A particle swarm optimization algorithm and neural network like self-tuning PID controller for CSTR system is presented. The scheme of the discrete-time PID control structure is based on neural network and tuned the parameters of the PID controller by using a particle swarm optimization PSO technique as a simple and fast training algorithm. The proposed method has advantage that it is not necessary to use a combined structure of identification and decision because it used PSO. Simulation results show the effectiveness of the proposed adaptive PID neural control algorithm in terms of minimum tracking error and smoothness control signal obtained for non-linear dynamical CSTR system.

In this work, the effect of variation of semi-angle of the conical part on the vibration characteristics of cylindrical-conical coupled structure is investigated. The shell is made of polyester resin reinforced by continuous E-glass fibers. The case is analyzed experimentally and numerically for orthotropic shell structures. The experimental program is conducted by exciting the fabricated structure by an impact hammer and monitoring the response using an attached accelerometer for different semi-angles of the conical part.
Software named SIGVIEW is used to perform the signal processing on the acquired signal in order to measure the natural frequencies and the corresponding mode shapes. The numerical investigation is achieved using ANS

Nanocomposites of polymer material based on CdS as filler
material and poly methyl methacrylate (PMMA) as host matrix have
been fabricated by chemical spray pyrolysis method on glass
substrate. CdS particles synthesized by co-precipitation route using
cadimium chloride and thioacetamide as starting materials and
ammonium hydroxide as precipitating agent. The structure is
examined by X-ray diffraction (XRD), the resultant film has
amorphous structure. The optical energy gap is found to be (4.5,
4.06) eV before and after CdS addition, respectively. Electrical
activation energy for CdS/PMMA has two regions with values of
0.079 and 0.433 eV.

In this work, fluid catalytic cracking of vacuum gas oil to produce gasoline over prepared faujasite type Y zeolite was investigated using experimental laboratory plant scale of fluidized bed reactor.
The catalytic activity of prepared faujasite type NaY, NaNH4Y and NaHY zeolites was investigated. The cracking process was carried out in the temperature range 440 to 500 oC, weight hourly space velocity (WHSV) range 10 to 25 h-1 ,and atmospheric pressure . The catalytic activities of the prepared faujasite type NaY , NaNH4Y and NaHY zeolites were determined in terms of vacuum gas oil (VGO) conversion, and gasoline yield . The conversion at 500oC and WHSV10 hr-1 by using faujasite type NaY, NaNH4Y and NaHY zeolite were 50.2%, 64.1% and 6

The denoising of a natural image corrupted by Gaussian noise is a problem in signal or image processing.  Much work has been done in the field of wavelet thresholding but most of it was focused on statistical modeling of wavelet coefficients and the optimal choice of thresholds.  This paper describes a new method for the suppression of noise in image by fusing the stationary wavelet denoising technique with adaptive wiener filter. The wiener filter is applied to the reconstructed image for the approximation coefficients only, while the thresholding technique is applied to the details coefficients of the transform, then get the final denoised image is obtained by combining the two results. The proposed method was applied by usin