Convolution Formula Research Articles

An augmented integral method for probability distribution evaluation of performance functions

The paper proposes an efficient augmented integral method for probability distribution evaluation of performance functions. In the proposed method, the performance function is augmented by adding an auxiliary random variable, whose probability density function (PDF) and cumulative distribution function (CDF) are formulated as the integrations of the original performance function with respect to basic random variables. The optimal auxiliary random variable is determined to provide an accurate estimation of the integrations by investigating the geometric properties of integrands and a distribution parameter optimization approach based on moment analysis. According to the convolution formula, the relationship between the PDFs of the augmented performance function and the original performance function is clarified. Then, the PDF of the original performance function is calculated by solving an unconstrained optimization problem that is established using the convolution formula. Finally, three numerical examples are investigated to demonstrate the efficiency and accuracy of the proposed method for structural reliability analysis. The results indicate that the proposed method can evaluate the probability distribution of performance functions accurately and efficiently, even when the performance functions are strongly nonlinear and implicit.

Treatment of 3D diffusion problems with discontinuous coefficients and Dirac curvilinear sources

Three-dimensional diffusion problems with discontinuous coefficients and unidimensional Dirac sources arise in a number of fields. The statement we pursue is a singular-regular expansion where the singularity, capturing the stiff behavior of the potential, is expressed by a convolution formula using the Green kernel of the Laplace operator. The correction term, aimed at restoring the boundary conditions, fulfills a variational Poisson equation set in the Sobolev space H1, which can be approximated using finite element methods. The mathematical justification of the proposed expansion is the main focus, particularly when the variable diffusion coefficients are continuous, or have jumps. A computational study concludes the paper with some numerical examples. The potential is approximated by a combined method: (singularity, by integral formulas, correction, by linear finite elements). The convergence is discussed to highlight the practical benefits brought by different expansions, for continuous and discontinuous coefficients.

The Operational Laws of Symmetric Triangular Z-Numbers

To model fuzzy numbers with the confidence degree and better account for information uncertainty, Zadeh came up with the notion of Z-numbers, which can effectively combine the objective information of things with subjective human interpretation of perceptive information, thereby improving the human comprehension of natural language. Although many numbers are in fact Z-numbers, their higher computational complexity often prevents their recognition as such. In order to reduce computational complexity, this paper reviews the development and research direction of Z-numbers and deduces the operational rules for symmetric triangular Z-numbers. We first transform them into classical fuzzy numbers. Using linear programming, the extension principle of Zadeh, the convolution formula, and fuzzy number algorithms, we determine the operational rules for the basic operations of symmetric triangular Z-numbers, which are number-multiplication, addition, subtraction, multiplication, power, and division. Our operational rules reduce the complexity of calculation, improve computational efficiency, and effectively reduce the information difference while being applicable to other complex operations. This paper innovatively combines Z-numbers with classical fuzzy numbers in Z-number operations, and as such represents a continuation and innovation of the research on the operational laws of Z-numbers.

Convolution formulas for multivariate arithmetic Tutte polynomials

The multivariate arithmetic Tutte polynomial of arithmetic matroids is a generalization of the multivariate Tutte polynomial of matroids. In this note, we give the convolution formulas for the multivariate arithmetic Tutte polynomial of the product of two arithmetic matroids. In particular, the convolution formulas for the multivariate arithmetic Tutte polynomial of an arithmetic matroid are obtained. Applying our results, several known convolution formulas including [5, Theorem 10.9 and Corollary 10.10] and [1, Theorems 1 and 4] are proved by a purely combinatorial proof. The proofs presented here are significantly shorter than the previous ones. In addition, we obtain a convolution formula for the characteristic polynomial of an arithmetic matroid.

Inverse and Moore–Penrose inverse of conditional matrices via convolution

AbstractMoore–Penrose inverse emerges in statistics, neural networks, machine learning, applied physics, numerical analysis, tensor computations, solving systems of linear equations and in many other disciplines. Especially after the 2000s, the topic of Moore–Penrose inverse has started to attract great attention by researchers and has become a popular subject. In this paper, we investigate the Moore–Penrose inverse of the conditional matrices via convolution product formula. In order to use convolution formula effectively, we derive some useful identities by using some properties of the generalized conditional sequence. Moreover, we express the Moore–Penrose inverse of the conditional matrices in the form of block matrices. Finally, we not only present more general results compared to earlier works, but also provide many novel results using analytical techniques.

