Scientia Iranica
Volume:23 Issue: 3, 2016

Cloud workflow is a special type of cloud computing systems which mainly concentrates on workflow management. One of the major issues with cloud workflow systems is automatic multi-cloud workflow management. This paper proposes a service oriented framework for cloud workflow management which integrates heterogeneous multi-cloud platforms to provide integrated applications for users by minimizing the human intervention as far as possible. The proposed framework involves a language to define some basic entities for environments and uses these definitions to integrate applications and services in a cloud workflow. This framework has been already implemented. In addition, its main operations have been evaluated by a case study and the results show that the framework works properly as a cloud integrator, and the framework main activities are done automatically with a reasonable performance.

Dynamic memory management is an important and essential part of computer systems design. Efficient memory allocation, garbage collection, and compaction are becoming critical in parallel and distributed applications using object oriented languages like C and Java. In addition to achieving fast allocation/de-allocation of memory objects and fragmentation, memory management techniques should strive to improve the overall execution performance of object oriented applications. In this paper, we introduce Address Ordered and Segregated Binary Trees, two memory management techniques particularly efficient for object oriented applications. Our empirical results manifest that both ABT and SBT when accompanied by coalescing outperform existing allocators such as Segregated free lists in terms of storage utilization and execution performance. We also show that these new allocators perform well in terms of storage utilization, even without coalescing. This is in particular suitable for web-applications.

Given a set P of red points and a set Q of blue points in the plane of total size n, we investigate the problem of finding two disjoint isothetic rectangles containing all the points of Q avoiding any points of P. Such rectangles are called two separating disjoint isothetic rectangles. We fi rst compute two separating disjoint axis-aligned rectangles in O(n log n) time. Then, we relax the axis-aligned constraint and report all combinatorially di erent two separating disjoint isothetic rectangles. To compute these rectangles, we introduce some events by rotating the coordinate system and process these events. Computing and processing all of the events are done in O(n^2 log n) time. Thus, our algorithm reports all combinatorially di erent separating rectangles in O(n^2 log n) time.

In this paper, we propose a transductive transfer learning framework, referred to as Transfer Maximum Margin Criterion (T-MMC). This framework is suitable to transfer the knowledge acquired in one domain, the source domain, to another domain, the target domain, where no labeled examples are available in the target domain. We introduce an e ective feature weighting approach, which proceeds to reduce the domain di erence between the source and target domains. Moreover, we exploit maximum margin criterion to well discriminate various classes in the reduced domains. We simultaneously transfer knowledge from the source domain to target domain and also discriminate various classes in the reduced domains. Comprehensive experiments on the synthetic and real datasets demonstrate that T-MMC outperforms existing transfer learning methods.

Magnetic gears (MGs) offer significant potential advantages relative to conventional mechanical gears, such as: no contact, no friction, no lubrication, inherent overload protection and free from noise and vibration. Despite of these superior features they have received little attention due to complexity of operation and relatively low torque density transmission. This paper introduces a new magnetic gear topology with a highly competitive torque density transmission capability. This configuration is inspired of combination of the traditional radial and axial flux MGs. The assurance of accuracy based on a basic design of axial flux magnetic gear dimensions leading to Proof-of-Concept “Arcuate Double-Sided MG”. The Proof-of-Concept seeks to provide a better solution to the problems of flux maldistribution and concentration compared to what has been proposed previously. Thus, torque density performance of this MG is intensively augmented. The cogging torque is also computed in further simulations. However, the above requires advanced numerical techniques and hence 3-Dimensional simulations for calculating localized flux density and the corresponding torque density. Completion of this development will mark an important milestone in magnetic gear technology, and significant performance improvement will be realized.

The optimization algorithm for High-Temperature Superconducting (HTS) Fault Current Limiters (FCL) normally includes some non-commensurable criteria or objectives such as cost, limiting factor, thermal stresses, and mechanical stresses that should be minimized or maximized concurrently in a protracted optimization process. It can be performed only by tools which rstly describe the HTSFCL precisely and predict its limitation behavior and then select the optimum design using optimization algorithm. Multi-Objective Decision Making (MODM) is one of the most widely used decision techniques in the business and engineering worlds. In the MODM problems, there are several objectives of the system concurrently optimized, and a solution set, i.e. the Pareto front, is usually obtained instead of a real optimal solution. This paper explains in details the combination models of HTSFCL as a component in PSCAD/EMTDC and presents the optimization algorithm based on a new approach of normalized multi-objective simulated annealing.

This paper presents a proposed method to search maximum power point (MPP) based on the adaptive fuzzy logic control (AFLC) which is applied to photovoltaic (PV) systems under varying temperatures and radiations. The proposed system is composed of boost converter, two fuzzy controllers and load. Whenever environmental conditions change in wide range, using only conventional fuzzy logic controller (CFLC) is not adequate and causes more errors in tracking. The proposed AFLC comprises two stages: Online and Offline tuning. The offline method by accurately setting CFLC controller parameters is applied for relatively stable atmospheric conditions. Meanwhile, the online method is considered for unstable atmospheric conditions and contains two fuzzy controllers-one primary, one secondary. The primary fuzzy controller is the CFLC, and the secondary controller is the decision-making, which due to atmospheric conditions, alters the primary fuzzy controller parameters in order to achieve a better answer compared to utilizing CFLC. Decision making controller with changing in irradiation and temperature changes gain of inputs of CFLC simultaneously that it increases rate and accuracy of tracking in comparison with using only fuzzy controller. By simulating results using CFLC and AFLC controllers, the proposed method is able to improved performance indicators with respect to CFLC.

