Sharif Digital Repository

Trusses are suitable load-bearing structural systems for heavy concentrated loads. In this paper, it is shown that it is possible to use active control mechanisms to enhance the load-bearing capacity of the trusses. Under heavy loading, some elements of a truss might experience high stresses and show non-linear behavior, resulting in large deformations in the truss. Under such a condition, some elements of the truss might damage which can lead to the collapse of the truss. Application of control forces on some of the degrees of freedom of the truss can render help the truss tolerate larger forces before its collapse. A neural network can then be trained to learn the relationship between the... 

The problem of clustering with side information has received much recent attention and metric learning has been considered as a powerful approach to this problem. Until now, various metric learning methods have been proposed for semi-supervised clustering. Although some of the existing methods can use both positive (must-link) and negative (cannot-link) constraints, they are usually limited to learning a linear transformation (i.e., finding a global Mahalanobis metric). In this paper, we propose a framework for learning linear and non-linear transformations efficiently. We use both positive and negative constraints and also the intrinsic topological structure of data. We formulate our metric... 

In the context of sustainable clean hydrogen production pathways, photovoltaic-electrolyzer systems are one of the most promising alternatives for acquiring hydrogen from renewable energy sources. In fact, determining the optimal set of design and operating variables are always a key issue while coupling directly renewable electricity sources to PEM electrolyzers. Few previous studies have attempted to find the optimal size and operational condition of directly coupled photovoltaic-electrolyzer (PV/El) systems in order to maximize the hydrogen production or to minimize energy transfer loss between photovoltaic devices and the electrolyzer. Nevertheless an easy and efficient approach still... 

A central problem in motor control is to understand how the many biomechanical degrees of freedom are coordinated to achieve a goal. A common assumption is that Central Nervous System (CNS) would minimize a performance index to achieve this goal which is called objective function. In this paper, two popular objective functions are utilized to design the optimal trajectory of trunk movements. A 3D computational method incorporated with 18 anatomically oriented muscles is used to simulate human trunk system. Inverse dynamics allows us to compute torque which is generated around Lumbosacral joint. This torque is divided among muscles by static stability-based optimization. Trunk movement from... 

An inexact nonmonotone filter sequential quadratic programming algorithm is presented for solving general constrained nonlinear programming problems. At every iteration, a steering direction is computed as a minimizer of a linear model of the constraint violation over a trust region. A possible infeasible stationary point can be detected using the steering direction. If the current iterate is not an infeasible stationary point, a strongly convex feasible quadratic programming subproblem is defined to compute a search direction as an inexact solution satisfying some loose and achievable conditions. We prove that the search direction is a descent direction for the constraint violation or the... 

An inexact nonmonotone filter sequential quadratic programming algorithm is presented for solving general constrained nonlinear programming problems. At every iteration, a steering direction is computed as a minimizer of a linear model of the constraint violation over a trust region. A possible infeasible stationary point can be detected using the steering direction. If the current iterate is not an infeasible stationary point, a strongly convex feasible quadratic programming subproblem is defined to compute a search direction as an inexact solution satisfying some loose and achievable conditions. We prove that the search direction is a descent direction for the constraint violation or the... 

We consider nonlinear optimization problems constrained by a system of fuzzy relation equations. The solution set of the fuzzy relation equations being nonconvex, in general, conventional nonlinear programming methods are not practical. Here, we propose a genetic algorithm with max-product composition to obtain a near optimal solution for convex or nonconvex solution set. Test problems are constructed to evaluate the performance of the proposed algorithm showing alternative solutions obtained by our proposed model  

Nature, as a rich source of solutions, can be an inspirational guide to answer scientific expectations. Seed dispersal mechanism as one of the most common reproduction method among the plants is a unique technique with millions of years of evolutionary history. In this paper, inspired by plants survival, a novel method of optimization is presented, which is called Fertile Field Algorithm. One of the main challenges of stochastic optimization methods is related to the efficiency of the searching process for finding the global optimal solution. Seeding procedure is the most common reproduction method among all the plants. In the proposed method, the searching process is carried out through a... 

We present a new filter trust-region approach for solving unconstrained nonlinear optimization problems making use of the filter technique introduced by Fletcher and Leyffer to generate non-monotone iterations. We also use the concept of a multidimensional filter used by Gould et al. (SIAM J. Optim. 15(1):17-38, 2004) and introduce a new filter criterion showing good properties. Moreover, we introduce a new technique for reducing the size of the filter. For the algorithm, we present two different convergence analyses. First, we show that at least one of the limit points of the sequence of the iterates is first-order critical. Second, we prove the stronger property that all the limit points... 

This paper addresses the problem of optimal state-feedback design for a class of non-linear systems. The method is applicable to all non-linear systems which can be linearised using the method of state-feedback linearisation. The alternative is to use linear optimisation techniques for the linearised equations, but then there is no guarantee that the original non-linear system behaves optimally. The authors use feedback linearisation technique to linearise the system and then design a state feedback for the feedback-linearised system in such a way that it ensures optimal performance of the original non-linear system. The method cannot ensure global optimality of the solution but the global... 

