Skikda, Algeria
Skikda, Algeria

The Université of 20 août 1955 of Skikda is a university located in Skikda, Algeria.The University of Skikda was created by Executive Decree No. 01/272 of 18 September 2001. She went through the following phases: . 1987 - 1998 Ecole Normale Supérieure of Technical Education . 1998 - 2001 University Center . September 18, 2001 University of Skikda . Launched August 20, 2005 and christened University August 20, 1955. Educational Organization: The University is organized into six schools: - Faculty of science. - Faculty of Technology. - Faculty of Economics, Trade and Management Science. - Faculty of Social science and Humanities. - Faculty of Arts and Languages. - Faculty of Law and Political science. Domicile: The university is domiciled in three sites: 1. El-haddaik main site is spread over 246 hectares and is home to five first faculties 2. The site Merdj-Eddib: home ENSET and the Department of Petrochemical and Process Engineering. 3. The site AZZABA: located in the city of Azzaba distant 39 km houses the Faculty of Law and Political science. Wikipedia.

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In order to satisfy the need for improved high performance materials for advanced engineering applications, intermetallic alloys have emerged as the most promising materials. Transition metal aluminides, including FeAl, NiAl and CoAl, are of great importance since these materials have not only good strength-to-weight ratio but also excellent corrosion and oxidation resistance. A lot of processing techniques were developed in order to synthesize these materials, such as mechanical alloying or high-energy ball milling. Indeed, mechanical alloying (MA) is one of the methods of producing homogeneous, fine-grained alloys in the solid state. The importance of this processing route lies on the possible ductilization of these brittle intermetallics by grain refinement and/or by the introduction of disorder. The aim of this review is to update and to offer an assessment of the investigations being carried out on B2 TM-Al (TM = Fe, Ni, Co) intermetallic alloys and to highlight their formation and disordering by high energy ball milling. © 2017 Elsevier B.V.

Boucheham B.,Skikda University
International Journal of Machine Learning and Cybernetics | Year: 2013

We propose a novel method (FANSEA) that performs very complex time series matching. The matching here includes comparison and alignment of time series, for diverse needs: diagnosis, clustering, retrieval, mining, etc. The complexity stands in the fact that the method is able to match quasi-periodic time series, that are eventually phase shifted, of different lengths, composed of different number of periods, characterized by local morphological changes and that might be shifted/scaled on the time/magnitude axis. This is the most complex case that can occur in time series matching. The efficiency stands in the fact that the newly developed FANSEA method produces alignments that are comparable to those of the previously published SEA method. However and as a result of data reduction, FANSEA consumes much less time and data; hence, allowing for faster matching and lower storage space. Basically, FANSEA is composed of two main steps: Data reduction by curve simplification of the time series traces and matching through exchange of extracted signatures between the time series under process. Due to the quasi-periodic nature of the electrocardiogram (ECG), the tests were conducted on records selected from the Massachusetts Institute of Technology-Beth Israel Hospital database (MIT-BIH). Numerically, the new method data reduction was up to 80 % and the time reduction was up to 95 %. Accordingly and among many possible applications, the new method is very suitable for searching, querying and mining of large time series databases. © 2012 Springer-Verlag.

In this study, a comparison between generalized regression neural network (GRNN) and multiple linear regression (MLR) models is given on the effectiveness of modelling dissolved oxygen (DO) concentration in a river. The two models are developed using hourly experimental data collected from the United States Geological Survey (USGS Station No: 421209121463000 [top]) station at the Klamath River at Railroad Bridge at Lake Ewauna. The input variables used for the two models are water, pH, temperature, electrical conductivity, and sensor depth. The performances of the models are evaluated using root mean square errors (RMSE), the mean absolute error (MAE), Willmott's index of agreement (d), and correlation coefficient (CC) statistics. Of the two approaches employed, the best fit was obtained using the GRNN model with the four input variables used. © 2014 Taylor & Francis.

In this study, we present application of an artificial intelligence (AI) technique model called dynamic evolving neural-fuzzy inference system (DENFIS) based on an evolving clustering method (ECM), for modelling dissolved oxygen concentration in a river. To demonstrate the forecasting capability of DENFIS, a one year period from 1 January 2009 to 30 December 2009, of hourly experimental water quality data collected by the United States Geological Survey (USGS Station No: 420853121505500) station at Klamath River at Miller Island Boat Ramp, OR, USA, were used for model development. Two DENFIS-based models are presented and compared. The two DENFIS systems are: (1) offline-based system named DENFIS-OF, and (2) online-based system, named DENFIS-ON. The input variables used for the two models are water pH, temperature, specific conductance, and sensor depth. The performances of the models are evaluated using root mean square errors (RMSE), mean absolute error (MAE), Willmott index of agreement (d) and correlation coefficient (CC) statistics. The lowest root mean square error and highest correlation coefficient values were obtained with the DENFIS-ON method. The results obtained with DENFIS models are compared with linear (multiple linear regression, MLR) and nonlinear (multi-layer perceptron neural networks, MLPNN) methods. This study demonstrates that DENFIS-ON investigated herein outperforms all the proposed techniques for DO modelling. © 2014 Springer-Verlag Berlin Heidelberg.