Abel's Convolution Formulae through Taylor Polynomials

By making use of the Taylor polynomials, new proofs are presented
 for three binomial identities including Abel’s convolution formula.

Source function from two-particle correlation through deblurring

In heavy-ion collisions, low relative-velocity two-particle correlations have been a tool for assessing space-time characteristics of particle emission. Those characteristics may be cast in the form of a relative emission source related to the correlation function through the Koonin-Pratt (KP) convolution formula that involves the relative wave-function for the particles in its kernel. In the literature, the source has been most commonly sought by parametrizing it in a Gaussian form and fitting to the correlation function. At times the source was more broadly imaged from the function, still employing a fitting. Here, we propose the use of the Richardson-Lucy (RL) optical deblurring algorithm for deducing the source from a correlation function. The RL algorithm originally follows from probabilistic Bayesian considerations and relies on the intensity distributions for the optical object and its image, as well as the convolution kernel, being positive definite, which is the case for the corresponding quantities of interest within the KP formula.

Realisations of Racah algebras using Jacobi operators and convolution identities

Using the representation theory of sl2 and an appropriate model for tensor product of lowest weight Verma modules, we give a realisation first of the Hahn algebra, and then of the Racah algebra, using Jacobi differential operators. While doing so we recover some known convolution formulas for Jacobi polynomials involving Hahn and Racah polynomials. Similarly, we produce realisations of the higher rank Racah algebras in which maximal commutative subalgebras are realised in terms of Jacobi differential operators.

Beyond islands: a free probabilistic approach

We give a free probabilistic proposal to compute the fine-grained radiation entropy for an arbitrary bulk radiation state, in the context of the Penington-Shenker-Stanford-Yang (PSSY) model where the gravitational path integral can be implemented with full control. We observe that the replica trick gravitational path integral is combinatorially matching the free multiplicative convolution between the spectra of the gravitational sector and the matter sector respectively. The convolution formula computes the radiation entropy accurately even in cases when the island formula fails to apply. It also helps to justify this gravitational replica trick as a soluble Hausdorff moment problem. We then work out how the free convolution formula can be evaluated using free harmonic analysis, which also gives a new free probabilistic treatment of resolving the separable sample covariance matrix spectrum.The free convolution formula suggests that the quantum information encoded in competing quantum extremal surfaces can be modelled as free random variables in a finite von Neumann algebra. Using the close tie between free probability and random matrix theory, we show that the PSSY model can be described as a random matrix model that is essentially a generalization of Page’s model. It is then manifest that the island formula is only applicable when the convolution factorizes in regimes characterized by the one-shot entropies. We further show that the convolution formula can be reorganized to a generalized entropy formula in terms of the relative entropy.

Compressive nonstationary near-field acoustic holography for reconstructing the instantaneous sound field

A compressive nonstationary near-field acoustic holography based on time-domain plane wave superposition method is proposed to precisely reconstruct the instantaneous sound field using the fewer measurement points. In the proposed method, by means of an instantaneous propagation kernel, a time-domain convolution superposition formula is established for relating the time-variant pressure of the hologram plane to the pressure time-wavenumber spectra of the virtual source plane. Next, compressed sensing theory and sparse representation framework are introduced into the time-marching solving inverse process of the established formula, and the smoothed l0 norm and revised Newton optimization algorithm are employed to solve the serious cumulative ill-posed problem over time for acquiring the pressure time-wavenumber spectra. The key is to approximately replace the discontinuous ℓ0 norm by using a suitable continuous function, and the revised Newton algorithm is applied to minimize the continuous function for obtaining the optimal solution. Finally, with the aid of a time-domain convolution formula, the solved pressure time-wavenumber spectra are employed to calculate the time-variant pressure on the reconstruction plane. The proposed method not only can evidently suppress the ill-posed problem of time-marching solving inverse process for improving the reconstruction accuracy, but also can greatly reduce the measurement points and microphone number for drastically decreasing the measurement cost on the premise of ensuring the reconstruction accuracy and spatial resolution. A numerical simulation is conducted, and the simulation effects prove that the proposed method can accurately reconstruct the instantaneous sound field with the fewer measurement points in time–space domains. The reconstruction results are also compared to those of time-domain plane wave superposition method with Tikhonov regularization to verify the superiority of the proposed method with fewer measurement points under different parameter configurations. An experiment of an impaction steel plate is implemented for further validating the competence of the proposed method.

