Distributed Synchronization of Coupled Time-Delay Neural Networks Based on Randomly Occurring Control

In this paper, the distributed synchronization of stochastic coupled neural networks with time-varying delay is concerned via randomly occurring control. We use two Bernoulli stochastic variables to describe the occurrence of distributed adaptive control and updating law according to certain probabilities. The distributed adaptive control and updating law for each vertex in the network depend on the state information on each vertex’s neighborhood. Based on Lyapunov stability theory, Itô differential equations, etc., by constructing the appropriate Lyapunov functional, we study and obtain sufficient conditions for the distributed synchronization of such networks in mean square.

Distributed Synchronization of Stochastic Complex Networks with Time-Varying Delays via Randomly Occurring Control

This paper studies the distributed synchronization control problem of a class of stochastic dynamical systems with time-varying delays and random noise via randomly occurring control. The activation of the distributed adaptive controller and the update of the control gain designed in this paper all happen randomly. Based on the Lyapunov stability theory, LaSalle invariance principle, combined with the use of the properties of the matrix Kronecker product, stochastic differential equation theory and other related tools, by constructing the appropriate Lyapunov functional, the criterion for the distributed synchronization of this type of stochastic complex networks in mean square is obtained.

Improving sound diffusion in a reverberation tank using a randomly fluctuating surface

Underwater reverberation environments that satisfy the conditions of uniformity and isotropy of the diffuse field can be used to measure the acoustic characteristics of underwater targets.This study combines two practical indicators—the standard deviation of the absolute sound pressure field(to indicate uniformity)and the analysis of the wavenumber spectrum in the spherical harmonics domain(to indicate isotropy)—for an accurate evaluation of the diffusion of the sound field in a reverberation tank.A method is proposed that can improve the narrow-band diffusion of the sound field by employing a randomly fluctuating surface.An acoustic experiment was performed in a reverberation water tank(1.2 m×1 m×0.8 m),where a randomly fluctuating surface was generated by making waves.The experimental results show that as the wave motion contributes effectively to the random reflection of sound rays in all directions,the uniformity and isotropy are improved significantly when the surface is fluctuating randomly.This work helps to ensure accurate measurements of the characteristics of underwater targets in reverberation tanks.

INELASTIC MULTIPLE SCATTERING BY ACTIVE RANDOMLY DISTRIBUTED SPHERICAL SCATTERERS

According to the model of equivalent dipoles for active molecules, the analysisof inelastic EM scattering by active molecules embedded in a sphere is given by the method ofdyadic Green’s functions at the first, and then based on the theory of elastic multiple scattering byrandomly distributed spherical scatterers, a new theory for analysis of inelastic multiple scatteringby active molecules embedded in randomly distributed spherical scatterers also is developed. Thistheory gives the expansions of the multiple scattering fields in all space region in terms of vectorspherical wave functions in which the expansion coefficients can be solved from a set of coupledlinear equations.

THE INFLUENCE OF SELECTING RECEIVING SLITS AND USING RANDOMLY ORIENTED STANDARD SAMPLES ON QUANTITATIVE TEXTURE ANALYSIS

The influence of the selection of receiving slits and the use of standard samples with random orientation on the result of quantitative texture analysis was tested and discussed. The result proves that it will improve the precision of the analysis to use proper slits and a randomly oriented standard sample. A simple method was given to interpolate the correction curves of random intensities.

ELASTIC MEDIA WITH RANDOMLY DISTRIBUTED DEFFCTS

In this paper, the clastic field in a solid with randomly distributed defects is derived. These defects are composed of cavities andmicrocracks, whose locations, orientation and size are random variables. The Random Point field Model is proposed to describe the random defects, and the basic equations far elastic field in a random defect medium are dcveloped Twoexaniples are studied in detail. One is a solid with random microcracks and the other is a solid with ellipsoidal cavities.

Asymptotics for the joint tail probability of bidimensional randomly weighted sums with applications to insurance

This paper studies the joint tail behavior of two randomly weighted sums∑_(i=1)^(m)Θ_(i)X_(i)and∑_(j=1)^(n)θ_(j)Y_(j)for some m,n∈N∪{∞},in which the primary random variables{X_(i);i∈N}and{Y_(i);i∈N},respectively,are real-valued,dependent and heavy-tailed,while the random weights{Θi,θi;i∈N}are nonnegative and arbitrarily dependent,but the three sequences{X_(i);i∈N},{Y_(i);i∈N}and{Θ_(i),θ_(i);i∈N}are mutually independent.Under two types of weak dependence assumptions on the heavy-tailed primary random variables and some mild moment conditions on the random weights,we establish some(uniformly)asymptotic formulas for the joint tail probability of the two randomly weighted sums,expressing the insensitivity with respect to the underlying weak dependence structures.As applications,we consider both discrete-time and continuous-time insurance risk models,and obtain some asymptotic results for ruin probabilities.

