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A Neural Networks based Predictive Voltage-Tracking Controller Design for Proton Exchange Membrane Fuel Cell Model
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In this work, a new development of predictive voltage-tracking control algorithm for Proton Exchange Membrane Fuel Cell (PEMFCs) model, using a neural network technique based on-line auto-tuning intelligent algorithm was proposed. The aim of proposed robust feedback nonlinear neural predictive voltage controller is to find precisely and quickly the optimal hydrogen partial pressure action to control the stack terminal voltage of the (PEMFC) model for N-step ahead prediction. The Chaotic Particle Swarm Optimization (CPSO) implemented as a stable and robust on-line auto-tune algorithm to find the optimal weights for the proposed predictive neural network controller to improve system performance in terms of fast-tracking desired voltage and less energy consumption through investigating and comparing under random current variations with the minimum number of fitness evaluation less than 20 iterations.

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Publication Date
Fri Jan 01 2016
Journal Name
Diyala Journal For Pure Sciences
Synthesis, Characterization and Biological Activity for Complexes VO(II), Mn(II), Co(II) and Ni(II) With New Multidentate Ligand [2-((E)-3-(2-hydroxyphenylimino)-1,5-dimethyl-2-phenyl-2,3-dihydro-1H-pyrazol-4- ylimino)acetic Acid][H2L] type (N2).
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In this work, the precursor [2-(1,5-dimethyl-3-oxo-2-phenyl-2,3-dihydro-1H-pyrazol-4-ylimino)acetic acid] was synthesised from 4-aminoantipyrine and glyoxylic acid, this precursor has been used in the synthesis of new multidentate ligand [2-((E)-3-(2-hydroxyphenylimino)-1,5-dimethyl-2-phenyl-2,3-dihydro-1H-pyrazol-4-ylimino)acetic acid][H2L] type (N2O2). The ligand was refluxed in ethanol with metal ions [VO(II), Mn(II), Co(II) and Ni(II)] salts to give complexes of general molecular formula:[M(H2L)2(X)(Y)].B, where: M=VO(II), X=0, Y=OSO3-2, B=2H2O; M=Mn(II),Co(II) ,X=Cl, Y=Cl, B=0; M=Ni(II), X=H2O, Y=Cl, B=Cl. These complexes were characterised by atomic absorpition(A.A), F.T-I.R., (U.V-Vis)spectroscopies (1H,13C NMR for ligand only), alon

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Publication Date
Fri Dec 01 2023
Journal Name
Chemical Methodologies
Investigations on TiO<inf>2</inf>-NiO@In<inf>2</inf>O<inf>3</inf> Nanocomposite Thin Films (NCTFs) for Gas Sensing: Synthesis, Physical Characterization, and Detection of NO<inf>2</inf> and H<inf>2</inf>S Gas Sensors
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Publication Date
Sun Jan 01 2012
Journal Name
Advances In Materials Physics And Chemistry
The Effect of Zn Concentration on the Optical Properties of Cd&amp;lt;sub&amp;gt;10–x&amp;lt;/sub&amp;gt;Zn&amp;lt;sub&amp;gt;x&amp;lt;/sub&amp;gt;S Films for Solar Cells Applications
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Publication Date
Tue Sep 29 2020
Journal Name
Iraqi Journal Of Science
An Automated Classification of Mammals and Reptiles Animal Classes Using Deep Learning
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Detection and classification of animals is a major challenge that is facing the researchers. There are five classes of vertebrate animals, namely the Mammals, Amphibians, Reptiles, Birds, and Fish, and each type includes many thousands of different animals. In this paper, we propose a new model based on the training of deep convolutional neural networks (CNN) to detect and classify two classes of vertebrate animals (Mammals and Reptiles). Deep CNNs are the state of the art in image recognition and are known for their high learning capacity, accuracy, and robustness to typical object recognition challenges. The dataset of this system contains 6000 images, including 4800 images for training. The proposed algorithm was tested by using 1200

