Electric load forecasting by using dynamic neural network

Electrical energy is fundamental for the wellbeing and for the economic development of any country. However, all countries must ensure access to essential resources and ensure the continuity of its supply. Due to the non-storable nature of electrical energy, the amount of consumed active power should always be equal the produced active power just to avoid power system frequency deviation problem. In order to keep the relationship production–consumption relation in compliance with different standards and to secure profitable operations of power system, electric load consumption must be predicted and controlled instantaneously. Several statistical and classical techniques are proposed in the literature but unfortunately all these methods are not accurate in a satisfactory manner. In this paper, a dynamic neural network is used for the prediction of daily power consumption. The suitability and the performance of the proposed approach is illustrated and verified with simulations on load data collected from French Transmission System Operator (RTE) website. The obtained results show that the accuracy and the efficiency are improved comparatively to conventional methods widely used in this field of research.     

Wavelet network for recognition system of Arabic word

Focusing on the development of new technologies of information, research in the speech communication field is an activity in full expansion. Several disciplines and skills interact in order to improve performance of Human Machine Communication Systems (HMC). In order to increase the performance of these systems, various techniques, including Hidden Markov Models (HMM) and Neural Network (NN), are implemented.     

Effect of seed coat fragments on cotton yarn structure: impact of fragment size,attached fibres and position within yarn core

Seed coat fragments are a major source of cotton yarn imperfections. This article discusses the factors influencing the disruption caused by seed coat fragments in ring-spun yarn structure with a focus on three characteristics: the fragment's size, the amount of fibres attached to it and its position relative to the yarn core. The three characteristics were investigated through two experiments involving various types of yarn defects and a range of ring-spun yarn counts. Results presented in this article indicate that the three factors considered have a significant influence on the intensity of the disruption the seed coat fragment causes in the yarn structure, and are determinant of the type of defect that this disruption engenders (short/long defect).     

Iron oxide (n-Fe2O3) nanowire films and carbon modified (CM)-n-Fe2O3 thin films for hydrogen production by photosplitting of water

Iron oxide n-Fe₂O₃ nanowire photoelectrodes were synthesized by thermal oxidation of Fe metal sheet (Alfa Co. 0.25 mm thick) in an electric oven then tested for their photoactivity. The photoresponse of the n-Fe₂O₃ nanowires was evaluated by measuring the rate of water splitting reaction to hydrogen and oxygen, which is proportional to photocurrent density, J_p. The optimized electric oven-made n-Fe₂O₃ nanowire photoelectrodes showed photocurrent densities of 1.46 mA cm⁻² at measured potential of 0.1 V/SCE at illumination intensity of 100 mW cm⁻² from a Solar simulator with a global AM 1.5 filter. For the optimized carbon modified (CM)-n-TiO₂ synthesized by thermal flame oxidation the photocurrent density for water splitting was found to increase by two fold to 3.0 mA cm⁻² measured at the same measured potential and the illumination intensity. The carbon modified (CM)-n-Fe₂O₃ electrode showed a shift of the open circuit potential by −100 mV/SCE compared to undoped n-Fe₂O₃ nanowires. A maximum photoconversion efficiency of 2.3% at applied potential of 0.5 V/E_aoc was found for CM-n-Fe₂O₃ compared to 1.69% for n-Fe₂O₃ nanowires at higher applied potential of 0.7 V/E_aoc. These CM-n- Fe₂O₃ and n- Fe₂O₃ nanowires thin films were characterized using photocurrent density measurements under monochromatic light illumination, UV-Vis spectra, X-ray diffraction (XRD) and scanning electron microscopy (SEM).     

Doubly-striped disk mirroring: Reliable storage for video servers

Disk striping is essential in Video-On-Demand servers to allow concurrent access for a large number of users to the same copy of a video object. On the other hand, disk striping makes the system more vulnerable to component failures. We propose a replication scheme for video content and an associated I/O scheduling algorithm that enables the system to tolerate a component failure. The scheme, called doubly-striped mirrors, compares favorably to other approaches for implementing redundancy such as RAID in terms of cost, bandwidth efficiency, buffer usage and system availability.     

