Publications Scientifiques
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Item A new methodology to predict the sequence of GFRP layers using machine learning and JAYA algorithm(Elsevier, 2023) Fahem, Noureddine; Belaidi, Idir; Oulad Brahim, Abdelmoumin; Capozucca, Roberto; Thanh, Cuong Le; Khatir, Samir; Abdel Wahab, Magd M.In this paper, the best stacking sequence using experimental tests of GFRP composites is investigated. The main objective of this work is to determine the main specification of GFRP composite material, which is represented by its physics-mechanical properties, weight, and cost, before performing a series of experimental tests based on various stacking sequences. Our methodology is divided into three stages. The first stage is characterized by extracting the bending data from mechanical tests of some GFRP composites. In the second stage, the validated numerical model is used to simulate numerous cases of stacking sequences. In the last stage, the extracted data is used to determine the parameters for different stacking sequences using an inverse technique based on ANN and JAYA algorithm. The results provide a good prediction of parameters as well as a good orientation to make decisions on the best GFRP stacking sequence to be used, according to the required specifications of the manufacturer.Item Exploring tensile properties of bio composites reinforced date palm fibers using experimental and Modelling Approaches(Elsevier, 2024) Saada, Khalissa; Zaoui, Moussa; Amroune, Salah; Benyettou, Riyadh; Hechaichi, Amina; Jawaid, Mohammad; Hashem, Mohamed Ibrahim; Uddin, ImranThe objective of this study was to assess the tensile strength of epoxy bio-composites reinforced with palm fibers, both untreated and treated with sodium carbonate NaHCO3 at a concentration of 10 % (w/v) for 24 and 96 h, with varying weight percentages of fibers (15 %, 20 %, 25 %, and 30 %). To predict the mechanical performance of the composites, two methods were employed: artificial neural network (ANN) and response surface methodology (RSM). A Box-Behnken RSM design was used to conduct experiments and establish a mathematical model of the bio-composite behavior as a function of the fiber percentage in the samples, specimen cross-section, and treatment time. The ANN forecasts showed consistent expected values for the bio-composite sample behavior, with a correlation coefficient (R2) greater than 0.98 for Young's modulus and 0.97 for stress. Similarly, the correlation coefficients obtained by RSM for the mechanical properties were also highly satisfactory, with an R2 of 0.89 for Young's modulus and 0.87 for stress. Finally, the errors generated by each method (Box-Behnken and ANN) were compared to the experimental results.Item EEG signal feature extraction and classification for epilepsy detection(Slovene Society Informatika, 2022) Cherifi, Dalila; Falkoun, Noussaiba; Ouakouak, Ferial; Boubchir, Larbi; Nait-Ali, AmineEpilepsy is a neurological disorder of the central nervous system, characterized by sudden seizures caused by abnormal electrical discharges in the brain. Electroencephalogram (EEG) is the most common technique used for Epilepsy diagnosis. Generally, it is done by the manual inspection of the EEG recordings of active seizure periods (ictal). Several techniques have been proposed throughout the years to automate this process. In this study, we have developed three different approaches to extract features from the filtered EEG signals. The first approach was to extract eight statistical features directly from the time-domain signal. In the second approach, we have used only the frequency domain information by applying the Discrete Cosine Transform (DCT) to the EEG signals then extracting two statistical features from the lower coefficients. In the last approach, we have used a tool that combines both time and frequency domain information, which is the Discrete Wavelet Transform (DWT). Six different wavelet families have been tested with their different orders resulting in 37 wavelets. The first three decomposition levels were tested with every wavelet. Instead of feeding the coefficients directly to the classifier, we summarized them in 16 statistical features. The extracted features are then fed to three different classifiers k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), and Artificial Neural Network (ANN) to perform two binary classification scenarios: healthy versus epileptic (mainly from interictal activity), and seizure-free versus ictal. We have used a benchmark database, the Bonn database, which consists of five different sets. In the first scenario, we have taken six different combinations of the available data. While in the second scenario, we have taken five combinations. For Epilepsy detection (healthy vs epileptic), the first approach performed badly. Using the DCT improved the results, but the best accuracies were obtained with the DWT-based approach. For seizure detection, the three methods performed quite well. However, the third method had the best performance and was better than many state-of-the-art methods in terms of accuracy. After carrying out the experiments on the whole EEG signal, we separated the five rhythms and applied the DWT on them with the Daubechies7 (db7) wavelet for feature extraction. We have observed that close accuracies to those recorded before can be achieved with only the Delta rhythm in the first scenario (Epilepsy detection) and the Beta rhythm in the second scenario (seizure detection)Item Identification and modeling of a rotary kiln in cement plant based on ANN (MLP)(Springer, 2022) Doghmane, Mohamed Zinelabidine; Kidouche, Madjid; Eladj, S.; Ouali, A.The objective of this study is to identify and model a rotary cement kiln based on production history data by using an artificial neural network MLP algorithm. The usefulness of this algorithm is that it provides a reliable empirical relation between the inputs parameters (Flow, Temperature, and pressure) and the outputs, which indicate the cement quality. Where, the most critical process in a cement production facility is cooking the mixed raw material in a rotary kiln; its task is to gradually burn and bakes a suitable mixture of input material to produce clinker. Therefore, the rotary kiln is the most important part in a cement factory. From another side, the control of a cement kiln is a complex process due to many factors namely: The Non linearity of the system caused by the chemical reactions, its dynamic and high dimensionality. Therefore, identification, modelling, prediction and simulation of Kiln system is very crucial step in managing and optimizing the cement production. Since the ANN has demonstrated its effectiveness in identifying a large class of complex nonlinear systems, it has been proposed in this case study to model cement Kiln of plant based on Multi-Layer Perceptron (MLP) approach. The MLP algorithm has been trained by using history data of twenty four months, and it has been tested and validated through comparison with production data of the next six months after the training. The obtained results have demonstrated the superiority of the proposed ANN approach over the conventional modelling approachesItem Ionospheric data prediction of DEMETER Satellite using Levenberg Marquardt neural network model. application to ISL instrument(2015) Ouadfeul, Sid-Ali; Tourtchine, Victor; Aliouane, LeilaItem The use of root mean square values (RMS) for the shortcoming diagnostic of permanent magnet synchronous machine using artificial neurone network module(2008) Khodja, Djalal Eddine; Chetate, BoukhmisItem Application of a radial basis function artificial neural network to seismic data inversion(Elsevier, 2009) Baddari, Kamel; Aïfa, Tahar; Djarfour, Noureddine; Ferahtia, JalalItem Sigmoid function approximation for ANN implementation in FPGA devices(2010) Khodja, Djalal Eddine; Kheldoun, Aissa; Refoufi, L.The objective of this work is the implementation of Artificial Neural Network on a FPGA board. This implementation aim is to contribute in the hardware integration solutions in the areas such as monitoring, diagnosis, maintenance and control of power system as well as industrial processes. Since the Simulink library provided by Xilinx, has all the blocks that are necessary for the design of Artificial Neural Networks except a few functions such as sigmoid function. In this work, an approximation of the sigmoid function in polynomial form has been proposed. Then, the sigmoid function approximation has been implemented on FPGA using the Xilinx library. Tests results are satisfactoryItem Daily geomagnetic field prediction of intermagnet observatories data using the multilayer perceptron neural network(Springer, 2014) Ouadfeul, Sid-Ali; Tourtchine, Victor; Aliouane, Leila