arXiv subjects

Rachid Taleb

Publications and source records attributed to Rachid Taleb.

5 recordsLinked to original sources

arXiv2023

The Important Electron Impact Collision Cross Sections with Methane

The electrical discharge plasma in methane is used in many fields of technology and the knowledge of the electron impact cross sections with the molecule of this gas is necessary for the modeling, which represents a good tool for understanding this phenomenon. The main object of this work is a finding the important electron impact cross sections with the methane molecule through the reviewing the theoretical and measured data found in the literature

physics.plasm-ph↗

arXiv2023

Calculation of Electron Swarm Parameters in Ar/H2 and Kr/H2 Mixtures

To develop the modeling of electrical discharges in a gaseous medium, it must to know a numbers of basic data such as: the electron collision cross sections for particle modeling or the electron swarm parameters in the case of the fluid modeling. The knowledge of all the electron swarm parameters as a function of the reduced electric field is indispensable for the fluid models based on the first and the second moment of the electron energy distribution function. The objective of this work is to calculate the mobility, the characteristic energy, and the ionization coefficient in the case of the mixtures Ar/H2 and Kr/H2. The results obtained will be compared with measurements already made by different authors.

physics.plasm-ph↗

arXiv2021

Cascade-Forward Neural Network Based on Resilient Backpropagation for Simultaneous Parameters and State Space Estimations of Brushed DC Machines

A sensorless speed, average temperature and resistance estimation technique based on Neural Network (NN) for brushed DC machines is proposed in this paper. The literature on parameters and state spaces estimations of the Brushed DC machines, shows a variety of approaches. However, these observers are sensitive to a noise, on the model accuracy also are difficult to stabilize and to converge. Furthermore, the majority of earlier works, estimate either the speed or the temperature or the winding resistance. According to the literatures, the Resilient backpropagation (RBP) as is the known as the faster BP algorithm, Cascade-Forward Neural Network (CFNN), is known as the among accelerated learning backpropagation algorithms, that's why where it is found in several researches, also in several applications in these few years. The main objective of this paper is to introduce an intelligent sensor based on resilient BP to estimate simultaneously the speed, armature temperature and resistance of brushed DC machines only from the measured current and voltage. A comparison between the obtained results and the results of traditional estimator has been made to prove the ability of the proposed method. This method can be embedded in thermal monitoring systems, in high performance motor drives.

eess.SP↗

arXiv2019

Estimation of speed, armature temperature and resistance in brushed DC machines using a CFNN based on BFGS BP

In this paper, a sensorless speed and armature resistance and temperature estimator for Brushed (B) DC machines is proposed, based on a Cascade-Forward Neural Network (CFNN) and Quasi-Newton BFGS backpropagation (BP). Since we wish to avoid the use of a thermal sensor, a thermal model is needed to estimate the temperature of the BDC machine. Previous studies propose either non-intelligent estimators which depend on the model, such as the Extended Kalman Filter (EKF) and Luenberger's observer, or estimators which do not estimate the speed, temperature and resistance simultaneously. The proposed method has been verified both by simulation and by comparison with the simulation results available in the literature

eess.SP↗

arXiv2019

Intelligent Sensor Based Bayesian Neural Network for Combined Parameters and States Estimation of a Brushed DC Motor

The objective of this paper is to develop an Artificial Neural Network (ANN) model to estimate simultaneously, parameters and state of a brushed DC machine. The proposed ANN estimator is novel in the sense that his estimates simultaneously temperature, speed and rotor resistance based only on the measurement of the voltage and current inputs. Many types of ANN estimators have been designed by a lot of researchers during the last two decades. Each type is designed for a specific application. The thermal behavior of the motor is very slow, which leads to large amounts of data sets. The standard ANN use often Multi-Layer Perceptron (MLP) with Levenberg-Marquardt Backpropagation (LMBP), among the limits of LMBP in the case of large number of data, so the use of MLP based on LMBP is no longer valid in our case. As solution, we propose the use of Cascade-Forward Neural Network (CFNN) based Bayesian Regulation backpropagation (BRBP). To test our estimator robustness a random white-Gaussian noise has been added to the sets. The proposed estimator is in our viewpoint accurate and robust.

eess.SY↗