Relevant bibliographies by topics / Multifractal model

Contents

  1. Journal articles

Academic literature on the topic 'Multifractal model'

Author: Grafiati
Published: 4 June 2021
Last updated: 5 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Multifractal model.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Multifractal model"

1

Cheng, Q. "Generalized binomial multiplicative cascade processes and asymmetrical multifractal distributions." Nonlinear Processes in Geophysics 21, no. 2 (2014): 477–87. http://dx.doi.org/10.5194/npg-21-477-2014.

Full text
Abstract:
Abstract. The concepts and models of multifractals have been employed in various fields in the geosciences to characterize singular fields caused by nonlinear geoprocesses. Several indices involved in multifractal models, i.e., asymmetry, multifractality, and range of singularity, are commonly used to characterize nonlinear properties of multifractal fields. An understanding of how these indices are related to the processes involved in the generation of multifractal fields is essential for multifractal modeling. In this paper, a five-parameter binomial multiplicative cascade model is proposed based on the anisotropic partition processes. Each partition divides the unit set (1-D length or 2-D area) into h equal subsets (segments or subareas) and m1 of them receive d1 (> 0) and m2 receive d2 (> 0) proportion of the mass in the previous subset, respectively, where m1+m2 ≤ h. The model is demonstrated via several examples published in the literature with asymmetrical fractal dimension spectra. This model demonstrates the various properties of asymmetrical multifractal distributions and multifractal indices with explicit functions, thus providing insight into and an understanding of the properties of asymmetrical binomial multifractal distributions.
APA, Harvard, Vancouver, ISO, and other styles
2

TCHIGUIRINSKAIA, I. "SCALE INVARIANCE AND STRATIFICATION: THE UNIFIED MULTIFRACTAL MODEL OF HYDRAULIC CONDUCTIVITY." Fractals 10, no. 03 (2002): 329–34. http://dx.doi.org/10.1142/s0218348x02001373.

Full text
Abstract:
A fairly large body of observational evidence shows that the hydraulic conductivity is an extremely heterogeneous physical phenomenon that exhibits wide variability over a broad range of horizontal and vertical scales. Stochastic multifractals that result from continuous dynamic cascades are suggested as an appropriate model to capture the scale-invariance and stratification of the Columbus site hydraulic conductivity. Then, observed interrelations between estimates of multifractal parameters, those characterize vertical and horizontal scaling regimes, are interpreted by the unified multifractal model.
APA, Harvard, Vancouver, ISO, and other styles
3

CORSO, G., and D. A. MOREIRA. "MULTIFRACTAL SURFACES: LUCENA AND STANLEY APPROACHES." Fractals 21, no. 03n04 (2013): 1350020. http://dx.doi.org/10.1142/s0218348x13500205.

Full text
Abstract:
We review multifractal surfaces focusing on a comparison between Lucena and Stanley approaches. The multifractal model as presented by Stanley is basically a multinomial measure over a standard partition of a square. The Lucena approach is geometric oriented, the multifractality is not imposed over a regular lattice, but the lattice itself follows a partition where area tiles obey a multifractal distribution. The non-trivial tilling has a distribution of neighbors of lattice elements that shows a fat tail. Despite the strong differences in the two bidimensional models, both Liacir and Stanley multifractals can be reduced to the same object in one dimension. The message of this article is that there is no unique multifractal object in two dimensions and, as a consequence, we should caution about algorithms that estimate multifractal spectrum in two dimensions because it is not clear what kind of multifractality is being measured. Finally we propose a mixed Lucena-Stanley bimultifractal, an object that combines a multifractal measure over a geometric multifractal tilling.
APA, Harvard, Vancouver, ISO, and other styles
4

Holdsworth, Amber M., Nicholas K. R. Kevlahan, and David J. D. Earn. "Multifractal signatures of infectious diseases." Journal of The Royal Society Interface 9, no. 74 (2012): 2167–80. http://dx.doi.org/10.1098/rsif.2011.0886.

