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Articles 1 - 4 of 4
Full-Text Articles in Engineering
Spoken Language Processing And Modeling For Aviation Communications, Aaron Van De Brook
Spoken Language Processing And Modeling For Aviation Communications, Aaron Van De Brook
Doctoral Dissertations and Master's Theses
With recent advances in machine learning and deep learning technologies and the creation of larger aviation-specific corpora, applying natural language processing technologies, especially those based on transformer neural networks, to aviation communications is becoming increasingly feasible. Previous work has focused on machine learning applications to natural language processing, such as N-grams and word lattices. This thesis experiments with a process for pretraining transformer-based language models on aviation English corpora and compare the effectiveness and performance of language models transfer learned from pretrained checkpoints and those trained from their base weight initializations (trained from scratch). The results suggest that transformer language …
Deep Learning Of Semantic Image Labels On Hdr Imagery In A Maritime Environment, Charles Montagnoli
Deep Learning Of Semantic Image Labels On Hdr Imagery In A Maritime Environment, Charles Montagnoli
Doctoral Dissertations and Master's Theses
Situational awareness in the maritime environment can be extremely challenging. The maritime environment is highly dynamic and largely undefined, requiring the perception of many potential hazards in the shared maritime environment. One particular challenge is the effect of direct-sunlight exposure and specular reflection causing degradation of camera reliability. It is for this reason then, in this work, the use of High-Dynamic Range imagery for deep learning of semantic image labels is studied in a littoral environment. This study theorizes that the use HDR imagery may be extremely beneficial for the purpose of situational awareness in maritime environments due to the …
A Bidirectional Deep Lstm Machine Learning Method For Flight Delay Modelling And Analysis, Desmond B. Bisandu, Irene Moulitsas
A Bidirectional Deep Lstm Machine Learning Method For Flight Delay Modelling And Analysis, Desmond B. Bisandu, Irene Moulitsas
National Training Aircraft Symposium (NTAS)
Flight delays can be prevented by providing a reference point from an accurate prediction model because predicting flight delays is a problem with a specific space. Only a few algorithms consider predicted classes' mutual correlation during flight delay classification or prediction modelling tasks. None of these existing methods works for all scenarios. Therefore, the need to investigate the performance of more models in solving the problem of flight delay is vast and rapidly increasing. This paper presents the development and evaluation of LSTM and BiLSTM models by comparing them for a flight delay prediction. The LSTM does the feature extraction …
A Deep Bilstm Machine Learning Method For Flight Delay Prediction Classification, Desmond B. Bisandu Phd, Irene Moulitsas Phd
A Deep Bilstm Machine Learning Method For Flight Delay Prediction Classification, Desmond B. Bisandu Phd, Irene Moulitsas Phd
Journal of Aviation/Aerospace Education & Research
This paper proposes a classification approach for flight delays using Bidirectional Long Short-Term Memory (BiLSTM) and Long Short-Term Memory (LSTM) models. Flight delays are a major issue in the airline industry, causing inconvenience to passengers and financial losses to airlines. The BiLSTM and LSTM models, powerful deep learning techniques, have shown promising results in a classification task. In this study, we collected a dataset from the United States (US) Bureau of Transportation Statistics (BTS) of flight on-time performance information and used it to train and test the BiLSTM and LSTM models. We set three criteria for selecting highly important features …