Search

In this paper, the fractional calculus approach is employed in the constitutive relationship of the fluid model and the Darcy's law. The flow of the fractional Jeffreys' fluid induced by the impulsive motion of a flat plate in a porous half space is studied in form of an initial and boundary value problem with fractional derivatives. Using the Laplace transform method, we obtain an exact solution of the model in term of Fox's H -function. As a byproduct, the solutions of the analogous flow for the generalized second grade fluid, the fractional Maxwell fluid and the Newtonian fluid in the porous half space are also deduced. In addition, the influence of the material parameters and the fractional parameters on the fluid motion is investigated, as well as a comparison among the fractional Jeffreys' fluid, the fractional Maxwell fluid and the Newtonian fluid in porous medium is also analyzed by graphical illustrations. [ABSTRACT FROM AUTHOR]

Some boundedness results are established for commutators generated by classical fractional Hardy operators and one-sided CMO functions on the homogeneous Herz spaces, which extends some known results. [ABSTRACT FROM AUTHOR]

In this paper, we study the nonlinear Schrödinger–Kirchhoff-type equation with pure power nonlinearities in R 3 by variational methods. By carrying out the constrained minimization on a special manifold which is a combination of the Nehari manifold and Pohozaev manifold, we proved the existence of positive radial solutions of this equation for the power p ∈ (1 , 5). The results of this paper extend some existing conclusions, especially for p ∈ (1 , 2 ]. [ABSTRACT FROM AUTHOR]

• Overall structural framework designed for the restoration of motion-blurred images is proposed with the aid of PSO-based parameter estimation and image quality assessment. • The proposed Image Restoration Framework has a complete function which can effectively enhance the restored image quality. • Blur parameter estimation algorithm based on PSO (BPPO) is employed to optimize the motion-blurred parameter estimation. • A polynomial-based radial basis function neural network is used as image quality evaluation method to evaluate restored image quality for efficient classification. The restoration of motion-blurred images has always been a complex problem in image restoration. The current single blurred image algorithm cannot very well solve the estimation error of motion blur parameters. A comprehensive motion-blurred image restoration framework is proposed, which includes motion-blurred data generation, blur parameter estimation, and image quality assessment of restored images. First, we designed and used four image data sets with different degrees of blurring. We innovatively propose a blur parameter estimation algorithm based on the particle swarm optimization (B-PSO) algorithm. The Naturalness Image Quality Evaluator (NIQE) is used as the fitness function of the PSO algorithm. The framework also introduces a polynomial-based radial basis function neural network (P-RBFNN) as a new image quality assessment (IQA) method , with good image classification performance. Test results from public datasets show that the proposed framework can accurately estimate blur parameters. The peak signal-to-noise ratio (PSNR) reaches 29.976 dB, the structural similarity (SSIM) reaches 0.9044, and the classification rate is 96%. The proposed restoration framework produces the best image restoration results. [ABSTRACT FROM AUTHOR]

• AFS and RS are used to effectively obtain the transmittance spectra of black plastic wastes not available in the existing NIR. • A feature selection method of the peak points is considered based on the analysis of chemical information for low cost spectrometer by using a small amount of input variables. • A method of 2D reconstruction through mapping from 1D signal data based on peak points is proposed to effectively deal with spatial structure information between data. • 2DCNN design driven by 2D information features or chemical peak points data leads to high performance as well as anti-noise capability through effective data mapping. In this study, two types of convolutional neural network (CNN) classifiers are designed to handle the problem of classifying black plastic wastes. In particular, the black plastic wastes have the property of absorbing laser light coming from spectrometer. Therefore, the classification of black plastic wastes remains still a challenging problem compared to classifying other colored plastic wastes using existing spectroscopy (i.e., NIR). When it comes the classification problem of black plastic wastes, effective classification techniques by the laser spectroscopy of Fourier Transform-Infrared Radiation (FT-IR) with Attenuated Total Reflectance (ATR) and Raman to analyze the classification problem of black plastic wastes are introduced. Due to the strong ability of extracting spatial features and remarkable performance in image classification, 1D and 2D CNN through data features are designed as classifiers. The technique of chemical peak points selection is considered to reduce data redundancy. Furthermore, through the selection of data features based on the extracted 1D data with peak points is introduced. Experimental results demonstrate that 2DCNN classifier designed with the help of 2D data feature selection as well as 1DCNN classifier shows the best performance compared with other reported methods for classifying black plastic wastes. [ABSTRACT FROM AUTHOR]