽  
Support Vector ◽  
Substantial Part ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Detrended Fluctuation

Abstract The vestibular system (VS) is a sensory system that has a vital function in human life by serving to maintain balance. In this study, multifractal detrended fluctuation analysis (MFDFA) is applied to insole pressure sensor data collected from subjects in order to extract features to identify diseases related to VS dysfunction. We use the multifractal spectrum width as the feature to distinguish between healthy and diseased people. It is observed that multifractal behavior is more dominant and thus the spectrum is wider for healthy subjects, where we explain the reason as the long-range correlations of the small and large fluctuations of the time series for this group. We directly process the instantaneous pressure values to extract features in contrast to studies in the literature where gait analysis is based on investigation of gait dynamics (stride time, stance time, etc.) requiring long gait cycles. Thus, as the main innovation of this work, we detrend the data to give meaningful information even for a relatively short-duration gait cycle. Extracted feature set was input to fundamental classification algorithms where the Support-Vector-Machine (SVM) performed best with an accuracy of 98.2% for the binary classification as healthy or suffering. This study is a substantial part of a big project where we finally aim to identify the specific VS disease that causes balance disorder and also determine the stage of the disease, if any. Within this scope, the achieved performance gives high motivation to work more deeply on the issue.

Multifractal Detrended Fluctuation Analysis on Friction Coefficient During the Friction Process

2017 ◽  
Vol 140 (1) ◽  
Author(s):  
Guodong Sun ◽  
Hua Zhu ◽  
Cong Ding ◽  
Yuankai Zhou
Keyword(s):  
Friction Coefficient ◽  
Detrended Fluctuation Analysis ◽  
Tribological Behavior ◽  
Multifractal Spectrum ◽  
Fluctuation Analysis ◽  
Friction Process ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Aisi 52100 ◽  
Aisi 1045 ◽  
Detrended Fluctuation

The dynamic evolutionary law and tribological behavior of tribopair AISI 52100-AISI 1045 were studied via the multifractal method. Friction experiment was performed on a ring-on-disk tribometer under lubrication, and the multifractal detrended fluctuation analysis (MF-DFA) method was adapted to characterize the multifractality of the friction coefficient. The multifractal spectra first exhibited a left-hook, then right-hook, and left-hook, respectively, during the friction stages. The multifractal spectrum width W decreases in running-in friction process, maintains at small values in steady friction process, and increases rapidly in increasing friction process. Corresponding shuffled series was analyzed to distinguish that the multifractality of friction coefficient is due to the long-range correlation of the fluctuations. The results inform quantitative interpretations of friction system's tribological behavior and friction process identification.

scholarly journals Multifractal Detrended Fluctuation Analysis of Temperature Reanalysis Data over Greece

Atmosphere ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 336 ◽  
Author(s):  
Kostas Philippopoulos ◽  
Nikolaos Kalamaras ◽  
Chris G. Tzanis ◽  
Despina Deligiorgi ◽  
Ioannis Koutsogiannis
Keyword(s):  
Time Series ◽  
Spatial Distribution ◽  
Detrended Fluctuation Analysis ◽  
Multifractal Spectrum ◽  
Spectral Width ◽  
Reanalysis Data ◽  
Fluctuation Analysis ◽  
Spectral Parameters ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Detrended Fluctuation

The Multifractal Detrended Fluctuation Analysis (MF-DFA) is used to examine the scaling behavior and the multifractal characteristics of the mean daily temperature time series of the ERA-Interim reanalysis data for a domain centered over Greece. The results showed that the time series from all grid points exhibit the same behavior: they have a positive long-term correlation and their multifractal structure is insensitive to local fluctuations with a large magnitude. Special emphasis was given to the spatial distribution of the main characteristics of the multifractal spectrum: the value of the Hölder exponent, the spectral width, the asymmetry, and the truncation type of the spectra. The most interesting finding is that the spatial distribution of almost all spectral parameters is decisively determined by the land–sea distribution. The results could be useful in climate research for examining the reproducibility of the nonlinear dynamics of reanalysis datasets and model outputs.

scholarly journals Extraction of Frictional Vibration Features with Multifractal Detrended Fluctuation Analysis and Friction State Recognition

Symmetry ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 272 ◽  
Author(s):  
Jing-Ming Li ◽  
Hai-Jun Wei ◽  
Li-Dui Wei ◽  
Da-Ping Zhou ◽  
Ye Qiu
Keyword(s):  
Detrended Fluctuation Analysis ◽  
Friction And Wear ◽  
Friction Pair ◽  
Multifractal Spectrum ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
State Recognition ◽  
Detrended Fluctuation ◽  
Frictional Vibrations ◽  
Frictional Vibration

For the purpose of extracting the frictional vibration characteristics of the friction pair during friction and wear in different friction states, the friction and wear tests of friction pair in different friction states were conducted on a testing machine. Higher-dimensional fractal and multifractal characteristics hidden in time series can be examined by multifractal detrended fluctuation analysis (MFDFA) method. The frictional vibration time-domain signals, the friction coefficient signals and the frictional vibration frequency-domain signals were analyzed and multifractal spectra were acquired by using the MFDFA algorithm. According to the spectra, the multifractal spectrum parameters of these signals were calculated to realize the quantitative characterization of frictional vibration characteristics in different friction states. The analysis shows that it is symmetric in the variation trends of the multifractal spectrum parameters of the frictional vibration signals and the friction coefficient data. Based on the multifractal spectrum parameters of frictional vibration, the principal component analysis (PCA) algorithm was applied to establish the friction state recognition method. The results show that the multifractal spectra and their parameters can characterize the frictional vibrations, and the friction state recognition can be realized based on the multifractal spectrum parameters of frictional vibrations.

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