Modifications of hyperplane arrangements

This paper is concerned with five kinds of modification of hyperplane arrangements, including elementary lift, parallel translation, coning, one-element extension and restriction to a hyperplane. We show that the combinatorial classification of all hyperplane arrangements of each kind of modification will be characterized by the intersection lattice of the discriminantal or adjoint arrangement. Based on the classifications, a number of combinatorial invariants, including the unsigned coefficients of the Whitney polynomial, Whitney numbers of both kinds, face numbers and region numbers, are constants on those strata associated to the intersection lattice of the discriminantal or adjoint arrangement. Moreover, we further establish the order-preserving relations of those combinatorial invariants and a series of convolution formulae on the characteristic polynomials.

On the generalized Fourier transform

In this paper, we introduce the theory of a generalized Fourier transform in order to solve differential equations with a generalized fractional derivative, and we state its main properties. In particular, we obtain the new corresponding convolution, inverse and Plancherel formulas, and Hausdorff–Young type inequality. We show that this generalized Fourier transform is useful in the study of several fractional differential equations (both ordinary and partial differential equations).

Hadamard product of series with special numbers

We evaluate in closed form a large class of power series where the coefficients are products of special numbers, or products of special numbers and Laguerre polynomials. A number of examples is presented involving harmonic, hyperharmonic, Fibonacci, Lucas, Bernoulli, Euler, and derangement numbers. We also obtain a new convolution formula for the Laguerre polynomials.

ON CLASSICAL FUNDAMENTAL SOLUTION OF THE CAUCHY PROBLEM FOR ONE CLASS OF ULTRA-PARABOLIC EQUATIONS OF KOLMOGOROV TYPE

The investigation is devoted to ultra-parabolic equations with two group of spatial variables which appear in Asian options problems. Unlike the European option, the payout of Asian derivative depends on the entire trajectory of the price value, not the final value only. Among methods of researching of the Asian options, the one is to include dependent on the price trajectory variables in the state space. The expansion of the state space by including of dependent on the price trajectory variables transforms the path-dependent problem for the Asian option into an equivalent path-independent Markov problem. However, the increasing of the dimension usually leads to partial differential equations which are not uniformly parabolic. The class of these equations under some conditions is a generalization of the well-known degenerate parabolic A.N.Kolmogorov's equation of diffusion with inertia. Mathematical models of the options have been studied in many works. Among the main problems in the study of the Asian options models when they are reduced to ultra-parabolic equations of the Kolmogorov type there are the following: the construction, researching of the existence, uniqueness and properties (for instance, such as non-negativity, normality, convolution formula) of the fundamental solution of the Cauchy problem as the probability density of the transition between the states of the stochastic process, which given by the corresponding stochastic differential equation. It has been constructed so called $L$-type fundamental solutions for equations from the class previously, and some their properties have been established. In the work, it is formulated some known results about $L$-type fundamental solutions. In current research, for the equations from this class we build and study the classical fundamental solutions of the Cauchy problem. For the coefficients of the equations we apply special H\"older conditions with respect to spatial variables. We prove the existing of the classic fundamental solutions and its properties such as estimates, including estimates of the derivatives, normality, convolution formula, positivity etc. The results obtained in the work can be used to receive the well-posedness of the Cauchy problem for such equations in the classical sense.

A NEW ∝-LAPLACE TRANSFORM ON TIME SCALES

In this paper we introduce a new ∝-Laplace transform which is a generalization of nabla version of Laplace transform on time scales. In particular for 0< ∝< 1 this transform will serve as fractional Laplace transform on time scales. Existence theorem and some important properties such as linearity, initial and nal value theorem, transform of integral, shifting theorem, transform of derivative are proved. Additionally convolution theorem and formulae for fractional integral, Riemann-Liouville fractional derivative, Liouville-Caputo fractional derivative, Mittag Leer function are given. At last for a suitable value of a fractional dynamic equation with given initial condition is solved.

