In this paper, the speed control of the real DC motor is experimentally investigated using nonlinear PID neural network controller. As a simple and fast tuning algorithm, two optimization techniques are used; trial and error method and particle swarm optimization PSO algorithm in order to tune the nonlinear PID neural controller's parameters and to find best speed response of the DC motor. To save time in the real system, a Matlab simulation package is used to carry out these algorithms to tune and find the best values of the nonlinear PID parameters. Then these parameters are used in the designed real time nonlinear PID controller system based on LabVIEW package. Simulation and experimental results are compared with each other and showe

This paper investigate a sensorless speed control of a separately excited dc motor fed from a buck type dc-dc converter. The control system is designed in digital technique by using a two dimension look-up table. The performance of the drive system was evaluated by digital simulation using Simulink toolbox of Matlab.

One of the unique properties of laser heating applications is its powerful ability for precise pouring of energy on the needed regions in heat treatment applications. The rapid rise in temperature at the irradiated region produces a high temperature gradient, which contributes in phase metallurgical changes, inside the volume of the irradiated material. This article presents a comprehensive numerical work for a model based on experimentally laser heated AISI 1110 steel samples. The numerical investigation is based on the finite element method (FEM) taking in consideration the temperature dependent material properties to predict the temperature distribution within the irradiated material volume. The finite element analysis (FEA) was carried

This research studying the phenomenon of Doppler (frequency Doppler) as a method through which the direction and speed of the blood cells flows in blood vessels wear measured. This Doppler frequency is relied upon in medicine for measuring the speed of blood flow, because the blood flow is an important concept from the concepts of medicine. It represents the function and efficient of the heart and blood vessels in the body so any defect in this function will appear as a change in the speed of blood flow from the normal value assumed. As this speed changes alot in cases of  disease and morbidity  of the heart, so in order to identify the effect of changing the Doppler frequency on the speed of blood flow and the relationship of

The study aims to identify the metamemory and perceptual speed among College students, the correlation between metamemory and perceptual speed among College students, and to which extend does metamemory contribute to perceptual speed among College students. The sample consisted of group of students were selected randomly by the researcher from five-different disciplines at the college of education for pure sciences. To collect study data, the researcher utilized two scales: perceptual speed scale that has translated to Arabic language by (Al-Shraqawi, Al- Shaikh, and Nadia Abed Al-Salam (1993). The second scale is metamemory scale (2002) which has translated to Arabic by Abu Ghazal (2007). The results revealed that college students have

Convolutional Neural Networks (CNN) have high performance in the fields of object recognition and classification. The strength of CNNs comes from the fact that they are able to extract information from raw-pixel content and learn features automatically. Feature extraction and classification algorithms can be either hand-crafted or Deep Learning (DL) based. DL detection approaches can be either two stages (region proposal approaches) detector or a single stage (non-region proposal approach) detector. Region proposal-based techniques include R-CNN, Fast RCNN, and Faster RCNN. Non-region proposal-based techniques include Single Shot Detector (SSD) and You Only Look Once (YOLO). We are going to compare the speed and accuracy of Faster RCNN,