ELEC Doctor of Philosophy Dissertation Defense by Priscila de Paula Silva-ECE
Topic: Predictive System Resilience Modeling
Abstract:
Resilience is the ability of a system to respond, absorb, and recover from disruptive events, applying to various domains. Traditional resilience models and metrics primarily focus on retrospective analysis to assess system performance post-disruption and inform future design and operation decisions. However, but do not project when the system will recover to a specified level of performance or what actions to take in order to reach a target level of performance quickly and cost effectively. To address the limitation of past research, this dissertation develops advanced predictive resilience models that utilize regression and time series analysis to forecast system performance, enhancing decision-making processes for system resilience. These models offer a general framework applicable across different fields, improving upon previous methods by enabling predictions of recovery timelines and optimal recourse allocation strategies. These models are validated using diverse datasets, including economic, cybersecurity, and radar systems, to demonstrate their applicability and effectiveness across different domains. Results show that mixture models allow greater flexibility and achieve higher accuracy in prediction, alongside optimized resource allocation strategies that ensure desired performance within specified constraints. These advancements mark a significant enhancement in resilience engineering, providing robust models for both predicting and managing the resilience of systems facing multiple disruptions.
Advisor: Dr. Lance Fiondella, Associate Professor, Dept. of Electrical & Computer Engineering, UMass Dartmouth
Committee Members:
Dr. Ruolin Zhou, Associate Professor, Dept. of Electrical & Computer Engineering, UMass Dartmouth;
Dr. Gokhan Kul, Assistant Professor, Dept. of Computer & Information Science, UMass Dartmouth;
Ms. Mindy Hotchkiss, Technical Statistics Specialist, Aerojet Rocketdyne
NOTE: All ECE Graduate Students are ENCOURAGED to attend.
All interested parties are invited to attend. Open to the public.
*For further information, please contact Dr. Lance Fiondella at 508.999.8596 or via email.
Charlton College of Business (CCB), Room 115 / Zoom Link: https://umassd.zoom.us/j/95490897965 Meeting ID: 954 9089 7965 Passcode: 792504
Lance Fiondella
5089998596
lfiondella@umassd.edu