SAE International Journal of Aerospace 2022 Special Issue: Artificial Intelligence, Machine Learning, and Deep Learning in Aerospace JRN-AS-SI-02

Volume 15, Issue 2, 2022

All articles in this special issue have been carefully selected to cover critical aspects of artificial intelligence, machine learning, and deep learning in aerospace.

Dnyanesh Rajpathak, General Motors, USA
Mark Roboff, SKYTHREAD, USA
Huafeng Yu, Research & Technology, Boeing, USA
Gautam Biswas, Vanderbilt University, USA

The SAE International Journal of Aerospace is the preeminent source for peer-reviewed, cutting-edge engineering research within the aerospace industry. The journal is an essential resource for anyone in academia, industry, or government seeking the latest studies and technology in aerospace engineering.

All articles in the special issue contribute to the trends in AI research. Authors have been invited from different sectors to publish research contributions toward new algorithms, techniques, developments, and/or applications of AI methods in aerospace. This special issue includes results of studies on ML development lifecycle for product certification in aviation, air traffic speech recognition, aircraft dynamics, optimization of flying formation for Unmanned Aerial Vehicles (UAVs), a localization method of loose particles based on chaos theory inside the additional pipe of a rocket engine, maritime-related accidents and their causes, safety of aircraft and risk assessment, anomalous behavior in aircraft, and prognostics to assess embedded delamination tolerance in composites.

Article Titles:

- Toward a Machine Learning Development Lifecycle for Product Certification and Approval in Aviation
- Predictive Modeling of Aircraft Dynamics Using Neural Networks
- Development and Optimization of Formation Flying for Unmanned Aerial Vehicles Using Particle Swarm Optimization Based on Reciprocal Velocity Obstacles
- Localization Method of Loose Particles Based on Chaos Theory and Particle Swarm Optimization-Back-Propagation Neural Network
- An Ongoing Safety Risk Assessment and Determination of Correction Time Limit for Civil Aircraft
- Multi-part Analysis and Techniques for Air Traffic Speech Recognition
- Cause and Risk Factors of Maritime-Related Accidents for Aircraft
- Prognostics and Machine Learning to Assess Embedded Delamination Tolerance in Composites
- Anomaly Detection for Unmanned Aerial Vehicle Sensor Data Using a Stacked Recurrent Autoencoder Method with Dynamic Thresholding