A MC-CDMA Mobile Communication System Employing Pre-Rake and Transmit Diversity

In this paper, a multi-carrier code division multiple access (MC-CDMA) downlink mobile communication system employing pre-rake and dual transmit diversity is proposed. It combines high spectral efficiency with an immunity to channel dispersion and fading. It also ensures small size, cost and power consumption of the terminal. Theoretical and simulation results for the system under consideration are obtained. Depicted results show appreciable improvements of the proposed system over those previously known.Emad K. Al-Hussaini received his B.Sc degree in Electrical Communication Engineering from Ain-Shams University, Cairo, Egypt, in 1964 and his M.Sc and Ph.D. degrees from Cairo University, Giza, Egypt, in 1974 and 1977, respectively. From 1964 to 1970, he was with the General Egyptian Aeroorganization. Since 1970, he has been with the Department of Electronics and Communications, Faculty of Engineering, Cairo University, and is currently professor there. He was a research fellow at Imperial College, London, UK, and at the Moore School of Electrical Engineering, University of Pennsylvania, Philadelphia, PA, USA, in the academic years 1976/1977 and 1981/1982, respectively. In 1990, he received the Egyptian national encouragement award for outstanding engineering research. He has written several papers for technical international journals and conferences. His research interests include signal processing, fading channel communication, modulation, and cellular mobile radio systems. Dr Al-Hussaini is a senior member of IEEE. He is listed in Marquis Whos Who in the World and in the IBC (International Biographical Center, Cambridge) for outstanding people of the 20th century.Hebat-Allah M.Mourad received her B.Sc, M.Sc and Ph.D degrees in Electrical Communication Engineering from Cairo University, Egypt, in 1983, 1987 and 1994 respectively. Since 1983 she has been with the Electronics and Communications Department, Faculty of Engineering Cairo University and is currently associate professor there. Her research interests include mobile communications, satellite communications and optical fiber communications.Fatma A. Newagy received her B.Sc and M.Sc degrees in electrical communication engineering from Cairo University, Egypt in 1998 and 2002 respectively. Since 1999, she has been a research assistant with the Department of Electronics and Communications, Faculty of Engineering, Cairo University. She is pursuing her Ph.D. there. Her research interests include mobile communications and modulation techniques for spread spectrum and wireless communications.     

Unsupervised Bayesian image segmentation using orthogonal series

This paper deals with the problem of unsupervised image segmentation which consists in first mixture identification phase and second a Bayesian decision phase. During the mixture identification phase, the conditional probability density function (pdf) and the a priori class probabilities must be estimated. The most difficult part is the estimation of the number of pixel classes or in other words the estimation of the number of density mixture components. To resolve this problem, we propose here a Stochastic and Nonparametric Expectation-Maximization (SNEM) algorithm. The algorithm finds the most likely number of classes, their associated model parameters and generates a segmentation of the image by classifying the pixels into these classes. The non-parametric aspect comes from the use of the orthogonal series estimator. Experimental results are promising, we have obtained accurate results on a variety of real images.     

Synergistic verification and validation of systems and software engineering models

In this paper, we present a unified approach for the verification and validation of software and systems engineering design models expressed in UML 2.0 and SysML 1.0. The approach is based on three well-established techniques, namely formal analysis, programme analysis and software engineering (SwE) techniques. More precisely, our contribution consists of the synergistic combination of model checking, static analysis and SwE metrics that enables the automatic and efficient assessment of design models from static and dynamic perspectives. Additionally, we present the design and implementation of an automated computer-aided assessing framework integrating the proposed approach. Moreover, we discuss the related technical details and the underlying synergism. Finally, we illustrate the proposed approach by assessing a design case study that is composed of state machine and sequence diagrams.     

An evolutionary algorithm for generalized comparison-based self-diagnosis of multiprocessor systems

In this article, we consider the problem of self-diagnosis of multiprocessor and multicomputer systems under the generalized comparison model. In this approach, a system consists of a collection n independent heterogeneous processors (or units) interconnected via point-to-point communication links, and it is assumed that at most t of these processors are permanently faulty. For the purpose of diagnosis, system tasks are assigned to pairs of processors and the results are compared. The agreements and disagreements among units are the basis for identifying faulty processors. Such a system is said to be t-diagnosable if, given any complete collection of comparison results, the set of faulty processors can be unambiguously identified. We present an efficient fault identification method based on genetic algorithms. Analysis and simulations are provided, first, to evaluate the genetic parameters of the diagnosis algorithm; second, to show the efficiency of the genetic approach. The new strategy is shown to correctly identify the set of faulty processors, making it an attractive and viable addition or alternative to present fault diagnosis techniques.     

VANET QoS-OLSR: QoS-based clustering protocol for Vehicular Ad hoc Networks

In this paper, we address the problem of clustering in Vehicular Ad hoc Networks (VANETs) using Quality of Service Optimized Link State Routing (QoS-OLSR) protocol. Several clustering algorithms have been proposed for VANET and MANET. However, the mobility-based algorithms ignore the Quality of Service requirements that are important for VANET safety, emergency, and multimedia services while the QoS-based algorithms ignore the high speed mobility constraints since they are dedicated for Mobile Ad hoc Networks (MANETs). Our solution is a new QoS-based clustering algorithm that considers a tradeoff between QoS requirements and high speed mobility constraints. The goal is to form stable clusters and maintain the stability during communications and link failures while satisfying the Quality of Service requirements. This is achieved by: (1) considering the high mobility metrics while computing the QoS, (2) using Ant Colony Optimization for MPRs selection, and (3) using MPR recovery algorithm able to select alternatives and keep the network connected in case of link failures. Performance analysis and simulation results show that the proposed model can maintain the network stability, reduce the end-to-end delay, increase the packet delivery ratio, and reduce the communications overhead.