Abstract

This research presents a on-line cognitive tuning control algorithm for the nonlinear controller of path-tracking for dynamic wheeled mobile robot to stabilize and follow a continuous reference path with minimum tracking pose error. The goal of the proposed structure of a hybrid (Bees-PSO) algorithm is to find and tune the values of the control gains of the nonlinear (neural and back-stepping method) controllers as a simple on-line with fast tuning techniques in order to obtain the best torques actions of the wheels for the cart mobile robot from the proposed two controllers. Simulation results (Matlab Package 2012a) show that the nonlinear neural controller with hybrid Bees-PSO cognitive algorithm is m

The purpose of this paper is to gain a good understanding about wake region behind the car body due to the aerodynamic effect when the air flows over the road vehicle during its movement. The main goal of this study is to discuss the effect of the geometry on the wake region and the aerodynamic drag coefficient. Results will be achieved by using two different shapes, which are the fastback and the notchback. The study will be implemented by the Computational Fluid Dynamic (CFD) by using STAR-CCM+^® software for the simulation. This study investigates the steady turbulent flow using k-epsilon turbulence model. The results obtained from the simulation show that the region of the air separation behind the vehicle

Survivin, a member of inhibitor of apoptosis family is increasingly used as a target for cancer therapy design because it has a key role in cell growth and inhibition of cell apoptosis. Also it can be used as a biomarker for targeting cancer because it is found in almost all cancer but not normal cells. Our strategy was to design (computationally) a molecule to be used as survivin inhibitor. This molecule was named lead10 and was used furthermore to find (virtually) existing drugs with a good survivin inhibition activity.

           Strives Total Productive Maintenance  to increase the overall effectiveness of the equipment through the early involvement in the design and manufacture of equipment productivity. It also operates in an environment of simultaneous engineering work on the synchronization of activities to take advantage of early information by maintenance engineers, design, operation, and that helps to reduce the faults and facilitate future maintenance tasks.

    Has adopted a search in the theoretical concept of the total maintenance productivity and concurrent engineering activities carried out during which the conjunction a

The purpose of the current investigation is to distinguish between working memory ( ) in five patients with vascular dementia ( ), fifteen post-stroke patients with mild cognitive impairment ( ), and fifteen healthy control individuals ( ) based on background electroencephalography (EEG) activity. The elimination of EEG artifacts using wavelet (WT) pre-processing denoising is demonstrated in this study. In the current study, spectral entropy ( ), permutation entropy ( ), and approximation entropy ( ) were all explored. To improve the  classification using the k-nearest neighbors ( NN) classifier scheme, a comparative study of using fuzzy neighbourhood preserving analysis with -decomposition ( ) as a dimensionality reduction technique an

The field of Optical Character Recognition (OCR) is the process of converting an image of text into a machine-readable text format. The classification of Arabic manuscripts in general is part of this field. In recent years, the processing of Arabian image databases by deep learning architectures has experienced a remarkable development. However, this remains insufficient to satisfy the enormous wealth of Arabic manuscripts. In this research, a deep learning architecture is used to address the issue of classifying Arabic letters written by hand. The method based on a convolutional neural network (CNN) architecture as a self-extractor and classifier. Considering the nature of the dataset images (binary images), the contours of the alphabet

The hydrological process has a dynamic nature characterised by randomness and complex phenomena. The application of machine learning (ML) models in forecasting river flow has grown rapidly. This is owing to their capacity to simulate the complex phenomena associated with hydrological and environmental processes. Four different ML models were developed for river flow forecasting located in semiarid region, Iraq. The effectiveness of data division influence on the ML models process was investigated. Three data division modeling scenarios were inspected including 70%–30%, 80%–20, and 90%–10%. Several statistical indicators are computed to verify the performance of the models. The results revealed the potential of the hybridized s

   This research provides a novel technique for using metal organic frameworks (HKUST-1) as a gas storage system for liquefied petroleum gas (LPG) in Iraqi vehicles to avoid the drawbacks of the currently employed method of LPG gas storage. A low-cost adsorbent called HKUST-1 was prepared and characterized in this research to investigate its ability for propane storage at different temperatures (25, 30, 35, and 40 ^oC) and pressures of (1-7) bar. HKUST-1 was made using a hydrothermal method and characterized using powder X-ray diffraction, BET surface area, scanning electron microscopic (SEM), and Fourier Transforms infrared spectroscopy (FTIR). The HKUST-1 was produced using a hydrothermal technique and possesses a high crys