Sharif Digital Repository

Theoretical and numerical analyses of rotating disks with non-uniform thickness and material properties subjected to thermo-mechanical loadings have been carried out by variable material properties (VMP), Runge-Kutta's (RK) and finite element (FE) methods. The material is assumed to be elastic-linear hardening. A power form function is used to describe the temperature gradient with the higher temperature at outer surface. Von-Mises theory has been used as failure criterion. The effects of geometry, material and thermal loading parameters as well as boundary conditions on radial, hoop and equivalent stress distributions which have not been studied in much detail in previous works have been... 

In this paper, a parallel algorithm for computing the roots of a given polynomial of degree n on a ring of processors is proposed. The algorithm implements Durand-Kerner's method and consists of two phases: initialisation, and iteration. In the initialisation phase all the necessary preparation steps are realised to start the parallel computation. It includes register initialisation and initial approximation of roots requiring 3n - 2 communications, 2 exponentiation, one multiplications, 6 divisions, and 4n - 3 additions. In the iteration phase, these initial approximated roots are corrected repeatedly and converge to their accurate values. The iteration phase is composed of some iteration... 

A new semianalytical solution is developed for the velocity-dependent dispersion Henry problem using the Fourier-Galerkin method (FG). The integral arising from the velocity-dependent dispersion term is evaluated numerically using an accurate technique based on an adaptive scheme. Numerical integration and nonlinear dependence of the dispersion on the velocity render the semianalytical solution impractical. To alleviate this issue and to obtain the solution at affordable computational cost, a robust implementation for solving the nonlinear system arising from the FG method is developed. It allows for reducing the number of attempts of the iterative procedure and the computational cost by... 

Centroid-based methods including k-means and fuzzy c-means are known as effective and easy-to-implement approaches to clustering purposes in many applications. However, these algorithms cannot be directly applied to supervised tasks. This paper thus presents a generative model extending the centroid-based clustering approach to be applicable to classification and regression tasks. Given an arbitrary loss function, the proposed approach, termed Supervised Fuzzy Partitioning (SFP), incorporates labels information into its objective function through a surrogate term penalizing the empirical risk. Entropy-based regularization is also employed to fuzzify the partition and to weight features,... 

In this paper, based on a successively accuracy-increasing approximation of the ℓ0 norm, we propose a new algorithm for recovery of sparse vectors from underdetermined measurements. The approximations are realized with a certain class of concave functions that aggressively induce sparsity and their closeness to the ℓ0 norm can be controlled. We prove that the series of the approximations asymptotically coincides with the ℓ1 and ℓ0 norms when the approximation accuracy changes from the worst fitting to the best fitting. When measurements are noise-free, an optimization scheme is proposed that leads to a number of weighted ℓ1 minimization programs, whereas, in the presence of noise, we propose... 

Inverse design in external flow regimes usually involves finding the wall shape associated with a prescribed distribution of wall pressure or velocity. In this research, a novel iterative inverse design method is developed for inviscid subsonic and transonic external flow regimes. The method links up a novel inverse design algorithm, called Ball-Spine Algorithm (BSA), and a 2D inviscid analysis code. The Euler equations are solved for a physical domain of which some unknown boundaries are iteratively modified via BSA until a prescribed pressure distribution is reached. In BSA, the unknown walls are composed of a set of virtual balls that move freely along the specified directions called... 

In this study a structured multiblock grid is used to solve two-dimensional transient inverse heat conduction problems. The multiblock method is implemented for geometric decomposition of the physical domain into regions with blocked interfaces. The finite-element method is employed for direct solution of the transient heat conduction equation in a Cartesian coordinate system. Inverse algorithms used in this research are iterative Levenberg-Marquardt and adjoint conjugate gradient techniques for parameter and function estimations. The measured transient temperature data needed in the inverse solution are given by exact or noisy data. Simultaneous estimation of unknown linear/nonlinear... 

The aim of this study is to develop iterative regularization algorithms based on parameter and function estimation techniques to solve two-dimensional/axisymmetric transient inverse heat conduction problems in curvilinear coordinate system. The multiblock method is used for geometric decomposition of the physical domain into regions with patched-overlapped interface grids. The central finite-difference version of the alternating-direction implicit technique together with structured body-fitted grids is implemented for numerical solution of the direct problem and other partial differential equations derived by inverse analysis. The approach of estimating unknown parameters and functions is... 

A new method for structural optimization is presented for successive approximation of the objective function and constraints in conjunction with Lagrange multipliers approach. The focus is on presenting the methodology with simple examples. The basis of the iterative algorithm is that after each iteration, it brings the approximate location of the estimated minimum closer to the exact location, gradually. In other words, instead of the linear or parabolic term used in Taylor expansion, which works based on a short step length, an arch is used that has a constant curvature but a longer step length. Using this approximation, the equations of optimization involve the Lagrange multipliers as the... 

The objective of this paper is to introduce and demonstrate an algorithm for stress-constrained topology optimization. The algorithm relies on tracking a level-set defined via the topological derivative. The primary advantages of the proposed method are: (1) the stresses are well-defined at all points within the evolving topology, (2) the finite-element stiffness matrices are well-conditioned, making the analysis robust and efficient, (3) the level-set is tracked through a simple iterative process, and (4) the stress constraint is precisely satisfied at termination. The proposed algorithm is illustrated through numerical experiments in 2D and 3D  

In this paper, the static pull-in behavior of electrostatically actuated functionally graded (FG) micro-beams resting on an elastic medium is studied using the modified strain gradient (MSG) theory. To this end, the equilibrium equation along with classical and non-classical boundary conditions is obtained by considering the fringing field and elastic foundations effects within the principle of minimum total potential energy. Also, the elastic medium is composed of a shear layer (Pasternak foundation) and a linear normal layer (Winkler foundation). The governing differential equation is solved for cantilever and doubly fixed FG beams using an iterative numerical method. This method is a... 

