Publications Internationales

Permanent URI for this collectionhttps://dspace.univ-boumerdes.dz/handle/123456789/13

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Author: search.filters.author.Baddari, Kamel

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Now showing 1 - 10 of 17
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    Distributed clutter-map constant false alarm rate detection using fuzzy fusion rules
    (Springer, 2019) Bouchelaghem, Houssameddine; Hamadouche, M'hamed; Soltani, Faouzi; Baddari, Kamel
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    A novel method to forecast 24 h of global solar irradiation
    (Springer, 2017) Saoud, L. Saad; Rahmoune, F.; Tourtchine, V.; Baddari, Kamel
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    Influence of PVP content on degradation of PES/PVP membranes : insights from characterization of membranes with controlled composition
    (Elsevier, 2017) Kourde-Hanafi, Yamina; Loulergue, Patrick; Szymczyk, Anthony; Van der Bruggen, Bart; Nachtnebel, Manfred; Rabiller-Baudry, Murielle; Audic, Jean-Luc; Pölt, Peter; Baddari, Kamel
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    Impulse noise reduction in 2D electrical resistivity imaging data based on fuzzy logic
    (IEEE, 2011) Ferahtia, J.; Djarfour, Noureddine; Baddari, Kamel; Khaldoun, Asmae
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    Electrokinetic analysis of PES/PVP membranes aged by sodium hypochlorite solutions at different pH
    (Elsevier, 2016) Hanafi, Yamina; Loulergue, Patrick; Ababou-Girard, Soraya; Meriadec, Cristelle; Rabiller-Baudry, Murielle; Baddari, Kamel; Szymczyk, Anthony
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    Probabilistic model to forecast earthquakes in the Zemmouri (Algeria) seismoactive area on the basis of moment magnitude scale distribution functions
    (2013) Baddari, Kamel; Makdeche, Said; Bellalem, Fouzi
    Based on the moment magnitude scale, a probabilistic model was developed to predict the occurrences of strong earthquakes in the seismoactive area of Zemmouri, Algeria. Firstly, the distributions of earthquake magnitudes M i were described using the distribution function F 0(m), which adjusts the magnitudes considered as independent random variables. Secondly, the obtained result, i.e., the distribution function F 0(m) of the variables M i was used to deduce the distribution functions G(x) and H(y) of the variables Y i = Log M 0,i and Z i = M 0,i , where (Y i)i and (Z i)i are independent. Thirdly, some forecast for moments of the future earthquakes in the studied area is given
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    Generalized dynamical fuzzy model for identification and prediction
    (2014) Saad Saoud, Lyes; Rahmoune, Fayçal; Tourtchine, Victor; Baddari, Kamel
    In this paper, the development of an improved Takagi Sugeno (TS) fuzzy model for identification and chaotic time series prediction of nonlinear dynamical systems is proposed. This model combines the advantages of fuzzy systems and Infinite Impulse Response (IIR) filters, which are autoregressive moving average models, to create internal dynamics with just the control input. The structure of Fuzzy Infinite Impulse Response (FIIR) is presented, and its learning algorithm is described. In the proposed model, the Butterworth analogue prototype filters are estimated using the obtained membership functions. Based on the founding orders of the analogue filters, the IIR filters could be constructed. The IIR filters are introduced to each TS fuzzy rule which produces local dynamics. Gustafson-Kessel (GK) clustering algorithm is used to generate the clusters which will be used to find the number of the IIR parameters for each rule. The hybrid genetic algorithm and simplex method are used to identify the consequence parameters. The stability of the obtained model is studied. To demonstrate the performance of this modeling method, three examples have been chosen. Comparative results between the FIIR model on one hand, and the traditional TS fuzzy model, the neural networks and the neuro-fuzzy network on the other hand. The results show that the proposed method provides promising identification results
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    Effect of stress-strain conditions on physical precursors and failure stages development in rock samples
    (2015) Baddari, Kamel; Frolov, Anatoly D.; Tourtchine, Victor; Rahmoune, Fayçal; Makdeche, Said
    Precursory stages of failure development in large rock samples were studied and simultaneous observations of the space-time variation of several physical fields were carried out under different stress-strain states. The failure process was studied in detail. A hierarchical structure of discreet rock medium was obtained after loading. It was found that the moisture reduced the rock strength, increased the microcrack distribution and influenced the shape of the failure physical precursors. The rise in temperature up to 400 °C affected the physical precursors at the intermediate and final stages of the failure. Significant variations were detected in the acoustic and electromagnetic emissions. The coalescence criterion was slightly depending on the rock moisture and temperature effect. The possibility of identifying the precursory stage of failure at different strain conditions by means of a complex parameter derived from the convolution of physical recorded data is shown. The obtained results point out the efficiency of the laboratory modelling of seismic processes
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    Seismic noise filtering based on Generalized Regression Neural Networks
    (Elsevier, 2015) Djarfour, Noureddine; Ferahtia, Jalal; Babaia, Foudel; Baddari, Kamel; Said, El-adj
    This paper deals with the application of Generalized Regression Neural Networks to the seismic data filtering. The proposed system is a class of neural networks widely used for the continuous function mapping. They are based on the well known nonparametric kernel statistical estimators. The main advantages of this neural network include adaptability, simplicity and rapid training. Several synthetic tests are performed in order to highlight the merit of the proposed topology of neural network. In this work, the filtering strategy has been applied to remove random noises as well as source-related noises from real seismic data extracted from a field in the South of Algeria. The obtained results are very promising and indicate the high performance of the proposed filter in comparison to the well known frequency–wavenumber filter