During design, microsatellites are subject to different changes like the changes in weight, location and dimension of parts and elements of subsystems and the payload. These changes should be accumulated by the structure subsystem in a way that some key structural parameters like the center of gravity (CG) and inertia moments remain unchanged. This subject is also important regarding the design of a modular structure of a satellite. In the present study it is tried to accommodate any changes in weight and location of any part(s) and element(s) of a microsatellite by optimum (minimum) rearrangement of other parts on their plane of rest in order to keep the CG and inertia moments unchanged.... 

Calculation of camera projection matrix, also called camera calibration, is an essential task in many computer vision and 3D data processing applications. Calculation of projection matrix using vanishing points and vanishing lines is well suited in the literature; where the intersection of parallel lines (in 3D Euclidean space) when projected on the camera image plane (by a perspective transformation) is called vanishing point and the intersection of two vanishing points (in the image plane) is called vanishing line. The aim of this paper is to propose a new formulation for easily computing the projection matrix based on three orthogonal vanishing points. It can also be used to calculate the... 

This study developed and validated a lumped parameter model for the FLEXI-BAR, a popular training instrument that provides vibration stimulation. The model which can be used in conjunction with musculoskeletal-modeling software for quantitative biomechanical analyses, consists of 3 rigid segments, 2 torsional springs, and 2 torsional dashpots. Two different sets of experiments were conducted to determine the model's key parameters including the stiffness of the springs and the damping ratio of the dashpots. In the first set of experiments, the free vibration of the FLEXI-BAR with an initial displacement at its end was considered, while in the second set, forced oscillations of the bar were... 

In this paper, the optimal performance of a planar humanlike musculoskeletal arm is investigated during reaching movements employing an optimal control policy. The initial and final states (position and velocity) are the only known data of the response trajectory. Two biomechanical objective functions are taken into account to be minimized as the central nervous system (CNS) strategy: (1) a quadratic function of muscle stresses (or forces), (2) total time of movement plus a quadratic function of muscle stresses. A two-degress of freedom (DOF) nonlinear musculoskeletal arm model (for planar movements) with six muscle actuators and four state variables is used in order to evaluate the proposed... 

Reliability of machinery and equipment in flexible manufacturing systems are among the most important issues to reduce production costs and to increase efficiency. This paper investigates the reliability of machinery in job shop production systems, where materials, parts, and other production needs are handled by automated guided vehicles (AGV). The failures time of the parallel machines in a given shop follow either an exponential or a Weibull distribution. As there is no closed-form equation to calculate the reliability of the shop in the Weibull case, a simulation approach is taken in this paper to estimate the reliability. Then, a bi-objective nonlinear optimization model is developed... 

The goal of this work is to propose a hybrid algorithm called real-coded self-organizing migrating genetic algorithm by combining real-coded genetic algorithm (RCGA) and self-organizing migrating algorithm (SOMA) for solving bound-constrained nonlinear optimization problems having multimodal continuous functions. In RCGA, exponential ranking selection, whole-arithmetic crossover and non-uniform mutation operations have been used as different operators where as in SOMA, a modification has been done. The performance of the proposed hybrid algorithm has been tested by solving a set of benchmark optimization problems taken from the existing literature. Then, the simulated results have been... 

A number of challenges including instability, nonconvergence, nonuniqueness, nonoptimality, and lack of a general guideline for inverse modelling have limited the application of automatic calibration by generic inversion codes in solving the saltwater intrusion problem in real-world cases. A systematic parameter selection procedure for the selection of a small number of independent parameters is applied to a real case of saltwater intrusion in a small island aquifer system in the semiarid region of the Persian Gulf. The methodology aims at reducing parameter nonuniqueness and uncertainty and the time spent on inverse modelling computations. Subsequent to the automatic calibration of the... 

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... 

This paper presents a novel opposition-based self-adaptive modified gravitational search algorithm (OSAMGSA) for optimal reactive power dispatch and voltage control in power-system operation. The problem is formulated as a mixed integer, nonlinear optimization problem, which has both continuous and discrete control variables. In order to achieve the optimal value of loss, voltage deviation, and voltage stability index, it is necessary to find the optimal value of control variables such as the tap positions of tap changing transformers, generator voltages, and compensation capacitor. Therefore, this complicated problem needs to be solved by an accurate optimization algorithm. This paper... 

Active control of multi-storey frame structures with nonlinear hysteretic response has been studied in this paper. A new nonlinear optimal control algorithm based on nonlinear Newmark integration method and distributed genetic algorithm (DGA) has been developed. The objective has been to reduce the response to below any desired level. In this study, DGA has been used to determine the weights in the control law corresponding to displacement, velocity and acceleration. The capabilities of the method has been assessed through simulation where an eight-storey frame with bilinear hysteretic behaviour has been subjected to a white noise ground acceleration and controlled by the controllers...