Saudi Cultural Missions Theses & Dissertations
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Item Restricted Advanced Autonomous Vehicles Analytics for Predicting Navigation Performance(University of Pittsburgh, 2024) Alharbi, Mohammed Abdullah H; Karimi, Hassan AliAutonomous Vehicles (AVs) stand as a monumental leap in modern transportation technology, offering the potential to enhance road safety and optimize transportation efficiency. However, their broad adoption is hindered by uncertainties associated with their sensors that allow for perceiving, interpreting, and interacting with their surroundings. Sensor uncertainties (SUs) arise from various sources, including sensor noise, varying environmental conditions, and inherent limitations in sensor design. SUs undermine the accuracy and reliability of AV navigation, posing substantial risks to AV performance and, by extension, to passenger safety. Considering the high stakes involved, including human lives, traffic flow optimization, and the structural integrity of transportation infrastructures, it is crucial for AVs to operate with minimal SUs. As driving on roads is dynamic with unpredictable elements, like sudden weather changes, AVs must be designed to handle both planned and unforeseen changes with unwavering precision. Failure to account for such uncertainties can cause unsafe driving and culminate in catastrophic outcomes, thereby deteriorating public confidence in autonomous driving technologies. Common approaches to identifying and handling SUs in AVs involve data fusion and machine learning techniques. Despite their acceptable performance, these techniques are constrained by several critical limitations that hinder their applicability in complex real-world scenarios. For instance, conventional sensor fusion techniques often make overly simplistic assumptions, such as treating uncertainties as independent and normally distributed, which fail to capture the complex interdependencies and nonlinearities present in real sensor data. This simplification leads to suboptimal solutions, especially in challenging environments. Additionally, these techniques lack adaptive mechanisms to respond to changing environmental conditions, limiting their robustness. On the other hand, machine learning techniques, though capable of processing large volumes of data and uncovering hidden patterns, typically suffer from a lack of interpretability, often referred to as the “black-box” problem. This opacity inhibits a comprehensive understanding of the decision-making processes, complicating efforts to ensure transparency and accountability in AV operations. Furthermore, the extant literature is void in furnishing robust evaluative metrics and tools that could facilitate the systematic analysis of AV sensor performance, both before and after occurrence of incidents. This thesis addresses these critical gaps by introducing an advanced AV analytics (AVA) framework and making the following contributions. Firstly, it introduces a novel ontology that represents and formalizes major concepts related to SUs in AV navigation. This ontology serves as a conceptual foundation for automated reasoning about navigation safety. Secondly, the thesis formulates a set of tailored performance metrics that provides a more nuanced evaluation of sensor reliability and accuracy under varying operational conditions. Thirdly, the AVA framework incorporates predictive models that not only quantify AV navigation sensor performance but also identify factors contributing to SUs. These models are unique in their multidimensional scope, encompassing environmental variables, and sensor specifications, and are of two types: online and offline. Online models focus on real-time evaluation of uncertainties for immediate decision-making, while offline models, also called forensic models, allow to analyze factors behind any unexpected behaviors. Finally, the thesis introduces a global path planner that integrates AVA’s analytical outputs to optimize AV route planning. Unlike commonly used route optimization criteria, such as shortest or fastest routes, this path planner incorporates sensor performance to identify safest routes by avoiding high-risk areas or conditions that could exacerbate SUs. These contributions are thoroughly validated using simulated and real data. The outcomes of the proposed research will help develop AV navigation solutions that are reliable and safe.19 0Item Restricted Using Semantic Richness for Metaphor Detection using Deep Learning(University of Birmingham, 2024) Alnafesah, Ghadi; Lee, MarkABSTRACT The Natural Language Processing (NLP) encounters difficulties with metaphors, known for their creative and non-literal usage. Metaphors involve using words or phrases from one context in entirely different contexts, making the meaning less clear and requiring human interpretation for understanding. This dissertation places its focus on the semantic richness elements derived from the perceptual part of the semantic network. These elements serve as the main linguistic features integrated into vector representations. By extracting the semantic information encompassing concreteness, imageability, sensory experience, sentiment, and embodiment, this study seeks to explore the feasibility of detecting metaphors using deep learning models. The investigation is conducted using two experimental structures: sentence-level classification for the categorisation of entire sentences and word-level classification for individual words. These models are assessed across three metaphorical datasets: VUAMC, MOH-X, and TroFi. The main objective is to evaluate the impact of these semantic elements on the metaphor detection task, with the potential for enhancing model performance.33 0Item Restricted Analysis of Two-Dimensional Truss Bridge Design Under Uncertainty(Saudi Digital Library, 2023-08-16) Al Jumaie, Abdulaziz; Febrianto, EkyIn civil engineering, computational analysis offers invaluable insights into understanding and predicting the responses of complex structures, with truss bridges as an example. Truss bridges, characterized by their various types and applications in construction projects, are mired in uncertainties arising from factors such as load variability and environmental conditions. While traditional deterministic approaches determine their design and performance, modern analytical techniques such as Neural Networks promise enhanced comprehension of these uncertainties. This report investigates the application of Neural Networks as a surrogate model to the FEM, with application to the analysis of truss bridge configurations, specifically five key designs: Warren, Pratt, Howe, K-Truss, and Bowstring. Preliminary findings illustrate initial agreement between FEM and Neural Network predictions, with the latter's higher computational speed during the prediction phase. However, the research acknowledges certain limitations, emphasizing the importance of comprehensive analyses incorporating real-world parameters and ground-truth validations.10 0