The Boeing Company

Jasper P Corleis

Nick Shadbeh Evans

Stephen G Dame

Amir Afrasiabi

Brian Kenyon Rupnik

Eric Raymond Muir

Michelle D Warren

Cullen W Billhartz

Jeff D Yoo

Kevin Leslie Gosling

Seth Micah Zelman

Joshua Welshmeyer

Michael Richard McGivern

Matthew Sheridan-Smith

Su Ying Li

Jose A Medina

Zarrin Khiang-Huey Chua

Taylor A Riccetti

David J Ross

Yu Qiao

Simran Pabla

An apparatus for identifying objects receives a dataset comprising an image having first real image features; trains a neural network to recognize the first real image features in the received dataset; and performs a synthetic image augmentation to generate synthetic image features corresponding to the first real image features in the received dataset using the neural network. The synthetic image augmentation allows for improved training of a computer vision function for recognizing second real image features, corresponding to the synthetic image features, in a real-world environment.

Unpaired image-to-image translation using a generative adversarial network (GAN)

A method includes determining, at a computing system of an aircraft, a target path for an approach to landing based on flight data. The method includes determining an approach phase of approach phases of the approach to landing. The method includes determining a real-time operational region of the aircraft for each monitored condition of monitored conditions based on the real-time aircraft data, the target path, and operational regions for each monitored condition. The method includes accessing notification content for a particular monitored condition for the approach phase. The real-time operational region for the particular monitored condition for the approach phase is outside of a target region of the particular monitored condition for the approach phase. The method also includes sending one or more notification signals based on the notification content from the computing system to one or more output systems.

System and method of response to an unstable approach during an approach to landing for an aircraft

A method is provided for supporting an aircraft approaching a runway on an airfield. The method includes receiving a sequence of images of the airfield, captured by a camera onboard the aircraft approaching the runway. For at least one image of the sequence of images, the method includes applying the image(s) to a machine learning model trained to predict a pose of the aircraft relative to the runway. The machine learning model is configured to map the image(s) to the pose based on a training set of labeled images with respective ground truth poses of the aircraft relative to the runway. The pose is output as a current pose estimate of the aircraft relative to the runway for use in at least one of monitoring the current pose estimate, generating an alert based on the current pose estimate, or guidance or control of the aircraft on a final approach.

Determining a current pose estimate of an aircraft relative to a runway to support the aircraft on approach

Illustrative embodiments provide a method and system for communicating air traffic control information. An audio signal comprising voice activity is received. Air traffic control information in the voice activity is identified using an artificial intelligence algorithm. A text transcript of the air traffic control information is generated and displayed on a confirmation display. Voice activity in the audio signal may be detected by identifying portions of the audio signal that comprise speech based on a comparison between the power spectrum of the audio signal and the power spectrum of noise and forming speech segments comprising the portions of the audio signal that comprise speech.

Voice activity detection and dialogue recognition for air traffic control

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Renton, WA, United States of America

Jasper P Corleis