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Publication Date
Sun Jul 09 2023
Journal Name
Journal Of Engineering
Compression of an ECG Signal Using Mixed Transforms
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Electrocardiogram (ECG) is an important physiological signal for cardiac disease diagnosis. With the increasing use of modern electrocardiogram monitoring devices that generate vast amount of data requiring huge storage capacity. In order to decrease storage costs or make ECG signals suitable and ready for transmission through common communication channels, the ECG data
volume must be reduced. So an effective data compression method is required. This paper presents an efficient technique for the compression of ECG signals. In this technique, different transforms have been used to compress the ECG signals. At first, a 1-D ECG data was segmented and aligned to a 2-D data array, then 2-D mixed transform was implemented to compress the

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Publication Date
Tue Jun 01 2021
Journal Name
Journal Of The College Of Languages (jcl)
A comparative reading of the social and political satire in the works of Abu al-Fadl Zrui Nasrabad and Ahmad Rajab (The tadhk: خوانش تطبیقی طنز اجتماعی – سیاسی در آثار ابوالفضل زرویی نصرآباد و احمد رجب(مورد مطالعه: تذکرة المقامات زرویی و نصف كلمة احمد رجب)
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           Satire is genre of the literary arts that has always been the source of human interest. Because  it is difficult to accept direct criticism, Satire appears as a literary tool in which vices, follies, abuses and shortcomings are held up to ridicule, with the intent of shaming individuals, corporations, government, or society itself into improvement. A satirical critic usually employs irony to attain this goal. Although satire is usually meant to be humorous, its greater purpose is often profitable social criticism, using wit to draw at

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Publication Date
Wed Mar 01 2017
Journal Name
International Communications In Heat And Mass Transfer
Optimization, modeling and accurate prediction of thermal conductivity and dynamic viscosity of stabilized ethylene glycol and water mixture Al 2 O 3 nanofluids by NSGA-II using ANN
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In this study, multi-objective optimization of nanofluid aluminum oxide in a mixture of water and ethylene glycol (40:60) is studied. In order to reduce viscosity and increase thermal conductivity of nanofluids, NSGA-II algorithm is used to alter the temperature and volume fraction of nanoparticles. Neural network modeling of experimental data is used to obtain the values of viscosity and thermal conductivity on temperature and volume fraction of nanoparticles. In order to evaluate the optimization objective functions, neural network optimization is connected to NSGA-II algorithm and at any time assessment of the fitness function, the neural network model is called. Finally, Pareto Front and the corresponding optimum points are provided and

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Publication Date
Sat Jan 01 2022
Journal Name
International Journal Of Nonlinear Analysis And Applications
Human recognition by utilizing voice recognition and visual recognition
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Audio-visual detection and recognition system is thought to become the most promising methods for many applications includes surveillance, speech recognition, eavesdropping devices, intelligence operations, etc. In the recent field of human recognition, the majority of the research be- coming performed presently is focused on the reidentification of various body images taken by several cameras or its focuses on recognized audio-only. However, in some cases these traditional methods can- not be useful when used alone such as in indoor surveillance systems, that are installed close to the ceiling and capture images right from above in a downwards direction and in some cases people don't look straight the cameras or it cannot be added in some

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Publication Date
Sat Jun 01 2013
Journal Name
مجلة كلية بغداد للعلوم الاقتصادية الجامعة
Proposed family speech recognition
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Speech recognition is a very important field that can be used in many applications such as controlling to protect area, banking, transaction over telephone network database access service, voice email, investigations, House controlling and management ... etc. Speech recognition systems can be used in two modes: to identify a particular person or to verify a person’s claimed identity. The family speaker recognition is a modern field in the speaker recognition. Many family speakers have similarity in the characteristics and hard to identify between them. Today, the scope of speech recognition is limited to speech collected from cooperative users in real world office environments and without adverse microphone or channel impairments.

Publication Date
Sat Oct 30 2021
Journal Name
Iraqi Journal Of Science
Galaxy Morphological Image Classification using ResNet
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     Machine learning-based techniques are used widely for the classification of images into various categories. The advancement of Convolutional Neural Network (CNN) affects the field of computer vision on a large scale. It has been applied to classify and localize objects in images. Among the fields of applications of CNN, it has been applied to understand huge unstructured astronomical data being collected every second. Galaxies have diverse and complex shapes and their morphology carries fundamental information about the whole universe. Studying these galaxies has been a tremendous task for the researchers around the world. Researchers have already applied some basic CNN models to predict the morphological classes

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