Estimating the grain size of microgranular material using laser-induced breakdown spectroscopy combined with machine learning algorithms

Recent work has validated a new method for estimating the grain size of microgranular materials in the range of tens to hundreds of micrometers using laser-induced breakdown spectroscopy(LIBS).In this situation,a piecewise univariate model must be constructed to estimate grain size due to the complex dependence of the plasma formation environment on grain size.In the present work,we tentatively construct a unified calibration model suitable for LIBS-based estimation of those grain sizes.Specifically,two unified multivariate calibration models are constructed based on back-propagation neural network(BPNN)algorithms using feature selection strategies with and without considering prior information.By detailed analysis of the performances of the two multivariate models,it was found that a unified calibration model can be successfully constructed based on BPNN algorithms for estimating the grain size in the range of tens to hundreds of micrometers.It was also found that the model constructed with a priorguided feature selection strategy had better prediction performance.This study has practical significance in developing the technology for material analysis using LIBS,especially when the LIBS signal exhibits a complex dependence on the material parameter to be estimated.

Fully sprayed MXene-based high-performance flexible piezoresistive sensor for image recognition

High-performance flexible pressure sensors provide comprehensive tactile perception and are applied in human activity monitoring,soft robotics,medical treatment,and human-computer interface.However,these flexible pressure sensors require extensive nano-architectural design and complicated manufacturing and are timeconsuming.Herein,a highly sensitive,flexible piezoresistive tactile sensor is designed and fabricated,consisting of three main parts:the randomly distributed microstructure on T-ZnOw/PDMS film as a top substrate,multilayer Ti_(3)C_(2)-MXene film as an intermediate conductive filler,and the few-layer Ti_(3)C_(2)-MXene nanosheetbased interdigital electrodes as the bottom substrate.The MXene-based piezoresistive sensor with randomly distributed microstructure exhibits a high sensitivity over a broad pressure range(less than 10 kPa for 175 kPa^(-1))and possesses an out-standing permanence of up to 5000 cycles.Moreover,a 16-pixel sensor array is designed,and its potential applications in visualizing pressure distribution and an example of tactile feedback are demonstrated.This fully sprayed MXene-based pressure sensor,with high sensitivity and excellent durability,can be widely used in,electronic skin,intelligent robots,and many other emerging technologies.

Uniform estimate for maximum of randomly weighted sums with applications to insurance risk theory

This paper obtains the uniform estimate for maximum of sums of independent and heavy-tailed random variables with nonnegative random weights, which can be arbitrarily dependent of each other. Then the applications to ruin probabilities in a discrete time risk model with dependent stochastic returns are considered.

Approximation by randomly weighting method in censored regression model

Censored regression ("Tobit") models have been in common use, and their linear hypothesis testings have been widely studied. However, the critical values of these tests are usually related to quantities of an unknown error distribution and estimators of nuisance parameters. In this paper, we propose a randomly weighting test statistic and take its conditional distribution as an approximation to null distribution of the test statistic. It is shown that, under both the null and local alternative hypotheses, conditionally asymptotic distribution of the randomly weighting test statistic is the same as the null distribution of the test statistic. Therefore, the critical values of the test statistic can be obtained by randomly weighting method without estimating the nuisance parameters. At the same time, we also achieve the weak consistency and asymptotic normality of the randomly weighting least absolute deviation estimate in censored regression model. Simulation studies illustrate that the per-formance of our proposed resampling test method is better than that of central chi-square distribution under the null hypothesis.

Bi-Spectrum Scattering Model for Conducting Randomly Rough Surface

A scattering model is developed to predict the scattering coefficient of a conducting randomly rough surface by analyzing the randomly rough surface in the spectral domain using the bi-spectrum method. For common randomly rough surfaces without obvious two-scale characteristics, a scale-compression filter can divide the auto-correlation spectrum into two parts with different correlation lengths. The Kirchhoff approximation and the small perturbation method are used to obtain the surface field, then a bistatic scattering model, the bi-spectrum model (BSM), is used to derive an explicit expression from the surface field. Examples using the integral equation model (IEM), finite difference of the time domain (FDTD) method, and BSM show that the BSM accuracy is acceptable and its range of validity is similar to IEM. BSM can also be extended to a scattering model for dielectric randomly rough surfaces.

