— When d = 2 the multifractional Hermite process corresponds to the multifractional Rosenblatt process previously introduced in the paper [56] Nevertheless Wiener Itô integrals of order 2 enjoy specific properties see the end of Sections 2 Preliminaries strategy and main results 8 Complements for the multifractional Rosenblatt
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WhatsApp— For at least a decade there has been considerable interest in applied and theoretical issues related to multifractional random models Nonetheless only a few results are known in the framework of heavy tailed stable distributions In this framework a paradigmatic example is the linear multifractional stable motion LMSM {Y t t∈R} Stoev
WhatsApp— Multifractional Brownian Motion mBm is considered to be one of the stochastic multifractal models employed to analyse and extract dissimilar patterns images and signals Fractal Brownian Motion fBm provides attention grabbing models with various related methods for a broad range of phenomena occurring in the natural world
WhatsApp— of the mean squared displacement MSD is observed [3 4 5 6 7 8] K α is the anomalous diffusion coefficient Depending on the magnitude of the anomalous diffusion exponent α we distinguish subdiffusion for 0 < α < 1 from superdiffusion with α > 1 [5 8] Subdiffusion is typically observed for submicron particles in both bacterial and
WhatsAppThe Multifractional Brownian Motion MBM is a generalization of the well known Fractional Brownian Motion One of the main reasons that makes the MBM interesting for modelization is that one can prescribe its regularity given any Holder function H t with values in ]0 1[ one can construct an MBM admitting at any t 0 a Holder exponent equal to H t 0
WhatsApp— We study a family of locally self similar stochastic processes Y = {Y t } t ∈ℝ with α stable distributions called linear multifractional stable motions They have infinite variance and may possess skewed distributions The linear multifractional stable motion processes include in particular the classical linear fractional stable motion processes
WhatsAppThis work defines two classes of processes that we term tempered fractional multistable motion and tempered multifractional stable motion They are extensions of fractional multistable motion and multifractional stable motion respectively obtained by adding an exponential tempering to the integrands We investigate certain basic features of these
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WhatsApp— This paper proposes an alternative generalization to MBM based on the FBM defined by the Riemann Liouville type of fractional integral which is found to be similar to that of the standard MBM Fractional Brownian motion FBM is widely used in the modeling of phenomena with power spectral density of power law type However FBM
WhatsApp— The LRD features reflect the slowness of the degradation process the multifractional features allow capturing local irregularities due to degenerate data fluctuations and can specifically describe degenerate sequences Finally a RUL prediction framework for CGC is proposed and then verified with real observation data collected
WhatsAppMultifractional and long range dependent characteristics for remaining useful life prediction of cracking gas compressor article{Song2022MultifractionalAL title={Multifractional and long range dependent characteristics for remaining useful life prediction of cracking gas compressor} author={Wanqing Song and Shouwu Duan and Enrico Zio and
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WhatsApp— The planar one dimensional motion of a multifractional bubble liquid in an acoustic field is consi dered The dispersed phase consists of N M fractions of bubbles of different gases and various bubble fractions contain steam and participate in phase transformations and M bubble fractions are composed of insoluble gas The dispersion
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WhatsApp— Semantic Scholar extracted view of "Multifractional Stochastic Fields" by A Ayache Skip to search form Skip to main content Skip to account menu Semantic Scholar s Logo Search 220 989 021 papers from all fields of science Search Sign In Create Free Account DOI /8917;
WhatsApp— Multifractional programming convex sets interior point methods self concordant barrier functions short step algorithms poly nomial time convergence predictor corrector step 1 Introduction Karmarkar s 1984 landmark paper Ref 1 about a practical poly nomial time interior point algorithm for linear programs triggered extensive
WhatsApp— by itself the predictability of returns the controversial failure of the random walk model does not prove market inefficiency since it suffices the expected conditional return to comply with in order to save both efficiency and non random walk the exploitation of the above relation to argue about the behavior of
WhatsApp— tion 2 We also discuss several generalizations of multifractional Brownian motion In Section 3 we introduce certain approximations of multifractional Brownian motion by absolutely continuous processes and prove that they converge In Section 4 we con sider stochastic differential equations with multifractional Brownian motion For such
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