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Öğe Analyzing the Impact of Temperature Variations on the Performance of Thermoelectric Generators(Institute of Electrical and Electronics Engineers Inc., 2023) Yahya, Adel E. M.; Sarreb, Rebha Daw; Yahya, Khalid; Attar, Hani; Aldababsa, Mahmoud; Hafez, MohamedRecent advancements in renewable energy technologies have brought Thermoelectric Generators (TEGs) to the forefront, primarily due to their ability to efficiently convert waste thermal energy into electrical power across multiple power levels. This study delves into the environmental benefits and operational efficiencies of TEGs, highlighting their zero-emission, silent operation, and low maintenance requirements. A significant portion of this research is dedicated to exploring the influence of temperature differential (?T) on the efficacy of TEGs, as temperature is a crucial factor in the energy conversion process. The electrical representation of TEGs is modeled as a voltage source in series with an internal resistance, while its thermal aspect comprises parallel-connected p- and n-type thermocouples. The study aims to meticulously analyze the behavior of TEG models under various thermal gradients and to scrutinize their electrical characteristics under different load conditions. This is achieved through comprehensive experimental methodologies, with the findings underscoring the impact of temperature variations on both hot and cold sides of TEGs on all electrical parameters. It is observed that an increase in the temperature difference results in a corresponding rise in both the maximum power output and the open-circuit voltage. In essence, the efficiency of TEGs is noted to improve with a higher and more stable temperature differential. © 2023 IEEE.Öğe Development of a Hybrid Support Vector Machine with Grey Wolf Optimization Algorithm for Detection of the Solar Power Plants Anomalies(Mdpi, 2023) Ahmed, Qais Ibrahim; Attar, Hani; Amer, Ayman; Deif, Mohanad A.; Solyman, Ahmed A. A.Solar energy utilization in the industry has grown substantially, resulting in heightened recognition of renewable energy sources from power plants and intelligent grid systems. One of the most important challenges in the solar energy field is detecting anomalies in photovoltaic systems. This paper aims to address this by using various machine learning algorithms and regression models to identify internal and external abnormalities in PV components. The goal is to determine which models can most accurately distinguish between normal and abnormal behavior of PV systems. Three different approaches have been investigated for detecting anomalies in solar power plants in India. The first model is based on a physical model, the second on a support vector machine (SVM) regression model, and the third on an SVM classification model. Grey wolf optimizer was used for tuning the hyper model for all models. Our findings will clarify that the SVM classification model is the best model for anomaly identification in solar power plants by classifying inverter states into two categories (normal and fault).Öğe Development of an Ultra-Sensitive Magnetic-Based Biosensor; a Simulation Study(Institute of Electrical and Electronics Engineers Inc., 2023) Yahya, Khalid; Husseini, Abbas Ali; Dirican, Onur; Attar, Hani; Aldababsa, Mahmoud; Hafez, MohamedThis study presents an advanced magnetic biosensor design incorporating an L-shaped ferromagnetic core with UL dimensions and an air gap replaced by highly porous aluminum or copper foam later-filled biological samples containing high-permeability ferromagnetic nanoparticles. The sensor detects specific biological molecules through magnetic field interactions. The system's electrical parameters were methodically optimized for enhanced performance. The research investigated the impact of various materials on the air gap's magnetic properties and assessed the relationships between permeability, output-induced voltage, input voltage, and input frequency. Findings indicate that using materials with higher magnetic permeability, such as Magnetite (Fe304) or Cobalt ferrite (CoFe2O4) ferrofluids, considerably improved the biosensor's performance by optimizing magnetic coupling between primary and secondary windings. This innovative magnetic biosensor holds potential for diverse applications, including medical diagnostics, environmental monitoring, and industrial process control. The study offers valuable insights into magnetic biosensor design and optimization, facilitating heightened sensitivity and selectivity in detecting target molecules. © 2023 IEEE.