Hrl Laboratories, LLC

The Boeing Company

Hrl Labortories, LLC

David J Huber

Deepak Khosla

Tsai-Ching Lu

Yang Chen

Rajan Bhattacharyya

Kevin R Martin

Paul Alex Dow

Nigel D Stepp

Steven W Skorheim

Tiffany Hwu

Kyungnam Kim

Qin Jiang

Anthony Wayne Baker

Aruna Jammalamadaka

Samuel D Johnson

Brandon M Courter

Alex N Waagen

Darrel J VanBuer

Narayan Srinivasa

David L Allen

Kang-Yu Ni

Hankyu Moon

Suhas E Chelian

Shane Edward Arthur

Lei Zhang

Rashmi N Sundareswara

Jaehoon Choe

Terrell Nathan Mundhenk

Luke Charles Ingram

Yongqiang Cao

Matthew S Keegan

Chris A Cantrell

Scott Rad

Aaron Feldman

Jacob Moorman

Myana Anderson

Penn Tasinga

Christopher Kanan

Hyun J Kim

Described is a system for improving machine operation performance. The system assigns and displays, on an interface having multiple interactive controls, a performance score for each skill of a sequential task in a simulation of operation of a machine. Based on the performance scores, one or more skills to improve with targeted training are identified and displayed on the interface. A training scenario of skills to perform via the interactive controls in a subsequent simulation is recommended to improve the performance scores. Following performance of the training scenario in the subsequent simulation, the system assigns and displays, on the interface, a new performance score for each skill performed. The training scenario is adapted based on the new performance scores.

Human machine teaching system for sequential task training

Apparatuses, systems, and methods dynamically model intrinsic parameters of a camera. Methods include: collecting, using a camera having a focus motor, calibration data at a series of discrete focus motor positions; generating, from the calibration data, a set of constant point intrinsic parameters; determining, from the set of constant point intrinsic parameters, a subset of intrinsic parameters to model dynamically; performing, for each intrinsic parameter of the subset of intrinsic parameters, a fit of the point intrinsic parameter values against focus motor positions; generating a model of the intrinsic parameters for the camera based, at least in part, on the fit of the point intrinsic parameter values against the focus motor positions; and determining a position of a fiducial marker within a field of view of the camera based, at least in part, on the model of the intrinsic parameters for the camera.

Method and apparatus for modeling dynamic intrinsic parameters of a camera

Apparatuses and methods train a model and then use the trained model to determine a global three dimensional (3D) position and orientation of a fiduciary marker. In the context of an apparatus for training a model, a wider field-of-view sensor is configured to acquire a static image of a space in which the fiducial marker is disposed and a narrower field-of-view sensor is configured to acquire a plurality of images of at least a portion of the fiducial marker. The apparatus also includes a pan-tilt unit configured to controllably alter pan and tilt angles of the narrower field-of-view sensor during image acquisition. The apparatus further includes a control system configured to determine a transformation of position and orientation information determined from the images acquired by the narrower field-of-view sensor to a coordinate system for the space for which the static image is acquired by the wider field-of-view sensor.

Method and apparatus for determining a three-dimensional position and pose of a fiducial marker

A pilot support system includes: a processing circuit; and memory storing instructions that, when executed by the processing circuit, cause the processing circuit to: receive input data regarding a current state of a vehicle; encode the input data to generate encoded input data; supply the encoded input data to a trained statistical model; compute a current context of the vehicle based on the input data using the trained statistical model; compute one or more pilot feedback indicators based on the current context using the trained statistical model; and provide the one or more pilot feedback indicators to a cockpit of the vehicle.

Neural network-based system for flight condition analysis and communication

Apparatuses and methods determine the three-dimensional position and orientation of a fiducial marker and tracking the three-dimensional position and orientation across different fields-of-view. Methods include: capturing an image of a first space in which the fiducial marker is disposed with a first sensor having a first field-of-view; determining the three-dimensional location and orientation of the fiducial marker within the first space based on the image of the first space in which the fiducial marker is disposed; capturing an image of a second space in which the fiducial marker is disposed with a second sensor having a second field-of-view; calculating pan and tilt information for the second sensor to move the second field-of-view of the second sensor to acquire an image of the fiducial marker; and determining the three-dimensional location and orientation of the fiducial marker within the second space based on the image of the second space.

Method and apparatus for hand-off and tracking for pose estimation of a fiducial marker

Described is a system for human-machine teaching for vehicle operation. The system determines currently enabled status reporting modes on a vehicle interface of a vehicle. The currently enabled status reporting modes are compared to a set of preferred status reporting modes of previous users. Based on the comparison, a status reporting mode is selected. A current operational status of the vehicle is reported to a current user, via the vehicle interface, using the selected status reporting mode. The system then determines preferred solutions of previous users to address the current operational status of the vehicle. Suggestions to address the current operational status of the vehicle based on the preferred solutions are reported to the user via the vehicle interface. A vehicle action corresponding to a solution selected by the current user is implemented via a vehicle component.

