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Articles 1 - 4 of 4
Full-Text Articles in Engineering
Integrated Organizational Machine Learning For Aviation Flight Data, Michael J. Pritchard Ph.D., Austin T. Walden Ph.D., Paul J. Thomas Ph.D.
Integrated Organizational Machine Learning For Aviation Flight Data, Michael J. Pritchard Ph.D., Austin T. Walden Ph.D., Paul J. Thomas Ph.D.
Journal of Aviation/Aerospace Education & Research
Increased availability of data and computing power has allowed organizations to apply machine learning techniques to various fleet monitoring activities. Additionally, our ability to acquire aircraft data has increased due to the miniaturization of small form factor computing machines. Aircraft data collection processes contain many data features in the form of multivariate time series (continuous, discrete, categorical, etc.) which can be used to train machine learning models. Yet, three major challenges still face many flight organizations: 1) integration and automation of data collection frameworks, 2) data cleanup and preparation, and 3) developing an embedded machine learning framework. Data cleanup and …
An Enhanced Deep Autoencoder For Flight Delay Prediction, Desmond B. Bisandu Phd, Dan Andrei Soviani-Sitoiu Msc, Irene Moulitsas Phd
An Enhanced Deep Autoencoder For Flight Delay Prediction, Desmond B. Bisandu Phd, Dan Andrei Soviani-Sitoiu Msc, Irene Moulitsas Phd
Journal of Aviation/Aerospace Education & Research
Accurate and timely flight delay prediction cannot be overemphasized because of the ever-increasing demand for air travel and its importance in deploying intelligent transportation systems. Nonetheless, there has not been a universal solution to the problem, as more intelligent flight decision systems are required for the aviation industry's future growth. Existing flight delay classification and prediction approaches are mainly shallow traffic models and do not satisfy many applications in the real world. Our motivation to rethink the deep architecture model for predicting flight delays emanates from the problem. In this research, we proposed a technique that modified stacked autoencoder architecture …
Interpersonal Skills In A Sociotechnical System: A Training Gap In Flight Decks, Kimberly Perkins Atp, Fraes, Sourojit Ghosh, Crystal Hall Phd
Interpersonal Skills In A Sociotechnical System: A Training Gap In Flight Decks, Kimberly Perkins Atp, Fraes, Sourojit Ghosh, Crystal Hall Phd
Journal of Aviation/Aerospace Education & Research
This research analyzed the perceptions of interpersonal skills on established aviation safety models, Crew Resource Management (CRM), and Threat and Error Management (TEM) using feedback from industry pilots. The flight deck is a sociotechnical system where much research has focused on the technical aspect, whereas we spotlight its socio aspect. The aviation industry must invest in training pilots on interpersonal skills to enhance safety through increased efficacy of safety models integrated throughout existing training programs. A 34-question survey was disseminated across both commercial and business aviation pilots (N=822). We explored three research questions regarding pilots’ perceived training on interpersonal skills …
A New Trajectory In Uav Safety: Leveraging Reinforcement Learning For Distance Maintenance Under Wind Variations, Xiaolin Xu M.S., Jeffrey Sun
A New Trajectory In Uav Safety: Leveraging Reinforcement Learning For Distance Maintenance Under Wind Variations, Xiaolin Xu M.S., Jeffrey Sun
Journal of Aviation/Aerospace Education & Research
In the field of aviation, safety is a critical cornerstone, and the operation of Unmanned Aerial Vehicle (UAV) systems is deeply connected with this principle. A thorough analysis and rigorous simulation and testing of aircraft systems are essential to avoid severe safety hazards. This paper delves into the safety issue in UAV operations, specifically regarding maintaining minimum safety distances under fluctuating wind conditions. The study introduces a novel solution based on a Deep Deterministic Policy Gradient (DDPG) model, a reinforcement learning method. The DDPG model was trained using a simulated environment created through the Gazebo simulator, with values for wind …