Aerospace & Defense Technology: June 2023
- Content
- Overcoming Machining Productivity Challenges with Aerospace Components
- Why the Turbomachinery Industry is Increasingly Bullish on Additive Manufacturing
3D-printed metal-alloys are answering critical needs for high fluid flow, high-pressure parts. - Airborne Inspection Sensor Evolves with LiDAR, Mid-IR and Artificial Intelligence
- Air Force eVTOL Research and Development Programs Make Remote Pilot Progress
- Creating a Digital Gateway for RF Domain Will Advance Designs That Meet DoD Initiatives
- Understanding the Unique RF Interconnect Requirements for Ultra-Demanding Hypersonic Missile and Satellite Applications
- ADS-B Classification Using Multivariate Long Short-Term Memory
This analysis extends previous research that used long short-term memory-fully convolutional networks to identify aircraft engine types from publicly available automatic dependent surveillance-broadcast (ADS-B) data. - Physics-Guided Neural Network for Regularization and Learning Unbalanced Data Sets
Directed energy deposition is of interest to the aerospace and defense industries for the production of novel and complex geometries, as well as repair applications. However, variability during the build process can result in deviations in final component geometry, structure, and mechanical properties, which adds to the complexity of process planning and slows down adoption of this technology. - Context-Aware Visual Search Using a Pan-Tilt-Zoom Camera
While in some scenarios an Internet-of-Things (IoT) approach allows multiple distributed cameras to cooperatively survey a large region of interest (ROI), other scenarios, such as surveillance by a mobile robot, require the ROI to be surveyed by a small number of collocated cameras. - Automated Atmospheric Correction of Nanosatellites Using Coincident Ocean Color Radiometer Data
Researchers present a machine-learning-based method for utilizing traditional ocean-viewing satellites to perform automated atmospheric correction of nanosatellite data. - Characterizing Motion Prediction in Small Autonomous Swarms
Despite the expanded use of robotic swarms, little is known about how swarms are perceived by human operators. To characterize human-swarm interactions, we evaluate how operators perceive swarm characteristics, including movement patterns, control schemes, and occlusion.