Bidirectional machine teaching interface for human-machine co-pilots

A method for generating human-machine hybrid predictions of answers to forecasting problems includes: parsing text of an individual forecasting problem to identify keywords; generating machine models based on the keywords; scraping data sources based on the keywords to collect scraped data relevant to the individual forecasting problem; providing the scraped data to the machine models; receiving machine predictions of answers to the individual forecasting problem from the machine models based on the scraped data; providing, by the computer system via a user interface, the scraped data to human participants; receiving, by the computer system via the user interface, human predictions of answers to the individual forecasting problem from the human participants; aggregating the machine predictions with the human predictions to generate aggregated predictions; and generating and outputting a hybrid prediction based on the aggregated predictions.

System and method for human-machine hybrid prediction of events

A method for computing a human-machine hybrid ensemble prediction includes: receiving an individual forecasting question (IFP); classifying the IFP into one of a plurality of canonical question topics; identifying machine models associated with the canonical question topic; for each of the machine models: receiving, from one of a plurality of human participants: a first task input including a selection of sets of training data; a second task input including selections of portions of the selected sets of training data; and a third task input including model parameters to configure the machine model; training the machine model in accordance with the first, second, and third task inputs; and computing a machine model forecast based on the trained machine model; computing an aggregated forecast from machine model forecasts computed by the machine models; and sending an alert in response to determining that the aggregated forecast satisfies a threshold condition.

Systems and methods for forecast alerts with programmable human-machine hybrid ensemble learning

A method for collecting and processing user input. In some embodiments the method includes presenting a first user with a prompt for eliciting a first response, the first response including a numerical portion including one or more numbers, and an explanatory portion; receiving, from the first user, the first response; receiving from each of a plurality of other users, a respective response of a plurality of other responses; and displaying, to the first user, an ordered list of other responses. Within the ordered list, a second response, of the plurality of other responses, may be earlier than a third response, of the plurality of other responses, the second response being, according to a measure of distance, more distant, than the third response, from the first response.

System of structured argumentation for asynchronous collaboration and machine-based arbitration

A method and apparatus for maintaining a vehicle, such as an aircraft. A plurality of maintenance messages generated during operation of the vehicle are stored to form a plurality of stored maintenance messages. The stored maintenance messages are filtered to remove from the stored maintenance messages those maintenance messages that are correlated to minimum equipment list actions to form filtered stored maintenance messages. A predicted maintenance message is generated from the filtered stored maintenance messages by applying a machine learning algorithm to the filtered stored maintenance messages. The predicted maintenance message may be used to perform a maintenance operation on the vehicle.

Aircraft maintenance message prediction

A method and apparatus for maintaining an aircraft. Real-time event information indicating faults in systems on the aircraft and aircraft condition monitoring system data indicating conditions of the systems on the aircraft are stored during a plurality of legs of flights of the aircraft. A feature table comprising the real-time event information and the aircraft condition monitoring system data is built. Feature vectors are extracted from the feature table. A machine learning algorithm is applied to the extracted feature vectors to generate a predicted maintenance event message that identifies a predicted maintenance event. The predicted maintenance event message is used to perform a maintenance operation on the aircraft.

Aircraft maintenance event prediction using higher-level and lower-level system information

The present invention relates to a surveillance system and, more particularly, to a graphical display and user interface system that provides high-speed triage of potential items of interest in imagery. The system receives at least one image of a scene from a sensor. The image is pre-processed to identify a plurality of potential objects of interest (OI) in the image. The potential OI are presented to the user as a series of chips on a threat chip display (TCD), where each chip is a region extracted from the image that corresponds to a potential OI. Finally, the system allows the user to designate, via the TCD, any one of the chips as an actual OI.

Graphical display and user-interface for high-speed triage of potential items of interest in imagery

Described is a system for identifying communication behavior patterns in communication activity time series. For each pair of variables in the communication activity time series, the system determines a transfer entropy measure, an effective transfer entropy measure from a randomly reordered version of the communication activity time series, and a partial effective transfer entropy measure. A dependency matrix is generated using pair-wised effective transfer entropy measures and partial effective transfer entropy measures, where each element in the matrix represents a total influence of a communication activity time series on another communication activity time series in the future. The dependency matrix is compared with dependency matrices generated from a predefined set of communication patterns to identify the communication behavior pattern. The system generates instructions regarding positioning of a sensor, such that the instructions provide guidance regarding placement of the sensor at a geographical region related to the identified communication pattern.

System to identify unknown communication behavior relationships from time series

An aircraft component failure prediction apparatus including a database and an aircraft maintenance controller coupled to the database. The controller classifies operational data, in a first classification, as corresponding with at least one maintenance message to form at least one classified multidimensional operation data matrix including classified data. The controller classifies the classified data of the at least one classified multidimensional data matrix, in a second classification, to predict an occurrence of a future maintenance message for the aircraft component. The controller preprocesses the at least one classified multidimensional operation data matrix, and generates an output of at least one partial dependency function that explains prediction of the occurrence of the future maintenance message.

System and method for aircraft failure prediction

Described is a system for collision detection. The system divides an image in a sequence of images into multiple sub-fields comprising complementary visual sub-fields. For each visual sub-field, motion is detected in a direction corresponding to the visual sub-field using a spiking Reichardt detector with a spiking neural network. Motion in a direction complementary to the visual sub-field is also detected using the spiking Reichardt detector. Outputs of the spiking Reichardt detector, comprising data corresponding to one direction of movement from two complementary visual sub-fields, are processed using a movement detector. Based on the output of the movement detector, an impending collision is signaled.

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Calabasas, CA, United States of America

David J Huber