Bi-Spectrum Scattering Model for Dielectric Randomly Rough Surface

The bistatic scattering model is offen used for remote microwave sensing. The bi-spectrum model (BSM) for conducting surfaces was used to develop a scattering model for dielectric randomly rough surfaces to estimate their bistatic scattering coefficients. The model for dielectric rough surfaces differs from the BSM for a conducting surface by including Fresnell reflection and transmission from dielectric rough surfaces. The bistatic scattering coefficients were defined to satisfy the reciprocal theorem. Values calculated using the BSM for dielectric randomly rough surfaces compare well with those of the integral equation model (IEM) and with experimental data, showing that the BSM accuracy is acceptable and its range of validity is similar to that of IEM while the BSM expression is simpler than that of IEM.

A large sample study of randomly weighted bootstrap in linear models

The method of randomly weighted bootstrap is used to derive the approximation of M-estimates in linear models. It is shown that the approximation is asymptotically valid under some mild conditions.

Genetic diversity analysis of Penicillium marneffei isolated from AIDS patients in Guangdong, China using randomly amplified polymorphic DNA

Background Penicillium mameffei （P. marneffe~） is an emerging pathogenic fungus that can cause invasive mycosis in patients with AIDS. The epidemiological features of P. marneffeiinfection in AIDS patients in Guangdong province remain unclear so far. This study aimed to investigate the genetic diversity within a population of 163 P. mameffei isolates obtained from AIDS patients and search for the dominant clinical strains in Guanqdong province.

LAD estimation for nonlinear regression models with randomly censored data

The least absolute deviations (LAD) estimation for nonlinear regression models with randomly censored data is studied and the asymptotic properties of LAD estimators such as consistency, boundedness in probability and asymptotic normality are established. Simulation results show that for the problems with censored data, LAD estimation performs much more robustly than the least squares estimation.

Identifying Lysine Succinylation Sites in Proteins by Broad Learning System and Optimizing Imbalanced Training Dataset via Randomly Labeling Samples

As one important type of post-translational modifications(PTMs),protein lysine succinylation regulates many important biological processes.It is also closely involved with some major diseases in the aspects of Cardiometabolic,liver metabolic,nervous system and so on.Therefore,it is imperative to predict the succinylation sites in proteins for both basic research and drug development.In this paper,a novel predictor called i Succ Lys-BLS was proposed by not only introducing a new machine learning algorithm—Broad Learning System,but also optimizing the imbalanced data by randomly labeling samples.Rigorous cross-validation and independent test indicate that the success rate of i Succ Lys-BLS for positive samples is overwhelmingly higher than its counterparts.

Scintillations of optical vortex in randomly inhomogeneous medium

The comparative numerical and analytical analysis of scintillation indices of the vortex Laguerre–Gaussian beam and the nonvortex doughnut hole and Gaussian beams propagating in the randomly inhomogeneous atmosphere has been performed. It has been found that the dependence of the scintillation index at the axis of the optical vortex on the turbulence intensity at the path has the form of a unit step. It has been shown that the behavior of scintillations in the cross sections of vortex and nonvortex beams differs widely. Despite the scintillation index of vortex beams has been calculated only for the simplest LG10 mode, the obtained results are quite general, because they demonstrate the main properties inherent in scintillations of vortex beams of any type.

Uniform Estimate for The Tail Probabilities of Randomly Weighted Sums

Several authors have studied the uniform estimate for the tail probabilities of randomly weighted sumsa.ud their maxima. In this paper, we generalize their work to the situation thatis a sequence of upper tail asymptotically independent random variables with common distribution from the is a sequence of nonnegative random variables, independent of and satisfying some regular conditions. Moreover. no additional assumption is required on the dependence structureof {θi,i≥ 1）.

(g,f)-Factorizations Randomly Orthogonal to a Subgraph in Graphs

Let G be a graph with vertex set V(G) and edge set E(G) and let g and f betwo integer-valued functions defined on V(G) such that 2k - 2 ≤ g(x) ≤ f(x) for all x ∈ V(G).Let H be a subgraph of G with mk edges. In this paper, it is proved that every (mg + m - 1, mf - m +l)-graph G has (g, f)-factorizations randomly k-orthogonal to H under some special conditions.