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Estimating Pitting Corrosion Depth and Density on Carbon Steel (C-4130) using Artificial Neural Networks
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The purpose of this research is to investigate the impact of corrosive environment (corrosive ferric chloride of 1, 2, 5, 6% wt. at room temperature), immersion period of (48, 72, 96, 120, 144 hours), and surface roughness on pitting corrosion characteristics and use the data to build an artificial neural network and test its ability to predict the depth and intensity of pitting corrosion in a variety of conditions. Pit density and depth were calculated using a pitting corrosion test on carbon steel (C-4130). Pitting corrosion experimental tests were used to develop artificial neural network (ANN) models for predicting pitting corrosion characteristics. It was found that artificial neural network models were shown to be quite effective; the results were validated by the experimental agreement with those acquired from laboratory tests. Specifically, the correlation coefficient, R = 0.9944.

 

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Publication Date
Fri Mar 31 2017
Journal Name
Journal Of Information And Communication Convergence Engineering
Survey on Physical Layer Security in Downlink Networks
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In this paper, we discuss physical layer security techniques in downlink networks, including eavesdroppers. The main objective of using physical layer security is delivering a perfectly secure message from a transmitter to an intended receiver in the presence of passive or active eavesdroppers who are trying to wiretap the information or disturb the network stability. In downlink networks, based on the random feature of channels to terminals, opportunistic user scheduling can be exploited as an additional tool for enhancing physical layer security. We introduce user scheduling strategies and discuss the corresponding performances according to different levels of channel state information (CSI) at the base station (BS). We show that the avai

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Publication Date
Fri Mar 01 2024
Journal Name
Baghdad Science Journal
A Comparison between Ericson's Formulae Results and Experimental Data Using New Formulae of Single Particle Level Density
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The partial level density PLD of pre-equilibrium reactions that are described by Ericson’s formula has been studied using different formulae of single particle level density . The parameter  was used from the equidistant spacing model (ESM) model and the non- equidistant spacing model (non-ESM) and another formula of  are derived from the relation between  and level density parameter . The formulae used to derive  are the Roher formula, Egidy formula, Yukawa formula, and Thomas –Fermi formula. The partial level density results that depend on  from the Thomas-Fermi formula show a good agreement with the experimental data.

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Publication Date
Tue Sep 30 2014
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Evaluation of Hydrocarbon Saturation Using Carbon Oxygen (CO) Ratio and Sigma Tool
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The main aim of this study is to evaluate the remaining oil in previously produced zones, locate the water productive zone and look for any bypassed oil behind casing in not previously perforated intervals. Initial water saturation was calculated from digitized open hole logs using a cut-off value of 10% for irreducible water saturation. The integrated analysis of the thermal capture cross section, Sigma and Carbon/oxygen ratio was conducted and summarized under well shut-in and flowing conditions. The logging pass zone run through sandstone Zubair formation at north Rumaila oil field. The zones where both the Sigma and the C/O analysis show high remaining oil saturation simila

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Publication Date
Mon Sep 01 2014
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Evaluation of Hydrocarbon Saturation Using Carbon Oxygen (CO) Ratio and Sigma Tool
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The main aim of this study is to evaluate the remaining oil in previously produced zones, locate the water productive zone and look for any bypassed oil behind casing in not previously perforated intervals. Initial water saturation was calculated from digitized open hole logs using a cut-off value of 10% for irreducible water saturation. The integrated analysis of the thermal capture cross section, Sigma and Carbon/oxygen ratio was conducted and summarized under well shut-in and flowing conditions. The logging pass zone run through sandstone Zubair formation at north Rumaila oil field. The zones where both the Sigma and the C/O analysis show high remaining oil saturation similar to the open hole oil saturation, could be good oil zones that

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Publication Date
Sun Nov 01 2020
Journal Name
Iop Conference Series: Materials Science And Engineering
Face Recognition and Emotion Recognition from Facial Expression Using Deep Learning Neural Network
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Abstract<p>Face recognition, emotion recognition represent the important bases for the human machine interaction. To recognize the person’s emotion and face, different algorithms are developed and tested. In this paper, an enhancement face and emotion recognition algorithm is implemented based on deep learning neural networks. Universal database and personal image had been used to test the proposed algorithm. Python language programming had been used to implement the proposed algorithm.</p>
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Publication Date
Sat Jun 06 2020
Journal Name
Journal Of The College Of Education For Women
Image classification with Deep Convolutional Neural Network Using Tensorflow and Transfer of Learning
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The deep learning algorithm has recently achieved a lot of success, especially in the field of computer vision. This research aims to describe the classification method applied to the dataset of multiple types of images (Synthetic Aperture Radar (SAR) images and non-SAR images). In such a classification, transfer learning was used followed by fine-tuning methods. Besides, pre-trained architectures were used on the known image database ImageNet. The model VGG16 was indeed used as a feature extractor and a new classifier was trained based on extracted features.The input data mainly focused on the dataset consist of five classes including the SAR images class (houses) and the non-SAR images classes (Cats, Dogs, Horses, and Humans). The Conv

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Publication Date
Wed May 10 2023
Journal Name
Journal Of Engineering
Effect of Petroleum Products on Steel Fiber Reinforced Concrete
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This Investigation aims to study the effect of adding Steel fibers with different volume fractions Vf (o.5, 0.75, and 1% by volume of concrete) with aspect ratio 100 on mechanical properties of concrete, and also
finding the influence of petroleum products (Kerosene and Diesel) on mechanical properties of Steel Fiber Reinforced Concrete (SFRC).
The experimental work consists of two groups: group one consists of specimens (cubes and prisms) plain and concrete reinforced with steel fiber exposed to continuous curing with water. Group two consists of
specimens (cubes and prisms) plain and concrete reinforced with steel fiber exposed to kerosene and diesel after curing them in water for 28 days before exposure. The results of all te

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Publication Date
Sat Aug 03 2024
Journal Name
Proceedings Of Ninth International Congress On Information And Communication Technology
Offline Signature Verification Based on Neural Network
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The investigation of signature validation is crucial to the field of personal authenticity. The biometrics-based system has been developed to support some information security features.Aperson’s signature, an essential biometric trait of a human being, can be used to verify their identification. In this study, a mechanism for automatically verifying signatures has been suggested. The offline properties of handwritten signatures are highlighted in this study which aims to verify the authenticity of handwritten signatures whether they are real or forged using computer-based machine learning techniques. The main goal of developing such systems is to verify people through the validity of their signatures. In this research, images of a group o

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Publication Date
Sun Mar 01 2020
Journal Name
Sustainable Chemistry And Pharmacy
A sustainable approach to utilize olive pips for the sorption of lead ions: Numerical modeling with aid of artificial neural network
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Publication Date
Thu Sep 01 2016
Journal Name
Journal Of Engineering
Application of Artificial Neural Network for Predicting Iron Concentration in the Location of Al-Wahda Water Treatment Plant in Baghdad City
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Iron is one of the abundant elements on earth that is an essential element for humans and may be a troublesome element in water supplies.  In this research an AAN model was developed to predict iron concentrations in the location of Al- Wahda water treatment plant in Baghdad city by water quality assessment of iron concentrations at seven WTPs up stream Tigris River. SPSS software was used to build the ANN model. The input data were iron concentrations in the raw water for the period 2004-2011. The results indicated the best model predicted Iron concentrations at Al-Wahda WTP with a coefficient of determination 0.9142. The model used one hidden layer with two nodes and the testing error was 0.834. The ANN model coul

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