The Boeing Company

Zachary D Jorgensen

Tyler Charles Staudinger

Jan Wei Pan

Yuri Levchuk

Kevin Scott Callahan

Nick Shabdeh Evans

Nick Shadbeh Evans

Stephen G Dame

Jai Joon Choi

Dragos Dorin Margineantu

Eric Raymond Muir

Daniel S ReMine

Isaac Chang

Joshua David Kalin

Huafeng Yu

Hieu Tat Nguyen

Oliver William Sykes

Daniel S Remine

A method of machine learning model development includes building an autoencoder including an encoder trained to map an input into a latent representation, and a decoder trained to map the latent representation to a reconstruction of the input. The method includes building an artificial neural network classifier including the encoder, and a classification layer partially trained to perform a classification in which a class to which the input belongs is predicted based on the latent representation. Neural network inversion is applied to the classification layer to find inverted latent representations within a decision boundary between classes in which a result of the classification is ambiguous, and inverted inputs are obtained from the inverted latent representations. Each inverted input is labeled with a class that is its ground truth, and thereby producing added training data for the classification, and the classification layer is further trained using the added training data.

Machine learning model development

A method and apparatus for manufacturing a part. The part is designed using a CAD system to generate a CAD part model of the part. Features of the part are identified from the CAD part model of the part. A parametric specification of the part is generated using the features of the part. The parametric specification of the part is saved as a parametric part model. The parametric part model is used to fabricate the part.

System and method of voxel based parametric specification for manufacturing a part

An apparatus is provided for automated object tracking in a video feed. The apparatus receives and sequentially processes a plurality of frames of the video feed to track objects. In particular, a plurality of objects in a frame are detected and assigned to a respective track fragment. A kinematic, visual, temporal or machine learning-based feature of an object is then identified and stored in metadata associated with the track fragment. A track fragment for the object is identified in earlier frames based on a comparison of the feature and a corresponding feature in metadata associated with the earlier frames. The track fragments for the object in the frame and the object in the earlier frames are linked to form a track of the object. The apparatus then outputs the video feed with the track of the object as an overlay thereon.

Automated object tracking in a video feed using machine learning

Systems, methods, and computer-readable media storing instructions for determining cross-track error of an aircraft on a taxiway are disclosed herein. The disclosed techniques capture electronic images of a portion of the taxiway using cameras or other electronic imaging devices mounted on the aircraft, pre-process the electronic images to generate regularized image data, apply a trained multichannel neural network model to the regularized image data to generate a preliminary estimate of cross-track error relative to the centerline of the taxiway, and post-process the preliminary estimate to generate an estimate of cross-track error of the aircraft. Further embodiments adjust a GPS-based location estimate of the aircraft using the estimate of cross-track error or adjust the heading of the aircraft based upon the estimate of cross-track error.

Aircraft positioning on a taxiway

A method of machine learning model development includes receiving a plurality of images of a scene, and performing an unsupervised image selection. This includes applying the images to a pre-trained model to extract and embed the images with respective feature vectors, and performing a cluster analysis to group the images in a clusters based on correlations among the respective feature vectors. The unsupervised image selection also includes selecting at least some but not all images in each of the clusters, and any images considered outliers that belong to none of the clusters, for a subset of the images that includes fewer than all of the images. And the method includes receiving user input to label or labeling objects depicted in the subset of the images to produce a training set of images, and building a machine learning model for object detection using the training set of images.

Machine learning model development with unsupervised image selection

A set of 3D user-designed images is used to create a high volume of realistic scenes or images which can be used for training and testing deep learning machines. The system creates a high volume of scenes having a wide variety of environmental, weather-related factors as well as scenes that take into account camera noise, distortion, angle of view, and the like. A generative modeling process is used to vary objects contained in an image so that more images, each one distinct, can be used to train the deep learning model without the inefficiencies of creating videos of actual, real life scenes. Object label data is known by virtue of a designer selecting an object from an image database and placing it in the scene. This and other methods care used to artificially create new scenes that do not have to be recorded in real-life conditions and that do not require costly and time-consuming, manual labelling or tagging of objects.

Generative image synthesis for training deep learning machines

An apparatus is provided for automated object and activity tracking in a live video feed. The apparatus receives and processes a live video feed to identify a plurality of objects and activities therein. The apparatus also generates natural language text that describes a storyline of the live video feed using the plurality of objects and activities so identified. The live video feed is processed using computer vision, natural language processing and machine learning, and a catalog of identifiable objects and activities. The apparatus then outputs the natural language text audibly or visually with a display of the live video feed.

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Huntsville, AL, United States of America

Zachary D Jorgensen