Wind power generations as a renewable source of energy have become under the spotlight of policy markets and energy providers recently. However, the technical constraints, production variability and uncertainty of wind power have limited its extensive integration into power system. Having these limitations in mind, the present paper sets for a new methodology for increasing wind power integration in deregulated electricity market. The proposed method is presented with the consideration of voltage stability assessment and wind power uncertainties. Our plan aims at maximizing social welfare and minimizing investment and annual operation cost. At the same time, we seek to overcome line constraint by taking into account wind uncertainties. In order to fulfill the aims, FACTS devices and price sensitive loads are utilized. On the other hand, a multi-objective optimization problem was used to evaluate the annual costs and outcomes of wind power investment, the optimal setting of FACTS devices, and optimal load shedding. Expected Security Cost Optimal Power Flow (ESCOPF) is used to minimize the expected total cost of system operation. To solve the proposed planning problem, Non Dominated Sorting Genetic Algorithm (NSGA-II) is exploited. Our proposed method has been applied to an IEEE30 bus test system by considering two wind scenarios. The simulation results demonstrate the efficiency of the proposed planning.

This paper reports a new realization of linear Voltage-Controlled Oscillator (VCO) using three Current-Feedback Op-Amps (CFOAs), three analog multipliers, and only four passive components. Thus, minimum numbers of passive components are used to devise a canonic sinusoidal oscillator. The circuit provides non-interactive electronic tuning of both the Condition of Oscillation (CO) and the Frequency of Oscillation (FO) via the gains of two di erent analog multipliers. The impedance at output node exhibits low impedance which is easy to directly connect to load without any additional voltage bu er. The proposed circuit has been simulated in SPICE using macro-model of AD844 CFOA ICs and AD633 multiplier ICs.

Estimation of an optimal controller is a fundamental problem in control engineering and is widely known as Optimization. Numerous computation and numerical techniques have been evolved during the past years for the estimation of the optimal solution. Optimal control of a discrete-time system is concerned with optimizing a given objective function using “Homogenous Polynomial Lyapunov Function (HPLF)”. This research focuses upon the design of optimal guaranteed cost controller (GCC) for discrete-time uncertain system using HPLF. The uncertainties are assumed to be norm bounded uncertainties. The effect of actuator saturation is also incorporated in the system. Sufficient conditions for the existence of HPLF are derived in term of Linear Matrix Inequalities (LMI). The LMI approach has the advantage that it can be solved efficiently using Convex Optimization. LMI’s combined with HPLF helps to design the guaranteed cost controller which minimizes the cost by minimizing cost function. Furthermore, the state trajectories and the invariant set is also shown to observe the overall performance.

Transmission Expansion Planning (TEP) problem refers to proposing the optimal number and location of new transmission lines in order to satisfy operation conditions with less investment costs. Adequacy evaluation of trial solutions is essential to solve TEP problem. This evaluation procedure may require a considerable computational e ort; therefore, improvement of Adequacy Evaluation Methods (AEMs) is a key to achieve more ecient TEP solution algorithms. It is a common practice to employ a Linear Programming (LP) called DC operation model" to perform this evaluation, especially in cases that generation rescheduling is considered in TEP studies. In this paper, an AEM is proposed as an alternative with less computational e ort to perform adequacy evaluation. In fact, the proposed AEM is applicable in TEP problems considering generation rescheduling. The study is based on DC TEP model where electrical aspect of the problem is modeled based on DC power flow equations. To examine the performance of the proposed AEM, this evaluation method is employed in a meta-heuristic TEP solution algorithm. Such a combinatorial algorithm is then applied to di erent standard test systems as well as practical cases. The simulation results show a promising improvement caused by employment of the proposed AEM.

This paper presents eight improved particle swarm optimization (IPSO) algorithms for solving combined heat and power economic dispatch (CHPED) problem with valve point loading effects on fuel cost function of pure power units and power losses in transmission system. The main objective of the problem is to find the power output of the pure power units, the heat output of the pure heat units and both power and heat outputs of cogeneration units so that the total fuel cost is minimized while satisfying power and heat demands and power and heat limits. The proposed IPSO algorithms are based on some modifications on the parameters and the use of Cauchy distribution. The effectiveness of the proposed IPSO methods have been validated through five different test systems including three systems with quadratic fuel cost function of pure power units neglecting transmission losses, and two systems with nonconvex fuel cost function of pure power units considering transmission losses. The result comparisons between the proposed IPSO methods and other methods in the literature have indicated that the proposed methods can obtain higher solution quality with faster computational time than many other methods. Therefore, the proposed IPSO methods would be very efficient methods for solving the nonconvex CHPED problem.

Considerable area occupied by capacitors is one of the main issues in the design of many ICs, especially in biomedical applications. MIM capacitor can be replaced by the MOSCAP to reduce the chip area and cost. Although the MOSCAP shows a non-linear behavior, linearization can be performed to some extent using serial and parallel compensation. In this paper, a new approach is presented, by which the impact of MOSCAP non-linearity on the THD of the circuit is reduced. The proposed technique is used in an electrocardiogram ampli er. The appropriate structure for each MOSCAP of the ampli er is selected by analyzing the non-linear e ect of the MOSCAPs on the ampli er's output linearity. The non-linear e ect of MOSCAP is reduced by choosing the appropriate slopes in the MOSCAP capacitance-voltage curve. As a result, the occupied area of the ampli er is reduced to less than 10 percent of the area of the ampli er with MIM capacitors, while the THD is not changed considerably. The output THD is 0.65% at 60 Hz and the total power consumption is 72 nW.