Öğe Diagnosis of Oral Squamous Cell Carcinoma Using Deep Neural Networks and Binary Particle Swarm Optimization on Histopathological Images: An AIoMT Approach(HINDAWI LTD, ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON W1T 5HF, ENGLAND, 2022) Deif, Mohanad A.; Attar, Hani; Amer, Ayman; Elhaty, Ismail A. M.; Khosravi, Mohammad R.; Solyman, Ahmad Amin AhmadOverall prediction of oral cavity squamous cell carcinoma (OCSCC) remains inadequate, as more than half of patients with oral cavity cancer are detected at later stages. It is generally accepted that the differential diagnosis of OCSCC is usually difficult and requires expertise and experience. Diagnosis from biopsy tissue is a complex process, and it is slow, costly, and prone to human error. To overcome these problems, a computer-aided diagnosis (CAD) approach was proposed in this work. A dataset comprising two categories, normal epithelium of the oral cavity (NEOR) and squamous cell carcinoma of the oral cavity (OSCC), was used. Feature extraction was performed from this dataset using four deep learning (DL) models (VGG16, AlexNet, ResNet50, and Inception V3) to realize artificial intelligence of medial things (AIoMT). Binary Particle Swarm Optimization (BPSO) was used to select the best features. The effects of Reinhard stain normalization on performance were also investigated. After the best features were extracted and selected, they were classified using the XGBoost. The best classification accuracy of 96.3% was obtained when using Inception V3 with BPSO. This approach significantly contributes to improving the diagnostic efficiency of OCSCC patients using histopathological images while reducing diagnostic costs.Öğe Evaluating the Performance of Fuzzy-PID Control for Lane Recognition and Lane-Keeping in Vehicle Simulations(MDPI, ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND, 2023) Moveh, Samuel; Yahya, Khalid O. Moh.; Attar, Hani; Amer, Ayman; Mohamed, Mahmoud; Badmos, Tajudeen AdelekeThis study presents the use of a vision-based fuzzy-PID lane-keeping control system for the simulation of a single-track bicycle model. The lane-keeping system (LKS) processes images to identify the lateral deviation of the vehicle from the desired reference track and generates a steering control command to correct the deviation. The LKS was compared to other lane-keeping control methods, such as Ziegler–Nichols proportional derivative (PD) and model predictive control (MPC), in terms of response time and settling time. The fuzzy-PID controller had the best performance, with fewer oscillations and a faster response time compared to the other methods. The PD controller was not as robust under various conditions due to changing parameters, while the MPC was not accurate enough due to similar reasons. However, the fuzzy-PID controller showed the best performance, with a maximum lateral deviation of 2 cm, a settling time of 12 s, and Kp and Kd values of 0.01 and 0.06, respectively. Overall, this work demonstrates the potential of using fuzzy-PID control for effective lane recognition and lane-keeping in vehicles.Öğe Hyperparameter Optimization of Regression Model for Electrical Load Forecasting During the COVID-19 Pandemic Lockdown Period(Intelligent Network and Systems Society, 2023) Al-azzawi, Saif Mohammed; Deif, Mohanad A.; Attar, Hani; Amer, Ayman; Solyman, Ahmed A. A.Due to global lockdown policies implemented against COVID-19, there has been an impact on electricity consumption. Several countries have emphasized the significance of ensuring electricity supply security during the pandemic to maintain the livelihood of people. Accurate forecasting of electricity demand plays a crucial role in ensuring energy security across all nations; accordingly to achieve this objective, this study employs metaheuristics optimization algorithms to enhance the prediction model's operation, such as Support Vector Machine (SVM), KNearest Neighbors (KNN), and Random Forest (RF), at an optimized level to minimize errors. Two metaheuristics optimization methods, Particle Swarm Optimization (PSO) and Genetic Algorithms (GA), are utilized. The suggested prediction models are trained using daily power usage data from three US urban regions. In terms of prediction accuracy, the findings show that KNN with PSO surpasses the other models. The COVID-19 pandemic reduced power usage by 20% relative to pre-pandemic levels. © 2023, International Journal of Intelligent Engineering and Systems. All Rights Reserved.