The paper present design of a control structure that enables integration of a Kinematic neural controller for trajectory tracking of a nonholonomic differential two wheeled mobile robot, then  proposes a Kinematic neural controller to direct a National Instrument mobile robot (NI Mobile Robot). The controller is to make the actual velocity of the wheeled mobile robot close the required velocity by guarantees that the trajectory tracking mean squire error converges at minimum tracking error. The proposed tracking control system consists of two layers; The first layer is a multi-layer perceptron neural network system that controls the mobile robot to track the required path , The second layer is an optimization layer ,which is impleme

Background: As a multifactorial disorder, temporomandibular joint (TMD) is difficult to diagnose, and multiple factors affect the joint and cause the temporomandibular disorder. Standardization of clinical diagnosis of TMD should be used to reach a definite clinical diagnosis; the condylar bone may degenerate in accordance with these disorders. Aims: Evaluate the correlation between the clinical diagnosis and degenerative condylar change (flattening, sclerosis, erosion, and osteophyte). Materials and Methods: A prospective study with a study group of 97 TMD patients (total of 194 joints) aged 20 to 50. Patients were sent to cone beam computed tomography (CBCT) to assess the degenerative condylar change. Results: No association was found bet

This paper presents a cognition path planning with control algorithm design for a nonholonomic wheeled mobile robot based on Particle Swarm Optimization (PSO) algorithm. The aim of this work is to propose the circular roadmap (CRM) method to plan and generate optimal path with free navigation as well as to propose a nonlinear MIMO-PID-MENN controller in order to track the wheeled mobile robot on the reference path. The PSO is used to find an online tune the control parameters of the proposed controller to get the best torques actions for the wheeled mobile robot. The numerical simulation results based on the Matlab package show that the proposed structure has a precise and highly accurate distance of the generated refere

With growing global demand for hydrocarbons and decreasing conventional reserves, the gas industry is shifting its focus in the direction of unconventional reservoirs. Tight gas reservoirs have typically been deemed uneconomical due to their low permeability which is understood to be below 0.1mD, requiring advanced drilling techniques and stimulation to enhance hydrocarbons. However, the first step in determining the economic viability of the reservoir is to see how much gas is initially in place. Numerical simulation has been regarded across the industry as the most accurate form of gas estimation, however, is extremely costly and time consuming. The aim of this study is to provide a framework for a simple analytical method to esti

The objective of this study is to apply Artificial Neural Network for heat transfer analysis of shell-and-tube heat exchangers widely used in power plants and refineries. Practical data was obtained by using industrial heat exchanger operating in power generation department of Dura refinery. The commonly used Back Propagation (BP) algorithm was used to train and test networks by divided the data to three samples (training, validation and testing data) to give more approach data with actual case. Inputs of the neural network include inlet water temperature, inlet air temperature and mass flow rate of air. Two outputs (exit water temperature to cooling tower and exit air temperature to second stage of air compressor) were taken in ANN.

In this study, an efficient compression system is introduced, it is based on using wavelet transform and two types of 3Dimension (3D) surface representations (i.e., Cubic Bezier Interpolation (CBI)) and 1 st order polynomial approximation. Each one is applied on different scales of the image; CBI is applied on the wide area of the image in order to prune the image components that show large scale variation, while the 1 st order polynomial is applied on the small area of residue component (i.e., after subtracting the cubic Bezier from the image) in order to prune the local smoothing components and getting better compression gain. Then, the produced cubic Bezier surface is subtracted from the image signal to get the residue component. Then, t

In this paper, we present multiple bit error correction coding scheme based on extended Hamming product code combined with type II HARQ using shared resources for on chip interconnect. The shared resources reduce the hardware complexity of the encoder and decoder compared to the existing three stages iterative decoding method for on chip interconnects. The proposed method of decoding achieves 20% and 28% reduction in area and power consumption respectively, with only small increase in decoder delay compared to the existing three stage iterative decoding scheme for multiple bit error correction. The proposed code also achieves excellent improvement in residual flit error rate and up to 58% of total power consumption compared to the other err