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Neural network modeling of rutting performance for sustainable asphalt mixtures modified by industrial waste alumina
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Publication Date
Tue May 01 2018
Journal Name
Journal Of Engineering
Performance Evaluation of Plant Produced Warm Mix Asphalt
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Warm mix asphalt (WMA) is relatively a new technology which enables the production and compaction of asphalt concrete mixtures at temperatures 15-40 °C lower than that of traditional hot mix asphalt HMA. In the present work, six asphalt concrete mixtures were produced in the mix plant (1 ton each) in six different batches. Half of these mixes were WMA and the other half were HMA.  Three types of fillers (limestone dust, Portland cement and hydrated lime) were used for each type of mix. Samples were then taken from these patches and transferred to lab for performance testing which includes: Marshall characteristics, moisture susceptibility (indirect tension test), resilient modulus, permanent deformation (axial repe

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Publication Date
Sun Mar 31 2013
Journal Name
Inventi Impact: Artificial Intelligence
SIMULATION OF IDENTIFICATION AND CONTROL OF SCARA ROBOT USING MODIFIED RECURRENT NEURAL NETWORKS
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This paper presents a modified training method for Recurrent Neural Networks. This method depends on the Non linear Auto Regressive (NARX) model with Modified Wavelet Function as activation function (MSLOG) in the hidden layer. The modified model is known as Modified Recurrent Neural (MRN). It is used for identification Forward dynamics of four Degrees of Freedom (4-DOF) Selective Compliance Assembly Robot Arm (SCARA) manipulator robot. This model is also used in the design of Direct Inverse Control (DIC). This method is compared with Recurrent Neural Networks that used Sigmoid activation function (RS) in the hidden layer and Recurrent Neural Networks with Wavelet activation function (RW). Simulation results shows that the MRN model is bett

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Publication Date
Wed Dec 30 2009
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Prediction of the Point Efficiency of Sieve Tray Using Artificial Neural Network
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An application of neural network technique was introduced in modeling the point efficiency of sieve tray, based on a
data bank of around 33l data points collected from the open literature.Two models proposed,using back-propagation
algorithm, the first model network consists: volumetric liquid flow rate (QL), F foctor for gas (FS), liquid density (pL),
gas density (pg), liquid viscosity (pL), gas viscosity (pg), hole diameter (dH), weir height (hw), pressure (P) and surface
tension between liquid phase and gas phase (o). In the second network, there are six parameters as dimensionless
group: Flowfactor (F), Reynolds number for liquid (ReL), Reynolds number for gas through hole (Reg), ratio of weir
height to hole diqmeter

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Publication Date
Wed May 31 2017
Journal Name
Journal Of Engineering
Evaluating the Performance of High Modulus Asphalt Concrete Mixture for Base Course in Iraq
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In the 1980s, the French Administration Roads LCPC developed high modulus mixtures (EME) by using hard binder. This type of mixture presented good resistance to moisture damage and improved . mechanical properties for asphalt mixtures including high modulus, good fatigue behaviour and excellent resistance to rutting. In Iraq, this type of mixture has not been used yet. The main objective of this research is to evaluate the performance of high modulus mixtures and comparing them with the conventional mixture, to achieve this objective, asphalt concrete mixes were prepared and then tested to evaluate their engineering properties which include moisture damage, resilient modulus, permanent deformation and fatigue characteristics. These prope

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Publication Date
Wed May 31 2017
Journal Name
Journal Of Engineering
Evaluating the Performance of High Modulus Asphalt Concrete Mixture for Base Course in Iraq
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In the 1980s, the French Administration Roads LCPC developed high modulus mixtures (EME) by using hard binder. This type of mixture presented good resistance to moisture damage and improved mechanical properties for asphalt mixtures including high modulus, good fatigue behaviour and excellent resistance to rutting. In Iraq, this type of mixture has not been used yet. The main objective of this research is to evaluate the performance of high modulus mixtures and comparing them with the conventional mixture, to achieve this objective, asphalt concrete mixes were prepared and then tested to evaluate their engineering properties which include moisture damage, resilient modulus, permanent deformation and fatigue characteristics. These pro

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Publication Date
Tue Dec 30 2008
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Separation of Hexane-Benzene Mixtures by Emulsion Liquid Membrane.
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The effect of operating parameters on the batch scale separation of hydrocarbon mixture (benzene and hexane) using
emulsion liquid membrane technique is reported. Sparkleen detergent was used as surfactant and heavy mineral oil as
solvent to receive the permeates.
From the experimental results, the parameters that influenced the permeation are, composition of feed, contact time
with solvent, ratio of volume of solvent to volume of hydrocarbon feed, ratio of volume of surfactant solution to volume
of hydrocarbon feed, surfactant concentration, mixing intensity and glycerol as polar additive in the surfactant solution
to eliminate drop breakup.
The best conditions for the separation in this study were found to be: comp

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Publication Date
Sun Dec 30 2007
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Prediction of Fractional Hold-Up in RDC Column Using Artificial Neural Network
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In the literature, several correlations have been proposed for hold-up prediction in rotating disk contactor. However,
these correlations fail to predict hold-up over wide range of conditions. Based on a databank of around 611
measurements collected from the open literature, a correlation for hold up was derived using Artificial Neiral Network
(ANN) modeling. The dispersed phase hold up was found to be a function of six parameters: N, vc , vd , Dr , c d m / m ,
s . Statistical analysis showed that the proposed correlation has an Average Absolute Relative Error (AARE) of 6.52%
and Standard Deviation (SD) 9.21%. A comparison with selected correlations in the literature showed that the
developed ANN correlation noticeably

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Publication Date
Sun Dec 31 2017
Journal Name
Al-khwarizmi Engineering Journal
Solving the Inverse Kinematic Equations of Elastic Robot Arm Utilizing Neural Network
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The inverse kinematic equation for a robot is very important to the control robot’s motion and position. The solving of this equation is complex for the rigid robot due to the dependency of this equation on the joint configuration and structure of robot link. In light robot arms, where the flexibility exists, the solving of this problem is more complicated than the rigid link robot because the deformation variables (elongation and bending) are present in the forward kinematic equation. The finding of an inverse kinematic equation needs to obtain the relation between the joint angles and both of the end-effector position and deformations variables. In this work, a neural network has been proposed to solve the problem of inverse kinemati

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Publication Date
Tue Jun 20 2023
Journal Name
Baghdad Science Journal
Detection of Autism Spectrum Disorder Using A 1-Dimensional Convolutional Neural Network
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Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D

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Publication Date
Tue Oct 23 2018
Journal Name
Journal Of Economics And Administrative Sciences
Use projection pursuit regression and neural network to overcome curse of dimensionality
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Abstract

This research aim to overcome the problem of dimensionality by using the methods of non-linear regression, which reduces the root of the average square error (RMSE), and is called the method of projection pursuit regression (PPR), which is one of the methods for reducing dimensions that work to overcome the problem of dimensionality (curse of dimensionality), The (PPR) method is a statistical technique that deals with finding the most important projections in multi-dimensional data , and With each finding projection , the data is reduced by linear compounds overall the projection. The process repeated to produce good projections until the best projections are obtained. The main idea of the PPR is to model

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