Abstract: A method for training a control system model includes introducing a simulated fault into a software simulation of a physical system and generating emulated sensor data based on the simulated fault, where the emulated sensor data emulates output from one or more sensors of the physical system. The method further includes obtaining output data from a test control system provided with the emulated sensor data, where the test control system emulates a control system of the physical system and tagging the output data with the simulated fault to create training data. The method further includes utilizing the training data to train the control system model, where the control system model is a machine learning model for use with the control system of the physical system during operation of the physical system.

Abstract: A control system, and associated control method, that processes aircraft tracking data to determine precise location information for aircraft in the vicinity of an outdoor show that includes laser projectors. The aircraft location information for each aircraft is processed along with heading and speed data to generate a set of laser control data, which is communicated via a monitoring interface to a laser projector operator for use in operating the laser projector. The control system determines the scan field into which the laser projector may project its light during the show, and this scan field is provided, e.g., visually in the monitoring interface, to the laser projector operator along with the sets of laser control data for each aircraft. The laser projector operator may then terminate or continue to operate the laser projector based on this very precise information related to the scan field and location of aircraft.

Abstract: A software-based (“soft”) real-time hub designed and implemented for use in simulation (or control testing) systems such as to provide a modular soft-real-time PIL. A simulation system of the present description typically may include one or more of the following useful subsystems or components: (a) a soft-real-time hub; (b) simulation interfaces; and (c) hardware emulation subsystems/devices. The soft-real-time hub is typically a combination of hardware and software adapted to provide deterministic data transport between simulations and input/output (I/O) emulation. By creating a common point, the hub enables simulation modules to be swapped out as the simulation system progresses without the operator having to worry about interface timing, forcing, or data visualization. A desirable aspect of the simulation system is it allows for testing certain conditions by forcing I/O and then seeing how the controller or system under testing responds.

Abstract: A software-based (“soft”) real-time hub designed and implemented for use in simulation (or control testing) systems such as to provide a modular soft-real-time PIL. A simulation system of the present description typically may include one or more of the following useful subsystems or components: (a) a soft-real-time hub; (b) simulation interfaces; and (c) hardware emulation subsystems/devices. The soft-real-time hub is typically a combination of hardware and software adapted to provide deterministic data transport between simulations and input/output (I/O) emulation. By creating a common point, the hub enables simulation modules to be swapped out as the simulation system progresses without the operator having to worry about interface timing, forcing, or data visualization. A desirable aspect of the simulation system is it allows for testing certain conditions by forcing I/O and then seeing how the controller or system under testing responds.

Abstract: A software-based (“soft”) real-time hub designed and implemented for use in simulation (or control testing) systems such as to provide a modular soft-real-time PIL. A simulation system of the present description typically may include one or more of the following useful subsystems or components: (a) a soft-real-time hub; (b) simulation interfaces; and (c) hardware emulation subsystems/devices. The soft-real-time hub is typically a combination of hardware and software adapted to provide deterministic data transport between simulations and input/output (I/O) emulation. By creating a common point, the hub enables simulation modules to be swapped out as the simulation system progresses without the operator having to worry about interface timing, forcing, or data visualization. A desirable aspect of the simulation system is it allows for testing certain conditions by forcing I/O and then seeing how the controller or system under testing responds.

Abstract: A software-based (“soft”) real-time hub designed and implemented for use in simulation (or control testing) systems such as to provide a modular soft-real-time PIL. A simulation system of the present description typically may include one or more of the following useful subsystems or components: (a) a soft-real-time hub; (b) simulation interfaces; and (c) hardware emulation subsystems/devices. The soft-real-time hub is typically a combination of hardware and software adapted to provide deterministic data transport between simulations and input/output (I/O) emulation. By creating a common point, the hub enables simulation modules to be swapped out as the simulation system progresses without the operator having to worry about interface timing, forcing, or data visualization. A desirable aspect of the simulation system is it allows for testing certain conditions by forcing I/O and then seeing how the controller or system under testing responds.

Abstract: A method comprises receiving one or more depth images from a depth camera, the depth images indicating a depth of a surface imaged by each pixel of the depth images. The method may further comprise identifying a human subject imaged by the depth images and recognizing within one or more of the depth images a beacon emitted from a control device. A position of the control device may be assessed in three dimensions, and the control device may be associated with the human subject based on a proximity of the control device to the human subject or other parameter of the control device with relation to the human subject.

Abstract: A control device comprises an electric motor and a controller. The controller may be configured to receive a signal from the electric motor and selectively change a state of the control device responsive to the signal.

Abstract: A control device comprises an electric motor and a controller. The controller may be configured to receive a signal from the electric motor and selectively change a state of the control device responsive to the signal.

Abstract: A method comprises detecting a control device, and if the control device is not bound to the computing system, detecting an optical beacon emitted from the control device and binding the control device to the computing system.

Abstract: A method comprises receiving one or more depth images from a depth camera, the depth images indicating a depth of a surface imaged by each pixel of the depth images. The method may further comprise identifying a human subject imaged by the depth images and recognizing within one or more of the depth images a beacon emitted from a control device. A position of the control device may be assessed in three dimensions, and the control device may be associated with the human subject based on a proximity of the control device to the human subject or other parameter of the control device with relation to the human subject.