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Modeling and Control of Fuel Cell Using Artificial Neural Networks
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This paper includes an experimental study of hydrogen mass flow rate and inlet hydrogen pressure effect on the fuel cell performance. Depending on the experimental results, a model of fuel cell based on artificial neural networks is proposed. A back propagation learning rule with the log-sigmoid activation function is adopted to construct neural networks model. Experimental data resulting from 36 fuel cell tests are used as a learning data. The hydrogen mass flow rate, applied load and inlet hydrogen pressure are inputs to fuel cell model, while the current and voltage are outputs. Proposed model could successfully predict the fuel cell performance in good agreement with actual data. This work is extended to developed fuel cell feedback control system using PID controller to stabilize the fuel cell voltage. Particle swarm optimization technique is used to tune the PID controller gains. The voltage error and hydrogen flow rate are input and the actuator of the PID controller respectively. Simulation results showed that using PID controller with proposed model of fuel cell can successfully improve system performance in tracking output voltage under different operating conditions.

 

 

 

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Publication Date
Sun Sep 03 2017
Journal Name
Baghdad Science Journal
Calculation of Lambda Doubling for 7LiH1 molecule Using the System band emission for electronic transmissions
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The A2?u-X1?g+ emission band system of 7LiH1 molecule has been calculated for Lambda doubling. The relation between wave number ?p , ?Q , ?R conducted the energies of the state of rotation F (J), and (J + 1) with rotational quantum number J, respectively, of 7LiH1 molecule for statehood A2?u using the rotation, fixed vibrational states of both the ground and raised crossovers vibrational against ???= 0 to V ' = 0-4using rotational levels J = 0 to J = 20 have found.

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Publication Date
Fri Jan 01 2021
Journal Name
Ieee Access
Total Dissolved Salt Prediction Using Neurocomputing Models: Case Study of Gypsum Soil Within Iraq Region
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Publication Date
Sun Mar 02 2014
Journal Name
Baghdad Science Journal
Rapid Detection of Aspergillus flavus isolates producing aflatoxin using UV light on different culture media
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This study included the isolation and identification of Aspergillus flavus isolates associated with imported American rice grains and local corn grains which collected from local markets, using UV light with 365 nm wave length and different media (PDA, YEA, COA, and CDA ). One hundred and seven fungal isolates were identified in rice and 147 isolates in corn.4 genera and 7 species were associated with grains, the genera were Aspergillus ,Fusarium ,Neurospora ,Penicillium . Aspergillus was dominant with occurrence of 0.47% and frequency of 11.75% in rice grains whereas in corn grains the genus Neurospora was dominant with occurrence of 1.09% and frequency 27.25% ,results revealed that 20 isolates out of 50 A. flavus isolates were able

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Publication Date
Sat Nov 15 2025
Journal Name
Journal Of Physical Education
Using Agility Exercises For Developing Some Refereeing Skills Of First Class Assistant Referees In Baghdad
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Agility is considered an important motor ability for assistant referees and it is vital for passing modern tests issued in September 2016. The researcher used the experimental method. The subjects were (10) referees; four of them for pilot study. The experimental group followed the exercises designed by the while the controlling group used the traditional exercises followed by the refereeing committee. All referees were tested the standardized agility test issued by the FIFA. The researchers concluded that agility plays a vital role in developing some refereeing skill. Finally they recommend developing agility for improving refereeing skills.

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Publication Date
Sun Sep 27 2020
Journal Name
Journal Of The College Of Education For Women
The Effect of Using Online Automated Feedback on Iraqi EFL Learners’ Writings at University Level
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Feedback on students’ assignments can be done in many different ways. Nowadays, the growing number of students at universities has increased the burden on the instructors to give feedback on students’ writings quickly and efficiently. As such, new methods of modern online automated feedback tools, such as Hemingway app and ecree,are used to assist and help instructors. Hence, this research is an explanatory study to examine the effect of using the online automated feedback on some Iraqi EFL learners’ writings at the university level. The study comprised 60 students enrolled in an English language course at the University of Anbar. They were divided randomly into two groups, experi

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Publication Date
Wed Nov 05 2014
Journal Name
International Journal Of Applied Mathematical Research
Approximate solution of a model describing biological species living together using a new iterative method
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Publication Date
Sat Dec 01 2018
Journal Name
Indian Journal Of Natural Sciences
Determination of the Electron Density Variation for Ionosphere Layer Over Iraqi Zone Using IRI Model
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KA Hadi, AH Asma’a, IJONS, 2018 - Cited by 1

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Publication Date
Mon Jan 01 2024
Journal Name
Baghdad Science Journal
Molecular Identification of Methylorubrum extorquens using PCR-Amplified MxaF Gene Fragments as A Molecular Marker
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  Methylotrophs bacteria are ubiquitous, and they have the ability to consume single carbon (C1) which makes them biological conversion machines. It is the first study to find facultative methylotrophic bacteria in contaminated soils in Iraq. Conventional PCR was employed to amplify MxaF that encodes methanol dehydrogenase enzyme. DNA templates were extracted from bacteria isolated from five contaminated sites in Basra. The gene specific PCR detected Methylorubrum extorquens as the most dominant species in these environments. The ability of M. extorquens to degrade aliphatic hydrocarbons compound was tested at the laboratory. Within 7 days, gas chromatographic (GC) studies of remaining utilize

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Publication Date
Sat Apr 30 2022
Journal Name
Eastern-european Journal Of Enterprise Technologies
Improvement of noisy images filtered by bilateral process using a multi-scale context aggregation network
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Deep learning has recently received a lot of attention as a feasible solution to a variety of artificial intelligence difficulties. Convolutional neural networks (CNNs) outperform other deep learning architectures in the application of object identification and recognition when compared to other machine learning methods. Speech recognition, pattern analysis, and image identification, all benefit from deep neural networks. When performing image operations on noisy images, such as fog removal or low light enhancement, image processing methods such as filtering or image enhancement are required. The study shows the effect of using Multi-scale deep learning Context Aggregation Network CAN on Bilateral Filtering Approximation (BFA) for d

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Publication Date
Wed Jan 01 2020
Journal Name
Periodicals Of Engineering And Natural Sciences
Forecasting the exchange rate of the Iraqi dinar against the US dollar using Markov chains
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