Browsing by Author "Zohdy, Mohamed A."
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Item Genetic Algorithms(Oakland University, 2002-10-01) Zohdy, Mohamed A.; Brieger, GottfriedGenetic algorithms and their recent variants that add more features such as viability (survival) and fertility (reproduction), are powerful global random search computational tools with a variety of potential applications especially to intelligent and autonomous systemsItem Nonlinear Discrete-Time Control of Modern Power Converters with Robust Adaptive Observer(2023-01-01) Hernandez, Mauricio E.; Zohdy, Mohamed A.; Kruk, Serge; Ganesan, Subra; Edwards, WilliamControl systems are an integral part of modern society, crucial to the performance of these systems is the accurate mathematical model representation of the physical systems. However, every physical system is subject to external factors that over time can change its characteristics. Therefore, to adapt the mathematical model representation of the plant over time a State Observer or State Estimator is often used, which would be the main topic of my research. A ZETA Power converter is a type of switch mode power supply that offers high efficiency in addition to various advantages, among some is its low output ripple which is desired for powering modern VLSI electronics. However due to its composition (non-Linear and fourth order), controlling the dynamics could be complex. With the advances in microprocessing and their economical affordability now, implementing the control scheme of a Zeta converter in the discrete time domain makes it an ideal solution for control implementation, given the lack of research regarding discrete-time control for Zeta Power Converters, state space observers, and adaptive state estimation, this dissertation research aims to identify a novel way of controlling Zeta.Item Novel Piezoelectric Biosensor Based On Sars-Cov-2 (Covid-19) Spike Antibody For Coronavirus (Covid-19) Detection(2022-07-17) Alromithy, Fares Sulaimin; Zohdy, Mohamed A.; Auner, Gregory; Kamel-ElSayed, Suzan; Das, Manohar; Kaur, AmanpreetAt the end of December 2019, the novel coronavirus SARS_CoV_2 appeared in Wuhan, China. The World Health Organization released a global health emergency declaration based on growing case notification rates in several locations worldwide. Therefore, sensitive, specific, rapid, and deliverable diagnostic monitoring is vital for making proper decisions on treating and isolating infected patients, which will help prevent the spread of infectious diseases. The surface Acoustic Wave (SAW) biosensor provides a unique, highly sensitive electrical approach to biomolecule detection and cell growth. For this study, a novel SAW sensor is developed, and the mass sensitivities are tested to detect the SARS_CoV_2 by attaching the SARS-CoV-2 spike antibody immobilized on the sensor surface. First, a two-dimensional (2D) and a three-dimensional (3D) finite element model were developed based on a realistic device to obtain a complete characterization of the senor. Then, the AlN/Al2O3 fabricated sensor was tested and ultrasonically rinsed in preparation for silanization. After depositions of (APTMS) on the sensor by the Chemical Vapor Deposition method, the antibodies were immobilized on surfaces with the aid of a crosslinker (EDC) and (Sulfo-NHS). Finally, the SARS-CoV-2 was introduced to the sensor, and the attachment of the immobilized antibody was tested and evaluated. The sensor was tested and characterized by Raman spectroscopy and the vector Network Analyzer. Finally, our device was able to detect the virus in real-time time (within two to three minutes), confirming its high sensitivity and selectivity with regard to the SARS-CoV-2 virus.Item Resilient Suppliers Selection System Using Machine Learning Algorithms And Risk Assessment Methodology(2022-07-02) Albadrani, Abdullah Meteb; Zohdy, Mohamed A.; Olawoyin, Richard; Edwards, William; Ruegg, EricaDemand, supply, pricing, and lead time are all unpredictable in the manufacturingindustry, and the manufacturer must function in this environment. Because of the enormous amount of data available and the introduction of new technologies, such as theinternet of things (IoT), machine learning (ML), and Blockchain, administrators and government officials are better able to deal with uncertainty by applying intelligent decision-making principles to their situations. All supply chains must make use of new technology and analyze previous data to forecast and improve the success of future operations. At the moment, we rely on the supply chain and its facilities for healthcare when we receive our vaccine, for our food when we go grocery shopping, and for transportation when we drive our cars. Supplier selection is exposed to the three most significant factors: quality, delivery, and performance history, which are all evaluated separately. The use of data analytic capabilities in the selection of robust supplier portfolios has not been thoroughly investigated. Manufacturers typically have three to four resilient suppliers for the same item, but occasionally one or two of them will fail, causing a ripple effect throughout the entire supply chain. This is a frequent problem that the supply chain must deal with on a daily basis. Supply chain resilience, on the other hand, ignores or is incompatible with the risk profiles of suppliers' performance.Item Resilient Suppliers Selection System Using Machine Learning Algorithms and Risk Assessment Methodology(2022-01-01) Albadrani, Abdullah Meteb; Zohdy, Mohamed A.; Edwards, William; Ruegg, EricaDemand, supply, pricing, and lead time are all unpredictable in the manufacturingindustry, and the manufacturer must function in this environment. Because of the enormous amount of data available and the introduction of new technologies, such as the internet of things (IoT), machine learning (ML), and Blockchain, administrators and government officials are better able to deal with uncertainty by applying intelligent decision-making principles to their situations. All supply chains must make use of new technology and analyze previous data to forecast and improve the success of future operations. At the moment, we rely on the supply chain and its facilities for healthcare when we receive our vaccine, for our food when we go grocery shopping, and for transportation when we drive our cars. Supplier selection is exposed to the three most significant factors: quality, delivery, and performance history, which are all evaluated separately. The use of data analytic capabilities in the selection of robust supplier portfolios has not been thoroughly investigated. Manufacturers typically have three to four resilient suppliers for the same item, but occasionally one or two of them will fail, causing a ripple effect throughout the entire supply chain. This is a frequent problem that the supply chain must deal with on a daily basis. Supply chain resilience, on the other hand, ignores or is incompatible with the risk profiles of suppliers’ performance.