Integrated Learning and Optimization and Computationally Efficient Model Predictive Control for Power Systems Applications Imran Pervez, Ph.D. Student, Electrical and Computer Engineering Oct 4, 17:00 - 19:00 B4 R5209; Zoom Meeting 96126193341 MCP power systems integrated machine learning optimization Advanced control systems engineering This dissertation studies novel model predictive control and integrated learning-optimization methodologies that enable computationally efficient, robust, and scalable real-time control and decision-making in modern power systems, improving renewable microgrid operation, electricity market performance, and optimization under uncertainty.
Azimuthal Super-Resolution Imaging of Ferromagnetic Tubulars: Deep Neural Network-Based Electromagnetic Inversion Guang An Ooi, Ph.D. Student, Electrical and Computer Engineering May 2, 09:00 - 11:30 B4 L5 R5209 The structural integrity of wellbore casings and transportation pipelines is a critical aspect in the oil and gas industry for operational efficiency and environmental safety. Traditional non-destructive testing methods, while effective, face significant challenges in accurately assessing and monitoring these crucial infrastructural components, especially under harsh operating environments. Furthermore, the inner volume of these tubular structures and the flow speed of transported liquids pose additional difficulties upon the performance of devices designed to inspect their structures for defects and deformations.
