Numerical analysis of corrugated tube flow using RBFNs

This paper reports the application of neural networks for the numerical analysis of steady-state axisymmetric flow through an indefinitely long corrugated tube. Meshless global radial basis function networks (RBFNs) are employed to represent all dependent variables in the governing differential equations. For a better quality of approximation, the networks used here are constructed based on the integration process rather than the usual differentiation process. Multiple spaces of network weights for each variable are converted into the single space of nodal variable values, resulting in the square system of equations with usual size. The governing equations are discretized in the strong form by point collocation and the resultant nonlinear system is solved with trust-region methods. The corrugated tube flow of a Newtonian fluid, power-law fluid and Oldroyd-B fluid are considered. With relatively low numbers of data points, flow resistance predictions obtained are in good agreement with the benchmark solutions

An efficient BEM for numerical solution of the biharmonic boundary value problem

This paper presents an efficient BEM for solving biharmonic equations. All boundary values including geometries are approximated by the universal high order radial basis function networks (RBFNs) rather than the usual low order interpolations. Numerical results show that the proposed BEM is considerably superior to the linear/quadratic-BEM in terms of both accuracy and convergence rate

Solving high-order partial differential equations with indirect radial basis function networks

This paper reports a new numerical method based on radial basis function networks (RBFNs) for solving high-order partial differential equations (PDEs). The variables and their derivatives in the governing equations are represented by integrated RBFNs. The use of integration in constructing neural networks allows the straightforward implementation of multiple boundary conditions and the accurate approximation of high-order derivatives. The proposed RBFN method is verified successfully through the solution of thin-plate bending and viscous flow problems which are governed by biharmonic equations. For thermally driven cavity flows, the solutions are obtained up to a high Rayleigh number

A Noise-Robust Method with Smoothed \ell_1/\ell_2 Regularization for Sparse Moving-Source Mapping

The method described here performs blind deconvolution of the beamforming output in the frequency domain. To provide accurate blind deconvolution, sparsity priors are introduced with a smooth \ell_1/\ell_2 regularization term. As the mean of the noise in the power spectrum domain is dependent on its variance in the time domain, the proposed method includes a variance estimation step, which allows more robust blind deconvolution. Validation of the method on both simulated and real data, and of its performance, are compared with two well-known methods from the literature: the deconvolution approach for the mapping of acoustic sources, and sound density modeling

Some Physical and Chemical Characteristics of some "gardud" Soils in the State pf North Kordofan, Sudan

    Four locations south of Elobied town in the State of North Kordofan were selected to represent three "gardud" soils in May-June 1996. These locations were Aradayia, Umood, Bangadeed and Kaba. The results of the study showed that the soils are genetically young and are, therefore classified as Entisols and Inceptisols. The soil at Kaba is coarse loamy and those at Umood, Aradayia and Bangadeed are fine loamy. Kaolinite is the dominant clay type of the soil at Kaba, whereas smectites dominate the other three soils. The soil at Kaba is conceived to be formed in situ from sandstone as substantiated by grain-size. distribution, while the other three soils are of alluvial origin. The compactness of the soils at Aradayia, Umood, and Bangadeed is seen as a result of cementation of coarse soil separates by smectitic clays, whereas that of the soil at Kaba is envisaged as cementation by iron oxides. The soils at Aradayia, Umood, and Bangadeed are moderately fertile, whereas that at Kaba is infertile. The former three soils can be utilized under judicious cultural practices that increase soil permeability for rain water, lessen erosion hazard, and conserve soil moisture, in addition to rational application of nitrogen fertilizers. The "gardud" soils at Aradayia, Umood and Bangadeed have promising potential for field crops suited to the prevalent climate in the area, whereas the "gardud" soil at Kaba is best suited for natural grazing and tree crops, i.e., Acacia senegal (Hashab)

Multi-modal adversarial autoencoders for recommendations of citations and subject labels

We present multi-modal adversarial autoencoders for recommendation and evaluate them on two different tasks: citation recommendation and subject label recommendation. We analyze the effects of adversarial regularization, sparsity, and different input modalities. By conducting 408 experiments, we show that adversarial regularization consistently improves the performance of autoencoders for recommendation. We demonstrate, however, that the two tasks differ in the semantics of item co-occurrence in the sense that item co-occurrence resembles relatedness in case of citations, yet implies diversity in case of subject labels. Our results reveal that supplying the partial item set as input is only helpful, when item co-occurrence resembles relatedness. When facing a new recommendation task it is therefore crucial to consider the semantics of item co-occurrence for the choice of an appropriate model