Öğe Investigating and calculating the temperature of hot-spot factor for transformers(Institute of Advanced Engineering and Science, 2023) Yahya, Khalid; Attar, Hani; Issa, Haitham; Ramadan Dofan, Jamal Ali; Iqteit, Nassim A.; Yahya, Adel E.M.; Solyman, Ahmed Amin AhmedThis article explores the measurement of temperature in transient states, utilizing the principles of heat transfer and thermal-electrical metaphor. The study focuses on the nonlinear thermal resistances present in various locations within a distribution transformer, while taking into account variations in oil physical variables and temperature loss. Real-time data obtained from heat run tests on a 250-MVA-ONAF cooled unit, conducted by the transformer manufacturer, is used to verify the thermal designs. The observations are then compared to the loading framework of the IEC 60076-7:2005 system. The findings of this research provide a better understanding of temperature measurement in transient states, particularly in distribution transformers, and can be applied to the design and development of more efficient and reliable transformer systems. © 2023 Institute of Advanced Engineering and Science. All rights reserved.Öğe A Low-Complexity Equalizer for Video Broadcasting in Cyber-Physical Social Systems Through Handheld Mobile Devices(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA, 2020) Solyman, Ahmad Amin Ahmad; Attar, Hani; Khosravi, Mohammad R.; Menon, Varun G.; Jolfaei, Alireza; Balasubramanian, Venki; Selvaraj, Buvana; Tavallali, PooyaIn Digital Video Broadcasting-Handheld (DVB-H) devices for cyber-physical social systems, the Discrete Fractional Fourier Transform-Orthogonal Chirp Division Multiplexing (DFrFT-OCDM) has been suggested to enhance the performance over Orthogonal Frequency Division Multiplexing (OFDM) systems under time and frequency-selective fading channels. In this case, the need for equalizers like the Minimum Mean Square Error (MMSE) and Zero-Forcing (ZF) arises, though it is excessively complex due to the need for a matrix inversion, especially for DVB-H extensive symbol lengths. In this work, a low complexity equalizer, Least-Squares Minimal Residual (LSMR) algorithm, is used to solve the matrix inversion iteratively. The paper proposes the LSMR algorithm for linear and nonlinear equalizers with the simulation results, which indicate that the proposed equalizer has significant performance and reduced complexity over the classical MMSE equalizer and other low complexity equalizers, in time and frequency-selective fading channels.Öğe MIMO-OFDM/OCDM low-complexity equalization under a doubly dispersive channel in wireless sensor networks(SAGE PUBLICATIONS INC, 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA, 2020) Solyman, Ahmad Amin Ahmad; Attar, Hani; Khosravi, Mohammad R.; Koyuncu, BakiIn this article, three novel systems for wireless sensor networks based on Alamouti decoding were investigated and then compared, which are Alamouti space-time block coding multiple-input single-output/multiple-input multiple-output multicarrier modulation (MCM) system, extended orthogonal space-time block coding multiple-input single-output MCM system, and multiple-input multiple-output system. Moreover, the proposed work is applied over multiple-input multiple-output systems rather than the conventional single-antenna orthogonal chirp division multiplexing systems, based on the discrete fractional cosine transform orthogonal chirp division multiplexing system to mitigate the effect of frequency-selective and time-varying channels, using low-complexity equalizers, specifically by ignoring the intercarrier interference coming from faraway subcarriers and using the LSMR iteration algorithm to decrease the equalization complexity, mainly with long orthogonal chirp division multiplexing symbols, such as the TV symbols. The block diagrams for the proposed systems are provided to simplify the theoretical analysis by making it easier to follow. Simulation results confirm that the proposed multiple-input multiple-output and multiple-input single-output orthogonal chirp division multiplexing systems outperform the conventional multiple-input multiple-output and multiple-input single-output orthogonal frequency division multiplexing systems. Finally, the results show that orthogonal chirp division multiplexing exhibited a better channel energy behavior than classical orthogonal frequency division multiplexing, thus improving the system performance and allowing the system to decrease the equalization complexity.