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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
Sun Dec 03 2017
Journal Name
Iosr Journal Of Pharmacy And Biological Sciences
Relationship between Lipid Peroxidation and Integrity of Sperm Plasma Membrane in a Sample of Iraqi Infertile Men
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The current study aims to investigate the relationship between lipid peroxidation, represented by seminal malondialdehyde (MDA) level, and integrity of sperm plasma membrane, represented by the hypo-osmotic swelling (HOS) test score, in infertile men. One hundred and twenty Iraqi men (20 fertile and 100 infertile) shared in this study during their attendance to the High Institute for Infertility Diagnosis and Assisted Reproductive Technologies/Al- Nahrain University. The mean age of the fertile men was (30.46±0.78) years while the mean age of infertile men was (32.58±0.64) years with the duration of infertility (5.36±0.33) years. Semen samples were collected and the parameters of seminal fluid analysis were assessed; also, the HOS

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Publication Date
Tue Dec 01 2020
Journal Name
Journal Of Engineering
Performance of 2- Link Robot by utilizing Adaptive Sliding Mode Controller
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The Sliding Mode Control (SMC) has been among powerful control techniques increasingly. Much attention is paid to both theoretical and practical aspects of disciplines due to their distinctive characteristics such as insensitivity to bounded matched uncertainties, reduction of the order of sliding equations of motion, decoupling mechanical systems design. In the current study, two-link robot performance in the Classical SMC is enhanced via Adaptive Sliding Mode Controller (ASMC) despite uncertainty, external disturbance, and coulomb friction. The key idea is abstracted as follows: switching gains are depressed to the low allowable values, resulting in decreased chattering motion and control's efforts of the two-link robo

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Publication Date
Tue Oct 01 2019
Journal Name
Journal Of Engineering
Removal of Dissolved Organic Compounds and Contaminants from Wastewater of a Petroleum Refinery by Ion Exchange
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The efficient removal of dissolved organic compounds (DOC) from wastewater has become a major environmental concern because of its high toxicity even at low concentrations. Therefore, a technique was needed to reduce these pollutants. Ion exchange technology (IE) was used with AmberliteTM IR120 Na, AmberliteTM IR96RF, and AmberliteTM IR402, firstly by using anion and mixed bed system, where the following variables are investigated for the process of adsorption: The height of the bed in column (8,10 and 14 cm), different concentrations of (DOC) content at constant flow rate. The use of an ion exchanger unit (continuous system) with three columns (cation, anion, and mixed bed) was studied.

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Publication Date
Mon Oct 03 2022
Journal Name
International Journal Of Interactive Mobile Technologies (ijim)
A New Feature-Based Method for Similarity Measurement under the Linux Operating System
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This paper presents a new algorithm in an important research field which is the semantic word similarity estimation. A new feature-based algorithm is proposed for measuring the word semantic similarity for the Arabic language. It is a highly systematic language where its words exhibit elegant and rigorous logic. The score of sematic similarity between two Arabic words is calculated as a function of their common and total taxonomical features. An Arabic knowledge source is employed for extracting the taxonomical features as a set of all concepts that subsumed the concepts containing the compared words. The previously developed Arabic word benchmark datasets are used for optimizing and evaluating the proposed algorithm. In this paper,

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Publication Date
Sat Aug 01 2020
Journal Name
Computational Biology And Chemistry
A graph-based multi-sample test for identifying pathways associated with cancer progression
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Cancer is in general not a result of an abnormality of a single gene but a consequence of changes in many genes, it is therefore of great importance to understand the roles of different oncogenic and tumor suppressor pathways in tumorigenesis. In recent years, there have been many computational models developed to study the genetic alterations of different pathways in the evolutionary process of cancer. However, most of the methods are knowledge-based enrichment analyses and inflexible to analyze user-defined pathways or gene sets. In this paper, we develop a nonparametric and data-driven approach to testing for the dynamic changes of pathways over the cancer progression. Our method is based on an expansion and refinement of the pathway bei

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Publication Date
Tue Apr 30 2024
Journal Name
Iraqi Journal Of Science
Topology-Based Modularity and Modularity Density for Detecting Protein Complexes: A Comparative Study
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     Binary relations or interactions among bio-entities, such as proteins, set up the essential part of any living biological system. Protein-protein interactions are usually structured in a graph data structure called "protein-protein interaction networks" (PPINs). Analysis of PPINs into complexes tries to lay out the significant knowledge needed to answer many unresolved questions, including how cells are organized and how proteins work. However, complex detection problems fall under the category of non-deterministic polynomial-time hard (NP-Hard) problems due to their computational complexity. To accommodate such combinatorial explosions, evolutionary algorithms (EAs) are proven effective alternatives to heuristics in solvin

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Publication Date
Tue Aug 06 2013
Journal Name
Robotica
Function approximation technique-based adaptive virtual decomposition control for a serial-chain manipulator
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SUMMARY<p>The virtual decomposition control (VDC) is an efficient tool suitable to deal with the full-dynamics-based control problem of complex robots. However, the regressor-based adaptive control used by VDC to control every subsystem and to estimate the unknown parameters demands specific knowledge about the system physics. Therefore, in this paper, we focus on reorganizing the equation of the VDC for a serial chain manipulator using the adaptive function approximation technique (FAT) without needing specific system physics. The dynamic matrices of the dynamic equation of every subsystem (e.g. link and joint) are approximated by orthogonal functions due to the minimum approximation errors produced. The contr</p> ... Show More
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Publication Date
Mon Aug 01 2022
Journal Name
Baghdad Science Journal
A Novel Technique for Secure Data Cryptosystem Based on Chaotic Key Image Generation
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The advancements in Information and Communication Technology (ICT), within the previous decades, has significantly changed people’s transmit or store their information over the Internet or networks. So, one of the main challenges is to keep these information safe against attacks. Many researchers and institutions realized the importance and benefits of cryptography in achieving the efficiency and effectiveness of various aspects of secure communication.This work adopts a novel technique for secure data cryptosystem based on chaos theory. The proposed algorithm generate 2-Dimensional key matrix having the same dimensions of the original image that includes random numbers obtained from the 1-Dimensional logistic chaotic map for given con

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Publication Date
Sat Dec 30 2023
Journal Name
Traitement Du Signal
Optimizing Acoustic Feature Selection for Estimating Speaker Traits: A Novel Threshold-Based Approach
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Publication Date
Thu Jun 01 2023
Journal Name
Bulletin Of Electrical Engineering And Informatics
A missing data imputation method based on salp swarm algorithm for diabetes disease
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Most of the medical datasets suffer from missing data, due to the expense of some tests or human faults while recording these tests. This issue affects the performance of the machine learning models because the values of some features will be missing. Therefore, there is a need for a specific type of methods for imputing these missing data. In this research, the salp swarm algorithm (SSA) is used for generating and imputing the missing values in the pain in my ass (also known Pima) Indian diabetes disease (PIDD) dataset, the proposed algorithm is called (ISSA). The obtained results showed that the classification performance of three different classifiers which are support vector machine (SVM), K-nearest neighbour (KNN), and Naïve B

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