As one type of resistance furnace, the electrical tube furnace (ETF) typically experiences input noise, measurement noise, system uncertainties, unmodeled dynamics and external disturbances, which significantly degrade its temperature control performance. To provide precise, and robust temperature tracking performance for the ETF, a robust composite control (RCC) method is proposed in this paper. The overall RCC method consists of four elements: First, the mathematical model of the ETF system is deduced, then a state feedback control (SFC) is constructed. Third, a novel disturbance observer (DO) is designed to estimate the lumped disturbance with one observer parameter. Moreover, the stability of the closed loop system including controller and observer dynamics is also analyzed by using the Lyapunov theory. Finally, numerical simulations are performed on the plant model of the ETF to demonstrate the efficiency and capabilities of the proposed control method.
This work introduces a new electrode geometry for making holes with high aspect ratios on AISI 304 using an electrical discharge drilling (EDD) process. In addition to commercially available cylindrical hollow electrodes, an elliptical electrode geometry has been designed, manufactured, and implemented. The principal aim was to improve the removal of debris formed during the erosion process that adversely affects the aspect ratio, dimensional accuracy, and surface integrity. The results were compared and discussed to evaluate the effectiveness of electrode geometry on the machining performance of EDD process with respect to the material removal rate (MRR,) the electrode wear rate (EWR), and the tool wear ratio (TWR). Dimensional features an
... Show MoreThe technology of reducing dimensions and choosing variables are very important topics in statistical analysis to multivariate. When two or more of the predictor variables are linked in the complete or incomplete regression relationships, a problem of multicollinearity are occurred which consist of the breach of one basic assumptions of the ordinary least squares method with incorrect estimates results.
There are several methods proposed to address this problem, including the partial least squares (PLS), used to reduce dimensional regression analysis. By using linear transformations that convert a set of variables associated with a high link to a set of new independent variables and unr
... Show MoreThis work was conducted to study the treatment of industrial waste water, and more particularly those in the General Company of Electrical Industries.This waste water, has zinc ion with maximum concentration in solution of 90 ppm.
The reuse of such effluent can be made possible via appropriate treatments, such as chemical coagulation, Na2S is used as coagulant.
The parameters that influenced the waste water treatment are: temperature, pH, dose of coagulant and settling time.
It was found that the best condition for zinc removal, within the range of operation used ,were a temperature of 20C a pH value of 13 , a coagulant dose of 15 g Na2S /400ml solution and a settling time of 7 days. Under these conditions the zinc concentrat
Tin dioxide doped silver oxide thin films with different x content (0, 0.03, 0.05, 0.07) have been prepared by pulse laser deposition technique (PLD) at room temperatures (RT). The effect of doping concentration on the structural and electrical properties of the films were studied. Atomic Force Measurement (AFM) measurements found that the average value of grain size for all films at RT decrease with increasing of AgO content. While an average roughness values increase with increasing x content. The electrical properties of these films were studied with different x content. The D.C conductivity for all films increases with increasing x content. Also, it found that activation energies decrease with increasing of AgO content for all films.
... Show MoreThe issue of penalized regression model has received considerable critical attention to variable selection. It plays an essential role in dealing with high dimensional data. Arctangent denoted by the Atan penalty has been used in both estimation and variable selection as an efficient method recently. However, the Atan penalty is very sensitive to outliers in response to variables or heavy-tailed error distribution. While the least absolute deviation is a good method to get robustness in regression estimation. The specific objective of this research is to propose a robust Atan estimator from combining these two ideas at once. Simulation experiments and real data applications show that the p
... Show MoreThe issue of penalized regression model has received considerable critical attention to variable selection. It plays an essential role in dealing with high dimensional data. Arctangent denoted by the Atan penalty has been used in both estimation and variable selection as an efficient method recently. However, the Atan penalty is very sensitive to outliers in response to variables or heavy-tailed error distribution. While the least absolute deviation is a good method to get robustness in regression estimation. The specific objective of this research is to propose a robust Atan estimator from combining these two ideas at once. Simulation experiments and real data applications show that the proposed LAD-Atan estimator
... Show MoreThe purpose of this paper is to identifying some of the physical, kinetic and electrical capabilities of the working muscles of patients with simple hemiplegic cerebral palsy, preparation of special exercises (rehabilitation and water) accompanied by symmetrical electrical stimulation in the rehabilitation of working muscles for patients with simple hemiplegic cerebral palsy, and identifying the effect of exercises, especially (rehabilitation and water), accompanied by symmetrical electrical stimulation, on some physical, kinetic and electrical capabilities in rehabilitating working muscles for patients with simple hemiplegic cerebral palsy. The researcher used the experimental approach with a one-group design with two pre and post-tests du
... Show MoreThis paper presents a modified training method for Recurrent Neural Networks. This method depends on the Non linear Auto Regressive (NARX) model with Modified Wavelet Function as activation function (MSLOG) in the hidden layer. The modified model is known as Modified Recurrent Neural (MRN). It is used for identification Forward dynamics of four Degrees of Freedom (4-DOF) Selective Compliance Assembly Robot Arm (SCARA) manipulator robot. This model is also used in the design of Direct Inverse Control (DIC). This method is compared with Recurrent Neural Networks that used Sigmoid activation function (RS) in the hidden layer and Recurrent Neural Networks with Wavelet activation function (RW). Simulation results shows that the MRN model is bett
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