Öğe Modeling and computational fluid dynamics simulation of blood flow behavior based on MRI and CT for Atherosclerosis in Carotid Artery(Springer, 2023) Attar, Hani; Ahmed, Tasneem; Rabie, Rahma; Amer, Ayman; Khosravi, Mohammad R.; Solyman, Ahmed; Deif, Mohanad. A.Carotid atherosclerosis is one of the main cardiovascular diseases, widely considered as the main reason for death. Atherosclerosis forms a plaque that impedes blood vessels, and if ruptured, it causes a stroke or heart attack. The treatment protocol for atherosclerosis depends heavily on plaque type, structure, and composition, affecting plaque behavior (stable/unstable) or vulnerability. The fluid-structure interaction between the blood vessels and the blood flow must be examined to study the plaque's behavior. Consequently, this paper aims to reconstruct patient-specific three-dimensional models of the blood vessels for simulation and three-dimensional (3D) Printing, particularly for the carotid artery. In addition, Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) datasets of atherosclerotic vessels are used to reconstruct the 3D model fed into a simulation program to measure the stress, strain, pressure, and velocity to assess the plaque. Five analyses studies were conducted on the constructed blood vessels; Non-pathological Flow in a cylindrical artery, Pathological Flow in a cylindrical artery, Non-pathological Flow in a 2D bifurcating carotid artery, Blood flow analysis in a normal carotid artery, and Blood flow analysis in a stenosed carotid artery. Two validation studies were performed for normal and atherosclerotic arteries. The results agreed with previous well-published work, considering that a 3D realistic model was printed for the vessel. Based on the above, our work provides a simulation environment for predicting atherosclerotic plaque behavior that helps medical specialists choose the proper treatment and preventive medical plans.Öğe A New Feature Selection Method Based on Hybrid Approach for Colorectal Cancer Histology Classification(Hindawi Limited, 2022) Deif, Mohanad A.; Attar, Hani; Amer, Ayman; Issa, Haitham; Khosravi, Mohammad R.; Solyman, Ahmed A. A.Colorectal cancer (CRC) is one of the most common malignant cancers worldwide. To reduce cancer mortality, early diagnosis and treatment are essential in leading to a greater improvement and survival length of patients. In this paper, a hybrid feature selection technique (RF-GWO) based on random forest (RF) algorithm and gray wolf optimization (GWO) was proposed for handling high dimensional and redundant datasets for early diagnosis of colorectal cancer (CRC). Feature selection aims to properly select the minimal most relevant subset of features out of a vast amount of complex noisy data to reach high classification accuracy. Gray wolf optimization (GWO) and random forest (RF) algorithm were utilized to find the most suitable features in the histological images of the human colorectal cancer dataset. Then, based on the best-selected features, the artificial neural networks (ANNs) classifier was applied to classify multiclass texture analysis in colorectal cancer. A comparison between the GWO and another optimizer technique particle swarm optimization (PSO) was also conducted to determine which technique is the most successful in the enhancement of the RF algorithm. Furthermore, it is crucial to select an optimizer technique having the capability of removing redundant features and attaining the optimal feature subset and therefore achieving high CRC classification performance in terms of accuracy, precision, and sensitivity rates. The Heidelberg University Medical Center Pathology archive was used for performance check of the proposed method which was found to outperform benchmark approaches. The results revealed that the proposed feature selection method (GWO-RF) has outperformed the other state of art methods where it achieved overall accuracy, precision, and sensitivity rates of 98.74%, 98.88%, and 98.63%, respectively. © 2022 Mohanad A. Deif et al.