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Multi-Vehicle Systems and Air Traffic Control Commons™
Open Access. Powered by Scholars. Published by Universities.®
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- (4D) Required Navigation Performance (RNP) (1)
- Airspace efficiency (1)
- Analysis (1)
- Aviation Safety (1)
- BiLSTM (1)
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- Cardiorespiratory Sensors (1)
- Cognitive Workload Monitoring (1)
- Deep deterministic policy gradient (1)
- Deep learning (1)
- Dynamic Delegated Corridors (DDCs) (1)
- Flight delay (1)
- Flight safety (1)
- Human-Machine Interface (HMI) (1)
- Machine learning (1)
- Neuro-Fuzzy Systems (1)
- Reinforcement learning (1)
- Safety distance (1)
- Sensor Validation (1)
- UAV (1)
- UAV fleet operation (1)
- Uncertainty Quantification (1)
- Unmanned aerial vehicle (1)
- Urban Air Mobility (UAM) (1)
Articles 1 - 4 of 4
Full-Text Articles in Multi-Vehicle Systems and Air Traffic Control
Towards The Wearable Cardiorespiratory Sensors For Aerospace Applications, Chandan Sheikder
Towards The Wearable Cardiorespiratory Sensors For Aerospace Applications, Chandan Sheikder
Journal of Aviation/Aerospace Education & Research
In safety-critical aviation operations, adaptive Human-Machine Interfaces (HMI) rely on accurate physiological monitoring to mitigate cognitive overload. While cardiorespiratory sensors are promising for real-time cognitive workload assessment, existing studies lack rigorous validation of consumer-grade devices in high-stress aviation contexts and fail to address measurement uncertainty propagation. This study evaluates the Zephyr BioHarness, a commercial wearable sensor, against clinical-grade equipment during arithmetic tasks simulating aviation cognitive demands. By integrating a neuro-fuzzy system with uncertainty propagation methods, we quantify the reliability of heart rate (HR) and breathing rate (BR) metrics for workload estimation. Results demonstrate moderate HR accuracy (RMSE: 4.85 bpm, CC: …
A New Trajectory In Uav Safety: Leveraging Reinforcement Learning For Distance Maintenance Under Wind Variations, Xiaolin Xu, Jeffrey Sun
A New Trajectory In Uav Safety: Leveraging Reinforcement Learning For Distance Maintenance Under Wind Variations, Xiaolin Xu, 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 …
A Deep Bilstm Machine Learning Method For Flight Delay Prediction Classification, Desmond B. Bisandu, Irene Moulitsas
A Deep Bilstm Machine Learning Method For Flight Delay Prediction Classification, Desmond B. Bisandu, Irene Moulitsas
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 …
Dynamic Delegated Corridors And 4d Required Navigation Performance For Urban Air Mobility (Uam) Airspace Integration, Trong Van Nguyen
Dynamic Delegated Corridors And 4d Required Navigation Performance For Urban Air Mobility (Uam) Airspace Integration, Trong Van Nguyen
Journal of Aviation/Aerospace Education & Research
Increased traffic congestion on urban road networks has impacted the travel time for commuters in highly populated urban centers. Urban Air Mobility (UAM) is recognized as a system that transports the passenger and air cargo from any location to any destination within a metropolitan area. UAM may offer a solution to the problematic issue of automobile urban surface transportation congestion. However, the predicted significant growth in the demand for integration of UAM operations into the existing airspace system in the next 20 years and beyond may exceed the capacity of current air traffic control (ATC) system resources, particularly the ATC …