Ferdi Y.,Skikda University
Journal of Mechanics in Medicine and Biology | Year: 2012

The goal of this paper is to describe some applications of fractional order calculus to biomedical signal processing with emphasis on the ability of this mathematical tool to remove noise, enhance useful information, and generate fractal signals. Three types of digital filters are considered, namely, lowpass differentiation filter, smoothing filter, and 1/f β-noise generation filter. The filter impulse responses are functions of the fractional order and the sampling period only, and thus can be computed easily. Application examples are presented for illustrations. © World Scientific Publishing Company.

Khelili F.,Skikda University
Physical Review D - Particles, Fields, Gravitation and Cosmology | Year: 2012

Using the noncommutative deformed canonical commutation relations proposed by Carmona et al., a model describing the dynamics of the noncommutative complex scalar field is proposed. The noncommutative field equations are solved, and the vacuum energy is calculated to the second order in the parameter of noncommutativity. As an application to this model, the Casimir effect, due to the zero-point fluctuations of the noncommutative complex scalar field, is considered. It turns out that in spite of its smallness, the noncommutativity gives rise to a repulsive force at the microscopic level, leading to a modified Casimir potential with a minimum at the point a min= √584πθ. © 2012 American Physical Society.

A modified Monte Carlo Potts model for grain growth simulation with the preferential influence of particles on grain boundaries is presented. The presence of particles can influence the grains growth by pinning their boundaries. This pinning effect occurs in a selective way in textured materials such as magnetic sheets Fe-3%Si. Owing to its simplicity, Monte Carlo Potts (MCP) model for grain growth simulation has been applied with sum success for various materials. Unfortunately, one cannot introduce the preferential pinning of particles on grain boundaries directly in the MCP simulation procedure. The proposed model provides a tool for studying the preferential effect of particles on grain boundaries. It is able to simulate the abnormal growth of Goss grains observed in the Fe-3%Si magnetic alloys. © 2013 Elsevier B.V. All rights reserved.

Boucheham B.,Skikda University
Pattern Recognition Letters | Year: 2010

We propose a similarity-based matching technique for the purpose of quasi-periodic time series patterns alignment. The method is based on combination of two previously published works: a modified version of the Douglas-Peucker line simplification algorithm (DPSimp) for data reduction in time series, and SEA for pattern matching of quasi-periodic time series. The previously developed SEA method was shown to be more efficient than the very popular DTW technique. The aim of the obtained ASEAL method (Approximate Shape Exchange ALgorithm) is reduction of the space and time necessary to accomplish alignments comparable to those of the SEA method. The study shows the effectiveness of the proposed ASEAL method on ECG signals taken from the Massachusetts Institute of Technology - Beth Israel Hospital (MIT-BIH) database in terms of the correlation factor and alignment quality, for savings up to 90% in used samples and processing time reduction up to 97% with respect to those of SEA. Particularly, the method is able to deal with very complex alignment situations (magnitude/time axis shift/scaling, local variabilities, difference in length, phase shift, arbitrary number of periods) in the context of quasi-periodic time series. Among other possible applications, the proposed ASEAL method is a novel step toward resolution of the 'person identification using ECG' problem. © 2009 Elsevier B.V. All rights reserved.

This article presents a comparison of two adaptive neuro-fuzzy inference systems (ANFIS)-based neuro-fuzzy models applied for modeling dissolved oxygen (DO) concentration. The two models are developed using experimental data collected from the bottom (USGS station no: 420615121533601) and top (USGS station no: 420615121533600) stations at Klamath River at site KRS12a nr Rock Quarry, Oregon, USA. The input variables used for the ANFIS models are water pH, temperature, specific conductance, and sensor depth. Two ANFIS-based neuro-fuzzy systems are presented. The two neuro-fuzzy systems are: (1) grid partition-based fuzzy inference system, named ANFIS-GRID, and (2) subtractive-clustering-based fuzzy inference system, named ANFIS-SUB. In both models, 60 % of the data set was randomly assigned to the training set, 20 % to the validation set, and 20 % to the test set. The ANFIS results are compared with multiple linear regression models. The system proposed in this paper shows a novelty approach with regard to the usage of ANFIS models for DO concentration modeling. © 2013 Springer Science+Business Media Dordrecht.

Lebaroud A.,Skikda University | Medoued A.,Skikda University
International Journal of Electrical Power and Energy Systems | Year: 2013

This paper presents online components calculation techniques for stator and rotor of the induction machine. Four techniques have been developed for online components calculation; the first one starts the calculation of the negative component of stator current space vector using the Discrete Fourier Transform (DFT) in order to detect the stator fault. The second technique is dealing with the detection of rotor fault by the Recursive Fourier Transform (RFT), This technique improves the signal acquisition and enhanced detection of components near the fundamental. The third technique allows improving of the rotor fault detection by the spectrum of analytical signal. The fourth and the last technique is the frequency analysis of the instantaneous power, which allows obtaining a singular signature of faults. These techniques have shown better detection, where each fault is characterized by a singular signature and therefore they improve the detection and diagnosis of faults. Experimental results applied on an asynchronous machine 5.5 kW, approve and validate these calculation techniques. © 2012 Elsevier Ltd. All rights reserved.

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