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TRAJECTORY PATTERN IDENTIFICATION AND CLASSIFICATION FOR ARRIVALS IN VECTORED AIRSPACEChuhao Deng (11184909) 26 July 2021 (has links)
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<p>As the demand and complexity of air traffic increase, it becomes crucial to maintain
the safety and efficiency of the operations in airspaces, which, however, could lead to an
increased workload for Air Traffic Controllers (ATCs) and delays in their decision-making
processes. Although terminal airspaces are highly structured with the flight procedures such
as standard terminal arrival routes and standard instrument departures, the aircraft are
frequently instructed to deviate from such procedures by ATCs to accommodate given traffic
situations, e.g., maintaining the separation from neighboring aircraft or taking shortcuts to
meet scheduling requirements. Such deviation, called vectoring, could even increase the
delays and workload of ATCs. This thesis focuses on developing a framework for trajectory
pattern identification and classification that can provide ATCs, in vectored airspace, with
real-time information of which possible vectoring pattern a new incoming aircraft could
take so that such delays and workload could be reduced. This thesis consists of two parts,
trajectory pattern identification and trajectory pattern classification.
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<p>In the first part, a framework for trajectory pattern identification is proposed based on
agglomerative hierarchical clustering, with dynamic time warping and squared Euclidean
distance as the dissimilarity measure between trajectories. Binary trees with fixes that are
provided in the aeronautical information publication data are proposed in order to catego-
rize the trajectory patterns. In the second part, multiple recurrent neural network based
binary classification models are trained and utilized at the nodes of the binary trees to
compute the possible fixes an incoming aircraft could take. The trajectory pattern identifi-
cation framework and the classification models are illustrated with the automatic dependent
surveillance-broadcast data that were recorded between January and December 2019 in In-
cheon international airport, South Korea .
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Interpretable natural language processing models with deep hierarchical structures and effective statistical trainingZhaoxin Luo (17328937) 03 November 2023 (has links)
<p dir="ltr">The research focuses on improving natural language processing (NLP) models by integrating the hierarchical structure of language, which is essential for understanding and generating human language. The main contributions of the study are:</p><ol><li><b>Hierarchical RNN Model:</b> Development of a deep Recurrent Neural Network model that captures both explicit and implicit hierarchical structures in language.</li><li><b>Hierarchical Attention Mechanism:</b> Use of a multi-level attention mechanism to help the model prioritize relevant information at different levels of the hierarchy.</li><li><b>Latent Indicators and Efficient Training:</b> Integration of latent indicators using the Expectation-Maximization algorithm and reduction of computational complexity with Bootstrap sampling and layered training strategies.</li><li><b>Sequence-to-Sequence Model for Translation:</b> Extension of the model to translation tasks, including a novel pre-training technique and a hierarchical decoding strategy to stabilize latent indicators during generation.</li></ol><p dir="ltr">The study claims enhanced performance in various NLP tasks with results comparable to larger models, with the added benefit of increased interpretability.</p>
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