Amira Y. Haikal

Reliable and efficient hierarchical organization model for computational grid

Abstract Although the hierarchical model appears to be an effective solution to organize the resource managements in grid systems which have more stringent demand for both scalability and efficiency, it has some limitations which need to be addressed. For example, the master/manager resources in different levels represent single points of failure and they may be sources of bottleneck and communication overhead especially if they are not efficiently selected. Moreover, the dynamic and fault-prone nature of grids cannot be treated by static structures while the manual construction and repairing are also prohibitive due to the highly caused overhead which often represents a significant obstruction to an efficient resource utilization (especially for those with intermittent availabilities). The main objective of this paper is to first introduce a self-repairing n-try dynamic hierarchical grid model for scheduling and load balancing in which each master resource will be replicated on one of its children resources. Second, an efficient methodology to elect masters–replicas resources is proposed. In this methodology, the masters–replicas are selected based on both resource reliability (in terms of MTBF) and resource proximity from the other nodes in specified groups (in terms of communication latency). Validation of the proposed methodology based on the proposed model is done via simulation. Experimental results show that the proposed model has a great impact on the overall performance. Compared to other approaches, the simulations show that our approach decrements the average completion time ( A C T ) by 18.9%–25%, increases the tree stability ratio ( T S R ) up to 26.2%–27.1%, and minimizes the total communication overhead ( T C O ) by 4.4%–18.7% in the range of system parameter values examined.

Towards Internet QoS Provisioning Based on Generic Distributed QoS Adaptive Routing Engine

Increasing efficiency and quality demands of modern Internet technologies drive todays network engineers to seek to provide quality of service (QoS). Internet QoS provisioning gives rise to several challenging issues. This paper introduces a generic distributed QoS adaptive routing engine (DQARE) architecture based on OSPFxQoS. The innovation of the proposed work in this paper is its undependability on the used QoS architectures and, moreover, splitting of the control strategy from data forwarding mechanisms, so we guarantee a set of absolute stable mechanisms on top of which Internet QoS can be built. DQARE architecture is furnished with three relevant traffic control schemes, namely, service differentiation, QoS routing, and traffic engineering. The main objective of this paper is to (i) provide a general configuration guideline for service differentiation, (ii) formalize the theoretical properties of different QoS routing algorithms and then introduce a QoS routing algorithm (QOPRA) based on dynamic programming technique, and (iii) propose QoS multipath forwarding (QMPF) model for paths diversity exploitation. NS2-based simulations proved the DQARE superiority in terms of delay, packet delivery ratio, throughput, and control overhead. Moreover, extensive simulations are used to compare the proposed QOPRA algorithm and QMPF model with their counterparts in the literature.

Adaptive Control for Torque Ripple Minimization in PM Synchronous motors

paper presents two methods for designing special purpose controllers for permanent magnet synchronous motor. The main target of the designed controllers is to reduce torque ripples of this type of motors. The first proposed adaptive method is based on two loop controllers (current controller and speed controller) in addition to using space vector pulse width modulation to maximize fundamental component of torque. The second proposed method is based on PI current controllers enabling tracking of quadrature current command values. Simulation results of the suggested adaptive controller are compared with that of the PI controller. Comparative analysis proves the effectiveness of the suggested adaptive controller than the classical PI one according to ripple reduction as well as dynamic response. Moreover, the suggested adaptive controller when compared with other controllers shows great success in torque ripples reduction, enabling speed tracking while minimizing the torque ripple. Keywordsreference adaptive system, PM synchronous motor, torque control.

Artificial Neural Network Control of Permanent Magnet Synchronous Motor

paper presents a neural network controller for permanent magnet synchronous motor (PMSM). The neural controller is used for torque ripple minimization of this type of motors. Two methods of neural controller design are used. The first method is based on two loop controllers (current controller and speed controller). The second method is based on estimation of torque constant and stator resistance in PMSM. The q-axis inductance is modeled off-line according to q-axis stator current. The neural weights are initially chosen small randomly and a model reference control algorithm adjusts those weights to give the optimal values. The neural network parameter estimator has been applied to flux linkage torque ripple minimization of the PMSM. Simulation results using the two methods are compared together. Moreover, the suggested algorithms when compared with other controllers show great success in torque ripples reduction. Keywordsnetwork, PM synchronous motor, torque control, ripple minimization, reference model.

A new ABC variant for solving inverse kinematics problem in 5 DOF robot arm

Abstract While the outcomes of artificial bee colony (ABC) have been encouraging enough, ABC algorithm lacks good compromise between exploration and exploitation. The main aim of this paper is to propose ABC-based algorithm; namely knowledge-based artificial bee colony (K-ABC), that is able to converge quickly and explore the most promising area of the intended search space. To be sure that the K-ABC algorithm is trustworthy and reliable, it is tested against the most recent well-known benchmarks CEC’2017. On top of that, the K-ABC wins against their counterpart on the inverse kinematic problem for a 5 DOF robot arm and some other complex constrained engineering problems. The effect of the limit parameter is investigated as well. Experimental results show that K-ABC has a mean squared error 60% less than standard ABC for the inverse kinematic solution.