Under different loading conditions, the over-head cranes may experience a wide range of model parameters variation. A robust control strategy is developed to achieve the high positioning accuracy, short transportation time and suppression of swing angle for an uncertain over-head crane system. Over-head crane is modeled as a three degrees of freedom system and control problem is investigated for two cases: a system with a single control input (the force on trolley) and a system with two control inputs (the force on trolley and the torque on lifter). Regulator and observer systems are designed. To achieve the tracking objectives, an optimal robust controller is designed based on μ-synthesis... 

Under different loading conditions, the over-head cranes may experience a wide range of model parameters variation. A robust control strategy is developed to achieve the high positioning accuracy, short transportation time and suppression of swing angle for an uncertain over-head crane system. Over-head crane is modeled as a three degrees of freedom system and control problem is investigated for two cases: a system with a single control input (the force on trolley) and a system with two control inputs (the force on trolley and the torque on lifter). Regulator and observer systems are designed. To achieve the tracking objectives, an optimal robust controller is designed based on μ-synthesis... 

Water cycle algorithm (WCA) is a population-based metaheuristic algorithm, inspired by the water cycle process and movement of rivers and streams towards sea. The WCA shows good performance in both exploration and exploitation phases. Further, the relationship between improvised exploitation and each parameter under asymmetric interval is derived and an iterative convergence of WCA is proved theoretically. In this paper, CEC’15 computationally expensive benchmark problems (i.e., 15 problems) have been considered for efficiency measurement of WCA accompanied with other optimisers. Also, a new discretisation strategy for the WCA has been proposed and applied along with other optimisers for... 

The ARMA filtered fractionally differenced Gaussian Noise (FdGn) model and a new AR Filtered FdGn Added up model are applied to speech signal and performance of their parameters on speech Unvoiced/Voiced/Mixed/Silence classification is evaluated against Zero Crossing Rate (ZCR) feature. For parameter estimation of AR filtered FdGn model two methods were applied: iterative Maximum Likelihood (ML) method of Tewfik and a new computationally efficient Linear Minimum Square Error (LMSE) algorithm Also for parameters estimation of new Added up model two approaches were implemented: an Expectation-Maximization (EM) based approach and an iterative MSE approach. The described models and methods were... 

This paper is concerned with designing efficient algorithms for recovering sparse signals from noisy underdetermined measurements. More precisely, we consider minimization of a nonsmooth and nonconvex sparsity promoting function subject to an error constraint. To solve this problem, we use an alternating minimization penalty method, which ends up with an iterative proximal-projection approach. Furthermore, inspired by accelerated gradient schemes for solving convex problems, we equip the obtained algorithm with a so-called extrapolation step to boost its performance. Additionally, we prove its convergence to a critical point. Our extensive simulations on synthetic as well as real data verify... 

Classical sampling theorem states that by using an anti-aliased low-pass filter at the Nyquist rate, one can transmit and retrieve the filtered signal. This approach, which has been used for decades in signal processing, is not good for high quality speech, image and video signals where the actual signals are not low-pass but rather sparse. The traditional sampling theorems do not work for sparse signals. Modern approach, developed by statisticians at Stanford (Donoho and Candes), give some lower bounds for the minimum sampling rate such that a sparse signal can be retrieved with high probability. However, their approach, using a sampling matrix called compressive matrix, has certain... 

This paper investigates the problem of recovering missing samples using methods based on sparse representation adapted for visually enhanced quality of reconstruction of image signals. Although, the popular Mean Square Error (MSE) criterion is convex and simple, it may not be entirely consistent with Human Visual System (HVS). Thus, instead of ℓ2-norm or MSE, a new perceptual quality measure is used as the similarity criterion between the original and the reconstructed images. The proposed criterion called Convex SIMilarity (CSIM) index is a modified version of the Structural SIMilarity (SSIM) index, which despite its predecessor, is convex and uni-modal. We derive mathematical properties... 

In this paper, a new algorithm for source recovery in under-determined Sparse Component Analysis (SCA) or atomic decomposition on over-complete dictionaries is presented in the noisy case. The algorithm is essentially a method for obtaining sufficiently sparse solutions of under-determined systems of linear equations with additive Gaussian noise. The method is based on iterative Expectation-Maximization of a Maximum A Posteriori estimation of sources (EM-MAP) and a new steepest-descent method is introduced for the optimization in the Mstep. The solution obtained by the proposed algorithm is compared to the minimum ℓ1-norm solution achieved by Linear Programming (LP). It is experimentally... 

In this paper, we propose an algorithm for recovery of sparse and low-rank components of matrices using an iterative method with adaptive thresholding. In each iteration of the algorithm, the low-rank and sparse components are obtained using a thresholding operator. The proposed algorithm is fast and can be implemented easily. We compare it with the state-of-the-art algorithms. We also apply it to some applications such as background modeling in video sequences, removing shadows and specularities from face images, and image restoration. The simulation results show that the proposed algorithm has a suitable performance with low run-time. © 2017 Elsevier Inc