Full text
Abstract:
Incidence of infection time-series data for the childhood diseases measles, chicken pox, rubella and whooping cough are described in the language of multifractals. We explore the potential of using the wavelet transform maximum modulus (WTMM) method to characterize the multiscale structure of the observed time series and of simulated data generated by the stochastic susceptible-exposed-infectious-recovered (SEIR) epidemic model. The singularity spectra of the observed time series suggest that each disease is characterized by a unique multifractal signature, which distinguishes that particular disease from the others. The wavelet scaling functions confirm that the time series of measles, rubella and whooping cough are clearly multifractal, while chicken pox has a more monofractal structure in time. The stochastic SEIR epidemic model is unable to reproduce the qualitative singularity structure of the reported incidence data: it is too smooth and does not appear to have a multifractal singularity structure. The precise reasons for the failure of the SEIR epidemic model to reproduce the correct multiscale structure of the reported incidence data remain unclear.
APA, Harvard, Vancouver, ISO, and other styles
5

ZHOU, H., E. PERFECT, Y. Z. LU, B. G. LI, and X. H. PENG. "MULTIFRACTAL ANALYSES OF GRAYSCALE AND BINARY SOIL THIN SECTION IMAGES." Fractals 19, no. 03 (2011): 299–309. http://dx.doi.org/10.1142/s0218348x11005403.

Full text
Abstract:
Multifractal analyses of binary images of soil thin sections (STS) are widely used to characterize pore structure. However, no geometrical model is known to exist for a binary multifractal. Thus, the multifractality of binary images, and the accuracy of multifractal parameters estimated from them, need to be carefully evaluated. We captured 8-bit depth resolution digital grayscale images of three STS images with dimensions of 1024 × 1024 pixels and a pixel length of 1.9 μm. Random grayscale geometrical multifractal fields (GMF) with similar dimensions and known multifractal parameters were constructed using generators extracted from the STS images. The STS and GMF grayscale images were objectively thresholded to give six binary images. The method of moments was used to compute the log-transformed partition function, log (χ(q, δ)) versus log(δ) where δ is box size, for each grayscale image and its binary counterpart. Consistent linearity was observed in the resulting functions for the grayscale images, indicating, by definition, multifractal behavior. In contrast, the log (χ(q, δ)) versus log(δ) plots for the binary images exhibited a two-region response, with a flat plateau at small scales and linearity at larger scales, indicating they were not true multifractals. Generalized dimensions (Dq) computed from the linear portions of the binary log-transformed partition functions were significantly over estimated for q ≪ 0 and underestimated for q ≫ 0 relative to corresponding Dq values for the grayscale images. Based on these results we contend that binary images are not mathematical multifractals, and that generalized dimensions estimated from them cannot be used to quantify pore space geometry. Instead we encourage further exploration of the use of grayscale images for multifractal characterization of soil structure. This direct approach is theoretically sound and does not require any intermediate thresholding step, which is known to influence the results of multifractal analyses.
APA, Harvard, Vancouver, ISO, and other styles
6

Schmitt, F. G. "Continuous multifractal models with zero values: a continuous $\beta $ -multifractal model." Journal of Statistical Mechanics: Theory and Experiment 2014, no. 2 (2014): P02008. http://dx.doi.org/10.1088/1742-5468/2014/02/p02008.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

de Montera, L., L. Barthès, C. Mallet, and P. Golé. "The Effect of Rain–No Rain Intermittency on the Estimation of the Universal Multifractals Model Parameters." Journal of Hydrometeorology 10, no. 2 (2009): 493–506. http://dx.doi.org/10.1175/2008jhm1040.1.

