and/or data from the onboard avionics; determining the aircraft state at a point N on the basis of the received data; determining the state of the aircraft at the point N+1 on the basis of the state of the aircraft at point N by applying a model learnt by means of machine learning....
American organizes several hundred departures and arrivals each day at its major hubs. Each flight must be matched to a gate based on aircraft type, runway concentration, taxi times, and other operational metrics. For decades, this gate assignment process typically required four hours of human labo...
So, machine learning not only helps increase the lifetime of aircraft parts, but also anticipates breakdowns and reduces operation interruptions. This technology is applied in three areas of manufacturing processes: sizing, production and operation monitoring. ...
“After tornadoes, hurricanes or earthquakes, C-130s quickly bring emergency supplies and aid. They can operate in the most austere conditions, landing in fields or on damaged runways.” Isbill’s team uses data and machine learning to make Lockheed Martin’s aircraft as reliable and cost-...
The data collected from wind tunnel tests, flight tests, and simulations is trained in a machine learning model, which can then be used to determine the optimal design parameters. These parameters are used to generate 3D models of the aircraft, which are then tested in a virtual environment ...
Wireless communications in aircraft cabin environments have drawn widespread attention with the increase of application requirements. To ensure reliable and stable in-cabin communications, the investigation of channel parameters such as path loss is necessary. In this paper, four machine learning methods,...
In Section 4, we set up a test scenario to demonstrate the prediction capabilities of our machine learning approach. We choose the random boarding (passengers have pre-assigned seats and enter the aircraft unconstrained, with no specific order) as a common boarding process and the individual ...
3]Aircraft Engines Remaining Useful Life Prediction with an Improved Online Sequential Extreme Learning...
For doing so, this paper develops a probabilistic horizontal interdependency measure between aircraft supported by machine learning algorithms, addressing time separations at crossing points.Then, vertical profiles of flight trajectories are characterised depending on this factor and other intrinsic features. ...
例子Anomaly detectionsupervised learning Fraud detection Email spam classification Manufacturing (e.g. aircraft engines) Weather prediction (sunny/raint/est). Monitoring machines in a data center Cancer classification总结:正例足够多就用监督学习,不够就用错误检测。