Sharif Digital Repository

In modern multi-core Mixed-Criticality (MC) systems, a rise in peak power consumption due to parallel execution of tasks with maximum frequency, specially in the overload situation, may lead to thermal issues, which may affect the reliability and timeliness of MC systems. Therefore, managing peak power consumption has become imperative in multi-core MC systems. In this regard, we propose an online peak power and thermal management heuristic for multi-core MC systems. This heuristic reduces the peak power consumption of the system as much as possible during runtime by exploiting dynamic slack and per-cluster Dynamic Voltage and Frequency Scaling (DVFS). Specifically, our approach examines... 

In modern multicore mixed-criticality (MC) systems, a rise in peak power consumption due to parallel execution of tasks with maximum frequency, specially in the overload situation, may lead to thermal issues, which may affect the reliability and timeliness of MC systems. Therefore, managing peak power consumption has become imperative in multicore MC systems. In this regard, we propose an online peak power and thermal management heuristic for multicore MC systems. This heuristic reduces the peak power consumption of the system as much as possible during runtime by exploiting dynamic slack and per-cluster dynamic voltage and frequency scaling (DVFS). Specifically, our approach examines... 

One of the main challenges of data streams is the occurrence of concept drift. Concept drift is the change of target (or feature) distribution, and can occur in different types: sudden, gradual, incremental or recurring. Because of the forgetting mechanism existing in the data stream learning process, recurring concepts has received much attention recently, and became a challenging problem. This paper tries to exploit the existence of recurring concepts in the learning process and improve the classification of data streams. It uses a pool of concepts to detect the reoccurrence of a concept using two methods: a Bayesian, and a heuristic method. Two approaches are used in the classification... 

Datapath designs that perform polynomial computations over Z 2n are used in many applications such as computer graphics and digital signal processing domains. As the market of such applications continues to grow, improvements in high-level synthesis and optimization techniques for multivariate polynomials have become really challenging. This paper presents an efficient algorithm for optimizing the implementation of a multivariate polynomial over Z2n in terms of the number of multipliers and adders. This approach makes use of promising heuristics to extract more complex common sub-expressions from the polynomial compared to the conventional methods. The proposed algorithm also utilizes a... 

This paper describes a novel approach will be called guided hill climbing to improve the efficiency of hill climbing in the planning domains. Unlike simple hill climbing, which evaluates the successor states without any particular order, guided hill climbing evaluates states according to an order recommended by an auxiliary guiding heuristic function. Guiding heuristic function is a self-adaptive and cost effective function based on the main heuristic function of hill climbing. To improve the performance of the method in various domains, we defined several heuristic functions and created a mechanism to choose appropriate functions for each particular domain. We applied the guiding method to... 

Photovoltaic (PV) panel accurate modeling is essential for operational studies and generation estimation under actual conditions and power electronic control device designs in grid connected or isolated power systems. Inaccuracy in modeling parameters such as photon's current, diode's saturation current, series and parallel resistances and diodes ideality coefficients causes errors in modeling and simulating photovoltaic panel's operation. In this paper, photovoltaic panel's parameters are estimated according to characteristic curves obtained from experiments. Heuristic optimization methods are utilized to calculate parameters for both singlediode and double-diode models. Results indicate... 

Given an undirected graph with weights associated with its edges, the Steiner tree problem consists of finding a minimum weight subtree spanning a given subset of (terminal) nodes of the original graph. Minimum Spanning Tree Heuristic (MSTH) is a heuristic for solving the Steiner problem in graphs. In this paper we first review existing algorithms for solving the Steiner problem in graphs. We then introduce a new parallel version of MSTH on three dimensional mesh of trees architecture. We describe our algorithm and analyze its time complexity. The time complexity analysis shows that the algorithm's running time is O(lg2 n) which is comparable with other existing parallel solutions. © 2007... 

Transportation Discrete Network Design Problem (TDNDP) aims at choosing a subset of proposed projects to minimize the users' total travel time with respect to a budget constraint. Because TDNDP is a hard combinatorial problem, recent research has widely addressed heuristic approaches and ignored the exact solution. This paper explores how application of parallel computation can affect the performance of an exact algorithm in TDNDP. First, we show that the Branch-and-Bound (B&B) algorithm, proposed by LeBlanc, is well adapted to a parallel design with synchronized Master-Slave (MS) paradigm. Then, we develop a parallel B&B algorithm and implement it with two search strategies of... 

Summary Increasing interest in smart grids exhibits its potential benefits for providing reliable, secure, efficient, environmental friendly and sustainable electricity from renewable energy resources. Here, reliability models of four types of renewable and hybrid distributed generation were developed. A fuzzy multi-objective function was suggested for simultaneous optimization of reliability, electricity generation cost, grid loss and voltage profile. This not only considers uncertainty of renewable energy resources but also provides smart generation dispatching. An efficient reliability index consisting of energy and interruption frequency terms was also defined. A novel hybrid heuristic... 

Isolated, high-density DC-DC converters play a crucial role in the power supply unit of information technology industries. By virtue of high efficiency and high power density, LLC resonant converters-among the rest of DC-DC converters-are suitable candidates for this application. The magnetic structure of this converter, however, is a bulky, dissipative part, which has a considerable portion of power loss. Improvement in the size and efficiency of the LLC transformer will lead to an overall enhancement of power density and efficiency of the converter. Nevertheless, the optimization of planar transformers for the LLC converter has not been studied enough. This paper proposes a... 

