Manchester, CT, United States of America

Mahmoud El Chamie


Average Co-Inventor Count = 2.0

ph-index = 1


Company Filing History:


Years Active: 2023

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1 patent (USPTO):Explore Patents

Title: Mahmoud El Chamie: Innovator in Matrix Converter Control

Introduction

Mahmoud El Chamie is an accomplished inventor based in Manchester, Connecticut. He has made significant contributions to the field of electrical engineering, particularly in the control of matrix converters using advanced machine learning techniques. His innovative approach has the potential to enhance the efficiency and performance of power conversion systems.

Latest Patents

Mahmoud holds a patent for a method titled "Control of matrix converters using machine learning." This patent describes a sophisticated method for controlling a matrix converter system. The method involves receiving an operating condition and consulting a trained Q-data structure for reward values associated with respective switching states of the switching matrix. The Q-data structure is trained using Q-learning to map predicted reward values for respective switching states to discrete operating states. The process includes sorting the reward values, selecting a subset of mappings, evaluating each switching state, and ultimately selecting an optimal switching state based on the evaluation results.

Career Highlights

Mahmoud El Chamie is currently employed at Hamilton Sundstrand Corporation, where he applies his expertise in electrical engineering and machine learning. His work focuses on developing innovative solutions that improve the performance of power systems. His dedication to research and development has positioned him as a key contributor in his field.

Collaborations

Mahmoud collaborates with Vladimir Blasko, a fellow engineer, to further advance their projects and share insights in the realm of matrix converter technology. Their partnership exemplifies the importance of teamwork in driving innovation.

Conclusion

Mahmoud El Chamie's contributions to the field of matrix converter control through machine learning demonstrate his commitment to advancing technology. His innovative patent and collaborative efforts highlight the impact of his work in electrical engineering.

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