Sharif Digital Repository

Particle Swarm Optimization (PSO) is a well-known swarm intelligence (SI) algorithm employed for solving various optimization problems. This algorithm suffers from premature convergence to local optima. Accordingly, a number of PSO variants have been proposed in the literature. These algorithms exploited different schemes to improve performance. In this paper, we propose a new variant of PSO with an enhanced Learning strategy and Crossover operator (PSOLC). This algorithm applies three strategies, comprising altering the exemplar particles, updating the PSO parameters, and integrating PSO with Genetic Algorithm (GA). In the proposed learning strategy, each particle is guided by the best... 

Data cache hit ratio has a major impact on execution performance of programs by effectively reducing average data access time. Prefetching mechanisms improve this ratio by fetching data items that shall soon be required by the running program. Software-driven prefetching enables application-specific policies and potentially provides better results in return for some instruction overhead, whereas hardware-driven prefetching gives little overhead, however general-purpose processors cannot adapt to the specific needs of the running application. In the application-specific processors that we develop customized to an object-oriented application, we implement application-specific hardware... 

In this paper, a hypoelasto-viscoplastic endochronic model is developed to capture the strain localization phenomena. The elastic response is stated in terms of hypoelastic model and endochronic constitutive equations are stated in unrotated frame of reference. The infinitesimal theory of endochronic plasticity is extended to large strain range on the basis of the additive decomposition of the strain rate tensor and hypoelasticity. Constitutive equations are stated in unrotated frame of reference that greatly simplifies endochronic constitutive relations in finite plasticity and yields the efficiency of the presented algorithm by total uncoupling material and geometrical nonlinearities. An... 

Improved load following capability is one of the main technical performances of advanced PWR (APWR). Controlling the nuclear reactor core during load following operation encounters some difficulties. These difficulties mainly arise from nuclear reactor core limitations in local power peaking, while the core is subject to large and sharp variation of local power density during transients. Axial offset (AO) is the parameter usually used to represent of core power peaking, in form of a practical parameter. This paper, proposes a new intelligent approach to AO control of PWR nuclear reactors core during load following operation. This method uses a neural network model of the core to predict the... 

A wide range of scheduling algorithms used in the data centers have traditionally concentrated on enhancement of performance metrics. Recently, with the rapid growth of data centers in terms of both size and number, the power consumption has become a major challenge for both industry and society. At the software level, energy-aware task scheduling is an effective technique for power reduction in the data centers. However, most of the currently proposed energy-aware scheduling approaches are only paying attention to computation cost. In the other words, they ignore the energy consumed by the network equipment, namely communication cost. In this paper, the problem of scheduling... 

Bayesian optimization algorithm (BOA) utilizes a Bayesian network to estimate the probability distribution of candidate solutions and creates the next generation by sampling the constructed Bayesian network. This paper proposes an improved real-coded BOA (IrBOA) for continuous global optimization. In order to create a set of Bayesian networks, the candidate solutions are partitioned by an adaptive clustering method. Each Bayesian network has its own structure and parameters, and the next generation is produced from this set of networks. The adaptive clustering method automatically determines the correct number of clusters so that the probabilistic building-block crossover (PBBC) is... 

A new heuristic filter, called Continuous Ant Colony Filter, is proposed for non-linear systems state estimation. The new filter formulates the states estimation problem as a stochastic dynamic optimization problem and utilizes a colony of ants to find and track the best estimation. The ants search the state space dynamically in a similar scheme to the optimization algorithm, known as Continuous Ant Colony System. The performance of the new filter is evaluated for a nonlinear benchmark and the results are compared with those of Extended Kalman Filter and Particle Filter, showing improvements in terms of estimation accuracy  

This paper presents a cost-efficient technique to jointly use circuit- and architecture-level techniques to protect an embedded processor's register file against soft errors. The basic idea behind the proposed technique is robust register caching (RRC), which creates a cache of the most vulnerable registers within the register file in a small and highly robust cache memory built from circuit-level single-event-upset-protected memory cells. To guarantee that the most vulnerable registers are always stored in the robust register cache, the average number of read operations during a register's lifetime is used as a metric to guide the cache replacement policy. A register is vulnerable to soft... 

Coping with the intrinsic limitations of electrical networks-on-chip, optical on-chip interconnect is emerged as a promising paradigm for future high performance multi-core designs. However, optical networks-on-chip (ONoCs) are drastically vulnerable to on-chip thermal fluctuation. Specifically, electrical power consumed by processing cores induces temperature drift, which may cause false paths for optical data communication through the network. Therefore, customizing electrical power distribution throughout the chip plays a critical role for reliable data communication in ONoCs. On the other hand, chip-scale distribution of electrical power is directly affected by mapping various... 

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... 

