A torque measurement system determines torque on a shaft by monitoring angular deflection of the shaft under load using phase shift measurements. Calibration of the system uses a defined offset that is determined using a reference operating condition. The offset calibration value is determined for a rotorcraft using the following steps: defining a reference operational condition in which the shaft is rotating, estimating the torque at the reference condition based on aerodynamic knowledge of the rotors coupled to the shaft, operating the shaft at the reference operational condition, capturing sensor data to determine the phase difference at the operational condition, and associating the phase difference and an estimated torque as a calibration value to enable calculation of torque in the torque measurement system.

An air data probe (10) and associated method of method of measuring air data is disclosed. The air data probe includes a plurality of air pressure sensors, and a body (14) that encloses a hollow interior cavity (16), where the body (14) has a generally symmetrical airfoil profile. The body (14) includes a plurality of projections (20a-d) extending beyond the generally symmetrical airfoil profile, each of the plurality of projections (20a-d) including an pressure port (22a-d) at a distal end (24a-d) that is in communication with the hollow interior cavity. Each of the pressure ports (22a-d) receives a corresponding air pressure sensor (12a-d) that is configured to collect static and dynamic air pressure data.

An air data probe (10) and associated method of method of measuring air data is disclosed. The air data probe includes a plurality of air pressure sensors, and a body (14) that encloses a hollow interior cavity (16), where the body (14) has a generally symmetrical airfoil profile. The body (14) includes a plurality of projections (20a-d) extending beyond the generally symmetrical airfoil profile, each of the plurality of projections (20a-d) including an pressure port (22a-d) at a distal end (24a-d) that is in communication with the hollow interior cavity. Each of the pressure ports (22a-d) receives a corresponding air pressure sensor (12a-d) that is configured to collect static and dynamic air pressure data.

An unmanned aerial vehicle (UAV) may include a communication interface and a pressure sensor configured to measure barometric pressure. The UAV may also include a processor configured to generate a request for elevation data and barometric pressure data and transmit, via the communication interface, the request to the at least one other device. The processor may also be configured to receive, from each of the at least one other device, elevation data and barometric pressure data, and estimate the elevation of the UAV based on the measured barometric pressure, the received elevation data and the received barometric pressure data.

According to an embodiment, an information processing device includes an acquirer configured to acquire at least one type of data within meteorological observation data indicating weather at a target location observed at each of a plurality of observation times and meteorological prediction data indicating the weather at the target location predicted using a meteorological prediction model, a deriver configured to derive an index value indicating a threat level of lightning of the target location at each of the plurality of observation times on the basis of the meteorological observation data and the like acquired by the acquirer, and a predictor configured to input the index value derived by the deriver to a model for outputting a future index value when a past or present index value is input and predict a threat of lightning of the target location at a future time later than the observation time on the basis of an output result of the model to which the index value has been input.

Monitoring and reporting methods and apparatus include the acquisition of detailed aircraft state and systems data, analysis of the collected data, and transmission of the collected data and/or analysis of the collected data to a destination automatically via a portable electronic device which is carried onto and off of the aircraft by the pilot or another crew member. More particularly, monitoring and reporting methods and apparatus include collecting analog or digital sensor data onboard an aircraft, analyzing the data in real-time, and automatically transmitting the data and/or analysis of the data to a destination including a portable storage device such as a portable computer, electronic flight bag (EFB), or smart phone, by means such as wireless transmission, for automatic transfer to another destination when the portable computer, electronic flight bag (EFB), or smart phone is off of the aircraft.

Monitoring and reporting methods and apparatus include the acquisition of detailed aircraft state and systems data, analysis of the collected data, and transmission of the collected data and/or analysis of the collected data to a destination automatically via a portable electronic device which is carried onto and off of the aircraft by the pilot or another crew member. More particularly, monitoring and reporting methods and apparatus include collecting analog or digital sensor data onboard an aircraft, analyzing the data in real-time, and automatically transmitting the data and/or analysis of the data to a destination including a portable storage device such as a portable computer, electronic flight bag (EFB), or smart phone, by means such as wireless transmission, for automatic transfer to another destination when the portable computer, electronic flight bag (EFB), or smart phone is off of the aircraft.

A dual agent reinforcement learning autonomous system (DARLAS) for the autonomous operation of aircraft and/or provide pilot assistance. DARLAS includes an artificial neural network, safe agent, and cost agent. The safe agent is configured to calculate safe reward Q values associated with landing the aircraft at a predetermined destination or calculated emergency destination. The cost agent is configured to calculate cost reward Q values associated with maximum fuel efficiency and aircraft performance. The safe and cost reward Q values are based on state-action vectors associated with an aircraft, which may include state data and action data. The system may include a user output device that provides an indication of an action to a user. The action corresponds to an agent action having the highest safe reward Q value and the highest cost require Q value. DARLAS prioritizes the highest safe reward Q value in the event of conflict.

A dual agent reinforcement learning autonomous system (DARLAS) for the autonomous operation of aircraft and/or provide pilot assistance. DARLAS includes an artificial neural network, safe agent, and cost agent. The safe agent is configured to calculate safe reward Q values associated with landing the aircraft at a predetermined destination or calculated emergency destination. The cost agent is configured to calculate cost reward Q values associated with maximum fuel efficiency and aircraft performance. The safe and cost reward Q values are based on state-action vectors associated with an aircraft, which may include state data and action data. The system may include a user output device that provides an indication of an action to a user. The action corresponds to an agent action having the highest safe reward Q value and the highest cost require Q value. DARLAS prioritizes the highest safe reward Q value in the event of conflict.

A cockpit switch device can include a pushbutton switch, a bi-stable relay and a toggle component. The pushbutton switch can be configured to be manually actuated by a user into a command state. The bi-stable relay can be controlled by input commands from the pushbutton switch and input commands from a processor, and can be configured to control operation of one or more systems of an a aircraft. The toggle component can be connected to the pushbutton switch, the processor and the bi-stable relay. The toggle component can receive an input command signal from at least one of the pushbutton switch or the processor, and cause a state of the bi-stable relay to be flipped responsive to the input command signal from the at least one of the pushbutton switch or the processor.