Saudi Cultural Missions Theses & Dissertations
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Item Restricted CAUSAL LEARNING IN UNMANNED/AUTONOMOUS VEHICLE DYNAMICS(Saudi Digital Library, 2023-11-23) Alwalan, Abdulaziz Abdulmohsen; Arana-Catania, MiguelWith the ascent of unmanned aerial vehicles (UAVs) in commercial and research sectors, addressing their susceptibilities to wind disturbances becomes paramount. Current methodologies, though effective, often hinge on specialized sensors, thereby adding to the UAV's weight and compromising its functionality. This thesis explores a recently proposed approach called "causal curiosity", using machine learning (ML) to identify varying wind conditions solely from a UAV's position trajectory, circumventing the need for dedicated wind speed sensors. Through the application of time series classification combined with the intrinsic "causal curiosity" reward system, the research delves into discerning three distinct wind environments: constant wind, shear wind, and turbulence. Ultimately, autonomous UAVs can employ this paper's findings to design optimal trajectories in challenging weather conditions.17 0Item Restricted A Novel Automated Assessment Approach for Diagnosis of Aircraft Composite Materials Based on Machine Learning Thermographic Images(Saudi Digital Library, 2023-03-06) Alhammad, Muflih; Avdelidis, Nicolas PeterInspecting, diagnosing, maintaining and predicting aircraft safety faults are among the most essential regular jobs in complex, safety-critical airframes. Moreover, the development of advanced imaging diagnostic tools such as Non-Destructive Testing techniques (NDT), in particular, for aircraft composite materials, has been considered the subject of intense research over the past decades. The need for prompt and reliable diagnostic tools for composite materials in aircraft applications is growing and attracting increasing interest. However, there is still an ongoing need to develop new tools and approaches to respond to the rapid industrial development and complex machine design. These tools will facilitate early detection and isolation of developing defects and prediction of damage propagation. This allows for early implementation of preventative maintenance and acts as a countermeasure to the possibility of catastrophic failure. In this study, following a short introductory summary and definitions, this research presents a brief review of the recent research literature on failure diagnosis of composite materials, and focuses on developing an automated assessment approach using machine learning tools for aerospace composites. However, to date this investigation is unique and offers a significant contribution to the existing body of knowledge on the use of thermography techniques.17 0