Browsing by Author "Rahmoune, Fayçal"

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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
Experimental investigation of the charge pumping current in integrated MOS transistors
(1998) Rahmoune, Fayçal
The aim of this work is the investigation of the charge pumping technique through the variation of the gate voltage pulse width. The main part of this work is related to the investigation of the carge pumping current of MOSFETS and its dependence on the time and voltage parameters of the input gate pulses…
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
An integrated study of the dynamics of electromagnetic and acoustic regimes during failure of complex macrosystems using rock blocks
(Springer, 2011) Rahmoune, Fayçal; Tourtchine, Victor; Frolov, Anatoly D.; Baddari, Kamel
The development of the failure process in complex macrosystems using large rock samples subjected to biaxial compression has been studied by means of electromagnetic radiation (EMR) and acoustic emission (AE). In order to increase the stage of macrofailure development, a special procedure of rock loading was used to reveal regularities of nucleation and evolution of electromagnetic and acoustic structures. The synchronised measurements of EMR and AE allowed the control of the stress–strain state in the rocks and the structural developments of fracturing. Nonhomogeneous distribution of the rock spatial crystalline structure subject to load leads to a mosaic distribution of EMR and AE characteristics. As a result, the crack scale effect may be observed in the EMR and AE structure behaviours. The EMR and AE following the failure at different levels behave differently according to the difference in the scale and type of cracks. Intense high-frequency EMR pulses were recorded during the initial stage of microcrack generation occurring prior to major failure of material. This was not the case for AE. The nucleation and development of the macroscopic progressive failure evolution caused an alternation in energetic and frequencial properties of electromagnetic and acoustic events. It has been detected that the tensile cracks were more efficient than shear cracks in capacity of EMR generation. The analysis of self potentials allowed reaching the maximum of registered anomalous variations in the stage of microcracking interaction. This stage showed an increase in the EMR activity, which implies the nucleation of microcracks in various regions of rock interfaces. The gradual accumulation of these defects led to weakening some parts of the rock along with a disintegration of electric anomalies, increase of AE and a significant fluctuation in the rate ofEMR. When crack concentration attains its critical value, which results in the formation of dangerous macroscopic failure of higher level, AE shows an intense activity as well as anEMRlower frequency. The hierarchical development of rock failure using the ratio of the average crack size and the mean distance between cracks as a statistical concentration criterion is used to control the boundary of the transition from small dispersed cracks accumulation to gradual crack merger and the formation of the main macrofailure. These results could be transferred into larger scale levels to forecast dynamic events in the earth crust
Retina blood vessels segmentation by combining deep learning networks
(Inder science, 2023) Bachiri, Mohamed Elssaleh; Rahmoune, Adel; Rahmoune, Fayçal
In this paper, we propose two deep learning architectures for the segmentation and detection of the vascular networks of blood vessels in fundus images. First, we combined VGG16 with U-net, then, we used Resnet 34 in combination with U-net. Both architectures employ an encoding and a decoding path. In this paper, we used the DRIVE and STARE databases. After applying VGG 16+U-net on the DRIVE database, we obtained the accuracy value of 0.96955, 0.79929 sensitivity, 0.98624 specificity, 0.9805 recall, and 0.9833 F1-score. We applied VGG 16+U-net on STARE database and we got 0.95259 accuracy, 0.89996 sensitivity, 0.95530 specificity, 0.9933 recall, and 0.9742 F1-score. Concerning Resnet 34 + U-net, we got the value of 0.9692 accuracy, 0.7859 sensitivity, 0.9870 specificity, 0.9794 recall, and 0.9832 F1-score after applying on DRIVE database. Moreover, we got 0.9363 accuracy, 0.9335 sensitivity, 0.9246 specificity, 0.9961 recall, and 0.9649 F1-score after we applied Resnet 34+U-net on STARE.