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  • Open Access


    Atrous Convolution-Based Residual Deep CNN for Image Dehazing with Spider Monkey–Particle Swarm Optimization

    CH. Mohan Sai Kumar*, R. S. Valarmathi

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1711-1728, 2023, DOI:10.32604/iasc.2023.038113

    Abstract Image dehazing is a rapidly progressing research concept to enhance image contrast and resolution in computer vision applications. Owing to severe air dispersion, fog, and haze over the environment, hazy images pose specific challenges during information retrieval. With the advances in the learning theory, most of the learning-based techniques, in particular, deep neural networks are used for single-image dehazing. The existing approaches are extremely computationally complex, and the dehazed images are suffered from color distortion caused by the over-saturation and pseudo-shadow phenomenon. However, the slow convergence rate during training and haze residual is the two… More >

  • Open Access


    Impact of Artificial Compressibility on the Numerical Solution of Incompressible Nanofluid Flow

    Tohid Adibi1, Shams Forruque Ahmed2,*, Seyed Esmail Razavi3, Omid Adibi4, Irfan Anjum Badruddin5, Syed Javed5

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5123-5139, 2023, DOI:10.32604/cmc.2023.034008

    Abstract The numerical solution of compressible flows has become more prevalent than that of incompressible flows. With the help of the artificial compressibility approach, incompressible flows can be solved numerically using the same methods as compressible ones. The artificial compressibility scheme is thus widely used to numerically solve incompressible Navier-Stokes equations. Any numerical method highly depends on its accuracy and speed of convergence. Although the artificial compressibility approach is utilized in several numerical simulations, the effect of the compressibility factor on the accuracy of results and convergence speed has not been investigated for nanofluid flows in… More >

  • Open Access


    Accelerated Iterative Learning Control for Linear Discrete Systems with Parametric Perturbation and Measurement Noise

    Xiaoxin Yang1, Saleem Riaz2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.132, No.2, pp. 605-626, 2022, DOI:10.32604/cmes.2022.020412

    Abstract An iterative learning control algorithm based on error backward association and control parameter correction has been proposed for a class of linear discrete time-invariant systems with repeated operation characteristics, parameter disturbance, and measurement noise taking PD type example. Firstly, the concrete form of the accelerated learning law is presented, based on the detailed description of how the control factor is obtained in the algorithm. Secondly, with the help of the vector method, the convergence of the algorithm for the strict mathematical proof, combined with the theory of spectral radius, sucient conditions for the convergence of More >

  • Open Access


    Geometrically Nonlinear Analysis of Structures Using Various Higher Order Solution Methods: A Comparative Analysis for Large Deformation

    Ali Maghami1, Farzad Shahabian1, Seyed Mahmoud Hosseini2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.121, No.3, pp. 877-907, 2019, DOI:10.32604/cmes.2019.08019

    Abstract The suitability of six higher order root solvers is examined for solving the nonlinear equilibrium equations in large deformation analysis of structures. The applied methods have a better convergence rate than the quadratic Newton-Raphson method. These six methods do not require higher order derivatives to achieve a higher convergence rate. Six algorithms are developed to use the higher order methods in place of the NewtonRaphson method to solve the nonlinear equilibrium equations in geometrically nonlinear analysis of structures. The higher order methods are applied to both continuum and discrete problems (spherical shell and dome truss).… More >

  • Open Access


    An adaptive load stepping algorithm for path-dependent problems based on estimated convergence rates

    M.T.C. Araújo Fernandes1, C.O. Cardoso2, W.J. Mansur3

    CMES-Computer Modeling in Engineering & Sciences, Vol.113, No.3, pp. 325-342, 2017, DOI:10.3970/cmes.2017.113.341

    Abstract A new adaptive (automatic) time stepping algorithm, called RCA (Rate of Convergence Algorithm) is presented. The new algorithm was applied in nonlinear finite element analysis of path-dependent problems. The step size is adjusted by monitoring the estimated convergence rate of the nonlinear iterative process. The RCA algorithm is relatively simple to implement, robust and its performance is comparable to, and in some cases better than, the automatic load incrementaion algorithm existent in commercial codes. Discussions about the convergence rate of nonlinear iterative processes, an estimation of the rate and a study of the parameters of More >

  • Open Access


    A Node-Based Smoothed eXtended Finite Element Method (NS-XFEM) for Fracture Analysis

    N. Vu-Bac1, H. Nguyen-Xuan2, L. Chen3, S. Bordas4, P. Kerfriden4, R.N. Simpson4, G.R. Liu5, T. Rabczuk1

    CMES-Computer Modeling in Engineering & Sciences, Vol.73, No.4, pp. 331-356, 2011, DOI:10.3970/cmes.2011.073.331

    Abstract This paper aims to incorporate the node-based smoothed finite element method (NS-FEM) into the extended finite element method (XFEM) to form a novel numerical method (NS-XFEM) for analyzing fracture problems of 2D elasticity. NS-FEM uses the strain smoothing technique over the smoothing domains associated with nodes to compute the system stiffness matrix, which leads to the line integrations using directly the shape function values along the boundaries of the smoothing domains. As a result, we avoid integration of the stress singularity at the crack tip. It is not necessary to divide elements cut by cracks More >

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