Browsing by Author "Zohdy, Mohamed A."

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    Genetic Algorithms
    (Oakland University, 2002-10-01) Zohdy, Mohamed A.; Brieger, Gottfried
    Genetic 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 systems
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    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, William
    Control 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 Converters using adaptive state observers and state feedback in the discrete-time domain that yields robustness in the presence of plant uncertainties that meets certain criteria. The results documented in this dissertation confirm but also outline the limitations of such nonlinear discrete-time domain controller state-feedback from a robust and adaptive full-state observer/estimator
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    Resilient Suppliers Selection System Using Machine Learning Algorithms and Risk Assessment Methodology
    (2022-01-01) Albadrani, Abdullah Meteb; Zohdy, Mohamed A.; Edwards, William; Ruegg, Erica
    Demand, 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.