With the emergence of Non-Volatile Memories (NVMs) and their shortcomings such as limited endurance and high power consumption in write requests, several studies have suggested hybrid memory architecture employing both Dynamic Random Access Memory (DRAM) and NVM in a memory system. By conducting a comprehensive experiments, we have observed that such studies lack to consider very important aspects of hybrid memories including the effect of: a) data migrations on performance, b) data migrations on power, and c) the granularity of data migration. This paper presents an efficient data migration scheme at the Operating System level in a hybrid DRAM-NVM memory architecture. In the proposed... 

This paper considers the problem of interpolating signals defined on graphs. A major presumption considered by many previous approaches to this problem has been low-pass/band-limitedness of the underlying graph signal. However, inspired by the findings on sparse signal reconstruction, we consider the graph signal to be rather sparse/compressible in the Graph Fourier Transform (GFT) domain and propose the Iterative Method with Adaptive Thresholding for Graph Interpolation (IMATGI) algorithm for sparsity promoting interpolation of the underlying graph signal. We analytically prove convergence of the proposed algorithm. We also demonstrate efficient performance of the proposed IMATGI algorithm... 

Integrated silicon photonic networks have attracted a lot of attention in the recent decades due to their potentials for low-power and high-bandwidth communications. However, these promising networks, as the future technology, are drastically susceptible to thermal fluctuations, which may paralyze wavelength-based operation of these networks. In this regard, precise addressing of thermally induced faults in optical networks-on-chip (ONoCs), as well as revealing practical methods to tackle this challenge will be a break-even point toward implementation of this technology. In this paper, thermal variation is investigated through analyzing on-chip power distribution, which is addressed by power... 

In this paper, a new nonlinear heuristic filter based on the hybridization of an extended Kalman filter and an ant colony estimator is proposed to estimate the states of a nonlinear system. In this filter, a group of virtual ants searches the state space stochastically and dynamically to find and track the best state estimation while the position of each ant is updated at the measurement time using the extended Kalman filter. The performance of the proposed filter is compared with well-known heuristic filters using a nonlinear benchmark problem. The statistical results show that this algorithm is able to provide promising and competitive results. Then, the new filter is tested on a nonlinear... 

In this paper, a new nonlinear heuristic filter based on the hybridization of an extended Kalman filter and an ant colony estimator is proposed to estimate the states of a nonlinear system. In this filter, a group of virtual ants searches the state space stochastically and dynamically to find and track the best state estimation while the position of each ant is updated at the measurement time using the extended Kalman filter. The performance of the proposed filter is compared with well-known heuristic filters using a nonlinear benchmark problem. The statistical results show that this algorithm is able to provide promising and competitive results. Then, the new filter is tested on a nonlinear... 

In this paper, we use simulated annealing heuristics for constructing an intrusion detection system (IDS). The proposed IDS combines the learning ability of simulated annealing heuristics with the approximate reasoning method of fuzzy systems. The use of simulated annealing is an effort to effectively explore the large search space related to intrusion detection problems, and find the optimum set of fuzzy if-then rules. The aim of this paper is to present the capability of simulated annealing based fuzzy system to deal with intrusion detection classification problem as a new real-world application area. Experiments were performed with KDD-Cup99 intrusion detection benchmark data set. © 2008... 

We study the application of cooperative control and game theoretic approaches to wind farm optimization. The conventional (greedy) wind farm control strategy seeks to individually maximize each turbine power. However, this strategy does not maximize the overall power production of wind farms due to the aerodynamic interactions (wake effect) between the turbines. We formulate the wind farm power optimization problem as an identical interest game which can also be used to solve other cooperative control problems. Two model-free learning algorithms are developed to obtain the optimal axial induction factors of the turbines and maximize power production. The algorithms are simulated for a... 

Stencil codes (a.k.a. nearest-neighbor computations) are widely used in image processing, machine learning, and scientific applications. Stencil codes incur nearest-neighbor data exchange because the value of each point in the structured grid is calculated as a function of its value and the values of a subset of its nearest-neighbor points. When running on Graphics Processing Unit (GPUs), stencil codes exhibit a high degree of data sharing between nearest-neighbor threads. Sharing is typically implemented through shared memories, shuffle instructions, and on-chip caches and often incurs performance overheads due to the redundancy in memory accesses. In this article, we propose Neighbor Data... 

A heuristic nonlinear model predictive controller is proposed, based on the gravitational search algorithm. The proposed method models a constrained nonlinear model predictive control problem in the form of a dynamic optimization and uses a set of virtual particles, moving within the search space, to find the best control sequence in an online manner. Particles affect the movement of each other through the gravitational forces. The optimality of the points, experienced by the particles, is evaluated by a cost function. This function reduces the tracking error, control effort, and control chattering. The better control sequence a particle finds, the more mass is assigned to that particle....