In this paper, we consider the problem of making simultaneous decisions on the location, service rate (capacity) and the price of providing service for facilities on a network. We assume that the demand for service from each node of the network follows a Poisson process. The demand is assumed to depend on both price and distance. All facilities are assumed to charge the same price and customers wishing to obtain service choose a facility according to a Multinomial Logit function. Upon arrival to a facility, customers may join the system after observing the number of people in the queue. Service time at each facility is assumed to be exponentially distributed. We first present several... 

This paper proposes a multi-item economic order quantity model with imperfect items in supply deliveries. The inspection process to classify the items is not perfect and involves two types of error: Type-I and Type-II. To cope with the uncertainty involved in real-world applications and to bring the problem closer to reality, operational constraints are assumed stochastic. The aim is to determine the optimal order and back order sizes of the items in order to achieve maximum total profit. As the proposed mathematical model is a constrained nonlinear programming, three different solution methods including an exact method named the interior-point and two novel meta-heuristics named grey wolf... 

In this paper, a multi-item economic production quantity model with fuzzy demand is developed in which shortages are backordered and the warehouse space is limited. While the demand is assumed to be a trapezoidal fuzzy number, the centroid defuzzification method is used to defuzzify fuzzy output functions. The Lagrangian relaxation procedure is first employed to solve the problem. Then, the model is extended to a constrained fuzzy integer nonlinear programming, in order to suit real-world situations. As the extended model cannot be solved in a reasonable time using exact methods, two meta-heuristic algorithms, named the genetic algorithm (GA) and the particle swarm optimisation (PSO) each... 

After a natural disaster, there is an urgent need to supply critical loads such as hospitals as soon as possible. Microgrid (MG) formation is one of the quickest ways to achieve this goal. However, in MG formation studies, there is a trade-off between maximizing the amount of restored loads and minimizing their risk of interruption due to the following aftershocks. For the former objective, the minimum number of MGs should be formed, whereas, for the latter objective, the maximum number of MGs should be configured. This paper tackles this contradictory situation by considering the failure risk of distribution feeders in its proposed optimization framework. In this paper, at first, a novel... 

Controlling networked structures has many applications in science and engineering. In this paper, we consider the problem of pinning control (pinning the dynamics into the reference state), and optimally placing the driver nodes, i.e., the nodes to which the control signal is fed. Considering the local controllability concept, a metric based on the eigenvalues of the Laplacian matrix is taken into account as a measure of controllability. We show that the proposed optimal placement strategy considerably outperforms heuristic methods including choosing hub nodes with high degree or betweenness centrality as drivers. We also study properties of optimal drivers in terms of various centrality... 

Installation of a significant number of distributed generators (DGs), besides the application of non-sinusoidal loads such as; Plug-in Hybrid Electric Vehicles (PHEVs), in the emerging smart distribution networks and the industrial plants have posed a major challenge to the existing methods of optimal transformer sizing (OTS). The harmonic currents generated in these new environments not only substantially increase the transformer load losses, but also cause abnormal winding temperature rise and hence transformer excessive loss of life. Therefore, the harmonic contents of the loads currents should be accounted in choosing the appropriate size of distribution transformers. To address this... 

Distribution network reconfiguration as a technique for reducing network losses and enhancing voltage profile has attracted attention of many researchers. In spite of impacts network reconfiguration can have on power quality indices, the potentials have not been studied enough. To fill the gap, this paper presents a method to determine optimum network configuration considering voltage profile, network losses, and total harmonic distortion (THD). The proposed method also vouches for radial structure of the network, supplying all loads, and maintaining voltages and currents within allowable bounds. Since network reconfiguration problem is a combinatorial optimization problem, a meta-heuristic... 

The investigation of plastic behavior and determining the collapse load factors are the important ingredients of every kinematical method that is employed for plastic analysis and design of frames. The determination of collapse load factors depends on many effective parameters such as the length of bays, height of stories, types of loads and plastic moments of individual members. As the number of bays and stories increases, the parameters that have to be considered make the analysis a complex and tedious task. In such a situation, the role of algorithms that can help to compute an approximate collapse load factor in a reasonable time span becomes more and more crucial. Due to their... 

In this work, we address peak power and maximum temperature in multi-core Mixed-Criticality (MC) systems. In these systems, a rise in peak power consumption may generate more heat beyond the cooling capacity. Additionally, the reliability and timeliness of MC systems may be affected due to excessive temperature. Therefore, managing peak power consumption has become imperative in multi-core MC systems. In this regard, we propose an online peak power management heuristic for multi-core MC systems. This heuristic reduces the peak power consumption of the system as much as possible during runtime by exploiting dynamic slack and Dynamic Voltage and Frequency Scaling (DVFS). Specifically, our... 

In this paper a method for enhancing the synchronizability of dynamical networks is discussed. The method considers both local structural properties, i.e. node degrees, and global properties, i.e. node and edge betweenness centrality measures. Its performance is compared with that of some other heuristic methods and the evidence for its superior behavior is discussed. As an index for the synchronizability of a dynamical network, the eigenratio of the Laplacian matrix of the connection graph is considered. As network structures, scale-free, Watts- Strogatz and random networks of different sizes and topological properties are used. Numerical calculations of the eigenratio in the networks...