Stabilization of networked systems with communication constraints: The random sampling periods case

In this paper, the stabilization problem of networked systems with communication constraints is studied, where the random sampling periods, deception attacks and packet losses are considered simultaneously. Different from the existing literature, the actual sampling periods are subject to undesirable physical constraints and fluctuate around an ideal sampling period with certain probability distribution. First, a closed-loop discrete-time stochastic system is constructed by considering the random sampling periods, deception attacks and packet losses in a unified framework. Second, an equivalent yet tractable stochastic model is established with the aid of matrix exponential computation. By reduced-order confluent Vandermonde matrix approach, law of total expectation, convolution formula and Kronecker product operation, a controller is designed such that the exponential stability in the mean-square sense of closed-loop stochastic system is guaranteed. Subsequently, a special model is discussed where no deception attack occurs and then the corresponding controller is designed. Finally, an illustrative example is given to show the effectiveness of the designed approach.

The Dumont Ansatz for the Eulerian Polynomials, Peak Polynomials and Derivative Polynomials

We observe that three context-free grammars of Dumont can be brought to a common ground, via the idea of transformations of grammars, proposed by Ma–Ma–Yeh. Then we develop a unified perspective to investigate several combinatorial objects in connection with the bivariate Eulerian polynomials. We call this approach the Dumont ansatz. As applications, we provide grammatical treatments, in the spirit of the symbolic method, of relations on the Springer numbers, the Euler numbers, the three kinds of peak polynomials, an identity of Petersen, and the two kinds of derivative polynomials, introduced by Knuth–Buckholtz and Carlitz–Scoville, and later by Hoffman in a broader context. We obtain a convolution formula on the left peak polynomials, leading to the Gessel formula. In this framework, we come to the combinatorial interpretations of the derivative polynomials due to Josuat-Vergès.

Convolution theorems associated with quaternion linear canonical transform and applications

Novel types of convolution operators for quaternion linear canonical transform (QLCT) are proposed. Type one and two are defined in the spatial and QLCT spectral domains, respectively. They are distinct in the quaternion space and are consistent once in complex or real space. Various types of convolution formulas are discussed. Consequently, the QLCT of the convolution of two quaternionic functions can be implemented by the product of their QLCTs, or the summation of the products of their QLCTs. As applications, correlation operators and theorems of the QLCT are derived. The proposed convolution formulas are used to solve Fredholm integral equations with special kernels. Some systems of second-order partial differential equations, which can be transformed into the second-order quaternion partial differential equations, can be solved by the convolution formulas as well. As a final point, we demonstrate that the convolution theorem facilitates the design of multiplicative filters.

Target-enclosing inversion using an interferometric objective function

SUMMARY Full waveform inversion is a high-resolution subsurface imaging technique, in which full seismic waveforms are used to infer subsurface physical properties. We present a novel, target-enclosing, full-waveform inversion framework based on an interferometric objective function. This objective function exploits the equivalence between the convolution and correlation representation formulas, using data from a closed boundary around the target area of interest. Because such equivalence is violated when the knowledge of the enclosed medium is incorrect, we propose to minimize the mismatch between the wavefields independently reconstructed by the two representation formulas. The proposed method requires only kinematic knowledge of the subsurface model, specifically the overburden for redatuming and does not require prior knowledge of the model below the target area. In this sense it is truly local: sensitive only to the medium parameters within the chosen target, with no assumptions about the medium or scattering regime outside the target. We present the theoretical framework and derive the gradient of the new objective function via the adjoint-state method and apply it to a synthetic example with exactly redatumed wavefields. A comparison with FWI of surface data and target-oriented FWI based on the convolution representation theorem only shows the superiority of our method both in terms of the quality of target recovery and reduction in computational cost.

Stabilization of Sampled-Data Systems With Noisy Sampling Intervals and Packet Dropouts via a Discrete-Time Approach

In real-world applications, sampled-data systems are often subject to undesirable physical constraints, which results in noisy sampling intervals that fluctuate around an ideal sampling period based on certain probability distribution. In the presence of noisy sampling intervals, this article is concerned with the stabilization problem for a class of sampled-data systems with successive packet dropouts. First, the relationship between two adjacent update times of zero-order hold is established. Then, by discrete-time approach, an equivalent discrete-time stochastic system is introduced. By the law of total expectation, together with confluent Vandermonde matrix, convolution formula, and Kronecker product operation, the mathematical expectation of a product of three matrices, including the system matrix and its transpose, is calculated. Based on this, a stabilization controller is designed such that the closed-loop system is stochastically stable. Finally, an illustrative example is provided to verify the effectiveness of the proposed design approach.