Full text
Abstract:
Abstract The multifractal properties of rain are investigated within the framework of universal multifractals. The database used in this study includes measurements performed over several months in different locations by means of a disdrometer, the dual-beam spectropluviometer (DBS). An assessment of the effect of the rain–no rain intermittency shows that the analysis of rain-rate time series may lead to a spurious break in the scaling and to erroneous parameters. The estimation of rain multifractal parameters is, therefore, performed on an event-by-event basis, and they are found to be significantly different from those proposed in scientific literature. In particular, the parameter H, which has often been estimated to be 0, is more likely to be 0.53, thus meaning that rain is a fractionally integrated flux (FIF). Finally, a new model is proposed that simulates high-resolution rain-rate time series based on these new parameters and on a simple threshold.
APA, Harvard, Vancouver, ISO, and other styles
8

Chigirinskaya, Y., D. Schertzer, S. Lovejoy, A. Lazarev, and A. Ordanovich. "Unified multifractal atmospheric dynamics tested in the tropics: part I, horizontal scaling and self criticality." Nonlinear Processes in Geophysics 1, no. 2/3 (1994): 105–14. http://dx.doi.org/10.5194/npg-1-105-1994.

Full text
Abstract:
Abstract. In this paper we test the Unified Mulifractal model of atmospheric dynamics in the tropics. In the first part, we empirically investigate the scaling behaviour along the horizontal, in the second part along the vertical. Here we concentrate on the presentation of basic multifractal notions and techniques and on how they give rise to self-organized critical structures. Indeed, we point out a rather simple and clear characterisation of these structures which may help to clarify both the nature of the oft-cited coherent structures and the generation of cyclones. Using 30 aircraft series of horizontal wind and temperature, we find rather remarkable constancy of the three universal multifractal indices H, C1 and α as well as the value of critical exponents qD, γD associated with multifractal phase transitions and self-organized critical structures. This constancy extends not only from wind tunnel and mid-latitude to the tropics, but also to multifractals generated by Navier-Stokes like equations.
APA, Harvard, Vancouver, ISO, and other styles
9

Mellen, R. H., and I. A. Leykin. "Nonlinear dynamics of wind waves: multifractal phase/time effects." Nonlinear Processes in Geophysics 1, no. 1 (1994): 51–56. http://dx.doi.org/10.5194/npg-1-51-1994.

Full text
Abstract:
Abstract. In addition to the bispectral coherence method, phase/time analysis of analytic signals is another promising avenue for the investigation of phase effects in wind waves. Frequency spectra of phase fluctuations obtained from both sea and laboratory experiments follow an F-β power law over several decades, suggesting that a fractal description is appropriate. However, many similar natural phenomena have been shown to be multifractal. Universal multifractals are quantified by two additional parameters: the Lévy index 0 < α < 2 for the type of multifractal and the co-dimension 0 < C1 < 1 for intermittence. The three parameters are a full statistical measure the nonlinear dynamics. Analysis of laboratory flume data is reported here and the results indicate that the phase fluctuations are 'hard multifractal' (α > 1). The actual estimate is close to the limiting value α = 2, which is consistent with Kolmogorov's lognormal model for turbulent fluctuations. Implications for radar and sonar backscattering from the sea surface are briefly considered.
APA, Harvard, Vancouver, ISO, and other styles
10

Bershadskii, A. "Multifractality–Monofractality Phase Transition in the Anderson Model." Modern Physics Letters B 12, no. 22 (1998): 921–27. http://dx.doi.org/10.1142/s0217984998001062.

Full text
Abstract:
It is shown that statistics of multifractality–monofractality phase transition is described by a generalization of the Bernoulli distribution (multifractal Bernoulli distribution). It is also shown that this distribution is observed in numerical simulations of multifractal wave functions which use the Anderson model, both for short- and long-range disorder. In the last case (corresponding to the dipole interactions) the multifractal specific heat of the most eigenstates — c ≃ d/3, where d is dimension of the space.
APA, Harvard, Vancouver, ISO, and other styles
More sources
You might also be interested in the extended bibliographies on the topic 'Multifractal model' for particular source types:
Journal articles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography