Sharif Digital Repository

The objective of this paper is to demonstrate a topology optimization method subject to displacement and stress constraints. The method does not rely on pseudo-densities; instead it exploits the concept of topological level-set where 'partial' elements are avoided. Consequently: (1) the stresses are well-defined at all points within the evolving topology, and (2) the finite-element analysis is robust and efficient. Further, in the proposed method, a series of topologies of decreasing volume fractions are generated in a single optimization run. The method is illustrated through numerical experiments in 2D  

Learning sparsifying dictionaries from a set of training signals has been shown to have much better performance than pre-designed dictionaries in many signal processing tasks, including image enhancement. To this aim, numerous practical dictionary learning (DL) algorithms have been proposed over the last decade. This paper introduces an accelerated DL algorithm based on iterative proximal methods. The new algorithm efficiently utilizes the iterative nature of DL process, and uses accelerated schemes for updating dictionary and coefficient matrix. Our numerical experiments on dictionary recovery show that, compared with some well-known DL algorithms, our proposed one has a better convergence... 

Synchronization of fractional-order hyperchaotic complex systems is an interesting phenomenon since it has several applications in applied sciences. Based on the complexity of hyperchaotic dynamical systems and unpredictability of hidden attractors, which may exist in their phase spaces and could be beneficial in secure communications, a scheme to derive the analytical formula of the control functions is stated to study active control synchronization. Further comparison of this analytical formula with numerical experiments yields an interesting agreement. A novel fractional-order complex Sprott system is proposed to study this kind of synchronization and its dynamics. A secure communication... 

Graph signal processing (GSP) have found many applications in different domains. The underlying graph may not be available in all applications, and it should be learned from the data. There exist complicated data, where the graph changes over time. Hence, it is necessary to estimate the dynamic graph. In this paper, a new dynamic graph learning algorithm, called dynamic K-graphs, is proposed. This algorithm is capable of both estimating the time-varying graph and clustering the temporal graph signals. Numerical experiments demonstrate the high performance of this algorithm compared with other algorithms. © 2021 European Signal Processing Conference, EUSIPCO. All rights reserved  

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 projected gradient method is applied as an effective gradient-based topology optimization algorithm in order to direct energy propagation through the desired region of composites microstructure. Rayleigh Damping model is also used in order to take the effect of internal damping mechanisms into account and thus, to fill in the gap between the designed layouts and those in reality. The success of the proposed algorithm is illustrated through several numerical experiments by revealing a set of various designed optimal layouts besides their corresponding energy distributions  

Coupled continuous quadratic knapsack problems (CCK) are introduced in the present study. The solution of a CCK problem is equivalent to the projection of an arbitrary point onto the volume constrained Gibbs N-simplex, which has a wide range of applications in computational science and engineering. Three algorithms have been developed in the present study to solve large scale CCK problems. According to the numerical experiments of this study, the computational costs of presented algorithms scale linearly with the problem size, when it is sufficiently large. Moreover, they show competitive or even superior computational performance compared to the advanced QP solvers. The ease of... 

Continuous-domain visual signals are usually captured as discrete (digital) images. This operation is not invertible in general, in the sense that the continuous-domain signal cannot be exactly reconstructed based on the discrete image, unless it satisfies certain constraints (e.g., bandlimitedness). In this paper, we study the problem of recovering shape images with smooth boundaries from a set of samples. Thus, the reconstructed image is constrained to regenerate the same samples (consistency), as well as forming a shape (bilevel) image. We initially formulate the reconstruction technique by minimizing the shape perimeter over the set of consistent binary shapes. Next, we relax the... 

The regularity problem of heat conduction equation corresponding to modeling of solidification based on the latent heat evolution approach is considered in the present study. It is shown that the corresponding PDE is actually semi-smooth, an issue that has not been taken into account explicitly in the related literature. A general smoothing (regularization) strategy is introduced to solve this problem. More specifically, the smoothed version of the effective heat capacity method is presented in this work. The presented approach is applied to model the quasi steady-state heat transfer problem in the continuous casting process. Numerical experiments demonstrate the success of the presented... 

This paper represents a numerical study on the effects of landslide initial submergence and its geotechnical and rheological properties on the characteristics of landslide-generated waves (LGWs) and landslide deformation. A number of 117 numerical experiments are performed using a two-layer Coulomb Mixture Flow (2LCMFlow) model on a real-sized numerical flume as a simplified cross section of the Maku dam reservoir, located in the Northwest of Iran. Three different initial locations are considered for landslide representing a subaerial (SAL), a semi-submerged (SSL), and a submarine (SML) landslide. Based on the numerical results, the majority of SMLs and in some cases SSLs generate tsunami... 

Incorporating outsourcing in scheduling is addressed by several researchers recently. However, this scope is not investigated thoroughly, particularly in the job shop environment. In this paper, a new job shop scheduling problem is studied with the option of jobs outsourcing. The problem objective is to minimise a weighted sum of makespan and total outsourcing cost. With the aim of solving this problem optimally, two solution approaches of combinatorial optimisation problems, i.e. mathematical programming and constraint programming are examined. Furthermore, two problem relaxation approaches are developed to obtain strong lower bounds for some large scale problems for which the optimality is... 

In this letter, we consider the problem of blind source separation under certain nonlinear mixing conditions using a deep learning approach. Conventionally, the separation of sources within linear mixtures is achieved by applying the independence property of the sources. In the nonlinear regime, however, this property is no longer sufficient. In this letter, we consider nonlinear mixing operators where the non-linearity could be fairly approximated using a Taylor series. Next, for solving the nonlinear BSS problem, we design an end-to-end recurrent neural network (RNN) that learns the inverse of the system, and ultimately separates the sources. For training the RNN, we employ a set of... 

In this paper, we introduce a novel and robust approach to quantized matrix completion. First, we propose a rank minimization problem with constraints induced by quantization bounds. Next, we form an unconstrained optimization problem by regularizing the rank function with Huber loss. Huber loss is leveraged to control the violation from quantization bounds due to two properties: first, it is differentiable; and second, it is less sensitive to outliers than the quadratic loss. A smooth rank approximation is utilized to endorse lower rank on the genuine data matrix. Thus, an unconstrained optimization problem with differentiable objective function is obtained allowing us to advantage from... 

One important heat transfer application in engineering is to predict the flow behavior and heat transfer rate in thin vertical air layers. There are numerous applications in engi- neering where high temperature gradients exist between the slot walls. In such cases, the methods based on simple Boussinesq approximations do not provide reliable predictions. Unfortunately, the compressibility effect in heat transfer rate through thin vertical slots has not been much investigated by the past investigators. In this work, a compressible algorithm is properly developed and utilized to solve compressible natural convection in vertical air layers. The current technique employs discretization equations... 

This article proposes a hybrid optimization algorithm based on a modified BFGS and particle swarm optimization to solve medium scale nonlinear programs. The hybrid algorithm integrates the modified BFGS into particle swarm optimization to solve augmented Lagrangian penalty function. In doing so, the algorithm launches into a global search over the solution space while keeping a detailed exploration into the neighborhoods. To shed light on the merit of the algorithm, we provide a test bed consisting of 30 test problems to compare our algorithm against two of its variations along with two state-of-the-art nonlinear optimization algorithms. The numerical experiments illustrate that the proposed... 

In this paper, a new algorithm for solving constrained nonlinear programming problems is presented. The basis of our proposed algorithm is none other than the necessary and sufficient conditions that one deals within a discrete constrained local optimum in the context of the discrete Lagrange multipliers theory. We adopt a revised particle swarm optimization algorithm and extend it toward solving nonlinear programming problems with continuous decision variables. To measure the merits of our algorithm, we provide numerical experiments for several renowned benchmark problems and compare the outcome against the best results reported in the literature. The empirical assessments demonstrate that... 

This study presents a novel perspective on pressure-transient analysis (PTA) of downhole-pressure and flow-rate data by use of system-identification (SI) techniques as widely used in advanced process engineering. Key features of the paper are that it considers the classic PTA process from a system-theoretical perspective; derives the causal structure of the flow dynamics; proposes a method to deal with continuously varying pressure and flow-rate signals contaminated with correlated noise, which estimates physical reservoir parameters through a systematic matching procedure in the frequency domain; and can cope with arbitrary (i.e., not necessarily piecewise constant) flow-rate signals. To... 

We present a sampling theory for a class of binary images with finite rate of innovation (FRI). Every image in our model is the restriction of 1{p le; 0} to the image plane, where 1 denotes the indicator function and p is some real bivariate polynomial. This particularly means that the boundaries in the image form a subset of an algebraic curve with the implicit polynomial p. We show that the image parameters - i.e., the polynomial coefficients'satisfy a set of linear annihilation equations with the coefficients being the image moments. The inherent sensitivity of the moments to noise makes the reconstruction process numerically unstable and narrows the choice of the sampling kernels to... 

Train scheduling has been one of the significant issues in the railway industry in recent years since it has an important role in efficacy of railway infrastructure. In this paper, the timetabling problem of a multiple-tracked railway network is discussed. More specifically, a general model is presented here in which a set of operational and safety requirements is considered. The model handles the trains overtaking in stations and considers the stations' capacity. The objective function is to minimize the total travel time. Unfortunately, the problem is NP-hard, and real-sized problems cannot be solved in an acceptable amount of time. In order to reduce the processing time, we presented some... 

Green and sustainability issues are being considered extensively in industry and in the literature. Most of the green-related effects in the manufacturing sector can be modeled by the costs associated with the production machines due to their resource consumption and/or pollutant emission, as a part of the operation cost. Accordingly, taking into account the machine processing costs alongside the other production criteria is one of the main approaches to consider green issues in the production management problems. In the same way, scheduling as one of the major operational problems in the factories can benefit this point of view to involve sustainability aspects. In this paper, the...