Öğe Prediction of Wear Rates of UHMWPE Bearing in Hip Joint Prosthesis with Support Vector Model and Grey Wolf Optimization(Hindawi Limited, 2022) Hammam, Rania E.; Attar, Hani; Amer, Ayman; Issa, Haitham; Vourganas, Ioannis; Solyman, Ahmed; Venu, P.One of the greatest challenges in joint arthroplasty is to enhance the wear resistance of ultrahigh molecular weight polyethylene (UHMWPE), which is one of the most successful polymers as acetabular bearings for total hip joint prosthesis. In order to improve UHMWPE wear rates, it is necessary to develop efficient methods to predict its wear rates in various conditions and therefore help in improving its wear resistance, mechanical properties, and increasing its life span inside the body. This article presents a support vector machine using a grey wolf optimizer (SVM-GWO) hybrid regression model to predict the wear rates of UHMWPE based on published polyethylene data from pin on disc (PoD) wear experiments typically performed in the field of prosthetic hip implants. The dataset was an aggregate of 29 different PoD UHMWPE datasets collected from Google Scholar and PubMed databases, and it consisted of 129 data points. Shapley additive explanations (SHAP) values were used to interpret the presented model to identify the most important and decisive parameters that affect the wear rates of UHMWPE and, therefore, predict its wear behavior inside the body under different conditions. The results revealed that radiation doses had the highest impact on the model's prediction, where high values of radiation doses had a negative impact on the model output. The pronounced effect of irradiation doses and surface roughness on the wear rates of polyethylene was clear in the results when average disc surface roughness Ra values were below 0.05 ?m, and irradiation doses were above 95 kGy produced 0 mg/MC wear rate. The proposed model proved to be a reliable and robust model for the prediction of wear rates and prioritizing factors that most significantly affect its wear rates. The proposed model can help material engineers to further design polyethylene acetabular linings via improving the wear resistance and minimizing the necessity for wear experiments. © 2022 Rania E. Hammam et al.Öğe The Role of Smart Environment Initiatives on Environmental Degradation: Consolidating the Resilient Built Landscape(Institute of Electrical and Electronics Engineers Inc., 2022) Agboola, Oluwagbemiga Paul; Moveh, Samuel; Yahya, Khalid; Attar, Hani; Amer, AymanPrimarily in industrialized and some developing nations, the adoption of the smart city approach as a sustainable approach to the management and implementation of infrastructure developments has been rising. Initiatives to build resilience are critical for successful cities in these developing nations, like Nigeria. This research explores the various advantages of building resilience in Nigeria's smart cities in light of this growth. Few scholars have examined the building and smart city efforts aimed at enhancing Nigeria's built environment's sustainability in the context of the current global environmental issues. Thus, this study closes the gaps by evaluating the various aspects of building and the city's resilience with a focus on Lagos, the most populated metropolis in Africa. The following issues are covered with reviews of the literature: (i) the current Lagos Smart City projects; (ii) Smart City and Building initiatives in Nigeria; and (iii) the goal of robust resilience. By succinctly describing the strategic planning for smart city development, in addition to the opportunities discovered in the smart city and building initiatives, this paper contributes to the conversation around smart cities. This will aid in the documentation, forecasting, and future decision-making processes for Nigeria's Smart Cities. © 2022 IEEE.Öğe Simple Mathematical and Simulink Model of Stepper Motor(MDPI, ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND, 2022) Iqteit, Nassim A.; Yahya, Khalid O. Moh.; Makahleh, Firas M.; Attar, Hani; Amer, Ayman; Solyman, Ahmad Amin Ahmad; Qudaimat, Ahmad; Tamizi, KhaledThis paper presents a simple mathematical and Simulink model of a two-phase hybrid stepper motor, where ignoring the permeance space harmonics of the hybrid stepper motor is regarded as the main physical assumption in this article. Moreover, the dq transformation method is adopted as the main mathematical approach for the derivation of the proposed model, where simple voltages, currents, and torque equations are obtained and used to build the proposed Simulink and circuit model of the stepper motor. The validity and the effectiveness of the proposed model are examined by comparing its results with the results collected from the Simulink model in the library of Matlab. The obtained simulation results showed that the proposed model achieved a high simplicity and high accuracy when compared with conventional models.
