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A Comparative Study of Various Intelligent Optimization Algorithms Based on Path Planning and Neural Controller for Mobile Robot
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In this paper, a cognitive system based on a nonlinear neural controller and intelligent algorithm that will guide an autonomous mobile robot during continuous path-tracking and navigate over solid obstacles with avoidance was proposed. The goal of the proposed structure is to plan and track the reference path equation for the autonomous mobile robot in the mining environment to avoid the obstacles and reach to the target position by using intelligent optimization algorithms. Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) Algorithms are used to finding the solutions of the mobile robot navigation problems in the mine by searching the optimal paths and finding the reference path equation of the optimal path. As well as, PSO algorithm is used to find and tune on-line the neural control gains values of the nonlinear neural controller to obtain the best torques actions of the wheels for the mining autonomous mobile robot. Simulation results by matlab showed that the proposed cognitive system is more accurate in terms of planning reference path to avoid obstacles and online finding and tuning parameters of the controller which generated smoothness control action without saturation state for tracking the reference path equation as well as minimize the mobile robot tracking pose error to zero value.

 

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Publication Date
Tue Sep 30 2008
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Optimization of Biochemical Treatment of Tannery Wastewater
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The present work is concerned with the finding of the optimum conditions for biochemical wastewater treatment for a local tannery. The water samples were taken from outline areas (the wastewater of the chrome and vegetable tannery) in equal volumes and subjected to sedimentation, biological treatment, and chemical and natural sedimentation treatment.
The Box-Wilson method of experimental design was adopted to find useful relationships between three operating variables that affect the treatment processes (temperature, aeration period and phosphate concentration) on the Biochemical Oxygen Demand (BOD5).
The experimental data collected by this method were successfully fitted to a second order polynomial mathematical model. The most fa

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Publication Date
Fri Nov 22 2019
Journal Name
Chalcogenide Letters
CONCENTRATION EFFECTS ON ELECTRONIC AND SPECTROSCOPIC PROPERTIES OF ZnCdS WURTZOIDS: A DENSITY FUNCTIONAL THEORY STUDY
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Publication Date
Sun Jul 02 2017
Journal Name
Iraqi Journal Of Science
A Study Medicinal and Nutritional Effects of Thyme on Hematological Changes in Male Albino Rats
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Publication Date
Sat Oct 01 2022
Journal Name
The Egyptian Journal Of Hospital Medicine
Impact of Vitamin D Status on Pregnant Women and Their Neonates: A Cross-Sectional Study
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Publication Date
Wed Jan 01 2014
Journal Name
Journal Of The College Of Languages (jcl)
A Contrastive Study on the Syntactic Structure of Standard English and Standard Arabic Determiner Phrase
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The determiner phrase is a syntactic category that appears inside the noun phrase and makes it definite or indefinite or quantifies it. The present study has found wide parametric differences between the English and Arabic determiner phrases in terms of the inflectional features, the syntactic distribution of determiners and the word order of the determiner phrase itself. In English, the determiner phrase generally precedes the head noun or its premodifying adjectival phrase, with very few exceptions where some determiners may appear after the head noun. In Arabic, parts of the determiner phrase precede the head noun and parts of it must appear after the head noun or after its postmodifying adjectival phrase creating a discontinu

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Publication Date
Sat Oct 01 2022
Journal Name
The Egyptian Journal Of Hospital Medicine
Study on Viral Infection and Related Parameters in A Sample of Diabetes Mellitus Type 2
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Publication Date
Mon Jun 01 2020
Journal Name
Journal Of Engineering
Artificial Neural Network (ANN) for Prediction of Viscosity Reduction of Heavy Crude Oil using Different Organic Solvents
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The increase globally fossil fuel consumption as it represents the main source of energy around the world, and the sources of heavy oil more than light, different techniques were used to reduce the viscosity and increase mobility of heavy crude oil. this study focusing on the experimental tests  and modeling with Back Feed Forward Artificial Neural Network (BFF-ANN) of the dilution technique to reduce a  heavy oil viscosity that was collected from the south- Iraq oil fields using organic solvents, organic diluents with different weight percentage  (5, 10 and  20 wt.% )  of  (n-heptane, toluene, and a mixture of  different ratio

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Publication Date
Mon Jun 01 2015
Journal Name
. International Journal Of Computer Science And Mobile Computing
A Hybrid Lossy Image Compression based on Wavelet Transform, Polynomial Approximation Model, Bit Plane Slicing and Absolute Moment Block Truncation
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Publication Date
Sat Nov 01 2008
Journal Name
Digital Signal Processing
A high performance parallel Radon based OFDM transceiver design and simulation
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major goal of the next-generation wireless communication systems is the development of a reliable high-speed wireless communication system that supports high user mobility. They must focus on increasing the link throughput and the network capacity. In this paper a novel, spectral efficient system is proposed for generating and transmitting twodimensional (2-D) orthogonal frequency division multiplexing (OFDM) symbols through 2- D inter-symbol interference (ISI) channel. Instead of conventional data mapping techniques, discrete finite Radon transform (FRAT) is used as a data mapping technique due to the increased orthogonality offered. As a result, the proposed structure gives a significant improvement in bit error rate (BER) performance. Th

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Publication Date
Thu Aug 01 2024
Journal Name
Water Practice & Technology
Artificial neural network and response surface methodology for modeling oil content in produced water from an Iraqi oil field
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ABSTRACT<p>The majority of the environmental outputs from gas refineries are oily wastewater. This research reveals a novel combination of response surface methodology and artificial neural network to optimize and model oil content concentration in the oily wastewater. Response surface methodology based on central composite design shows a highly significant linear model with P value &lt;0.0001 and determination coefficient R2 equal to 0.747, R adjusted was 0.706, and R predicted 0.643. In addition from analysis of variance flow highly effective parameters from other and optimization results verification revealed minimum oily content with 8.5 ± 0.7 ppm when initial oil content 991 ppm, tempe</p> ... Show More
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