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The Air Traffic Control System
Published in V. David Hopkin, Human Factors in Air Traffic Control, 2017
Standardization also applies to communications. English is the language of international air traffic control. Controllers everywhere are expected to adopt some similar procedures and to couch instructions in standard forms. There has to be international agreement on radar and its specifications, and on the allocation of radio frequencies for air traffic control purposes. Potentially ambiguous concepts have to be clarified, such as flight level which refers to a standard pressure setting and applies above 3000 feet, compared with altitude which refers to actual minimum pressure and applies below 3000 feet, and with height which refers to actual pressure at an airfield and applies during descent to it (Ratcliffe, 1991). Future international agreements must cover the automated transponding of air traffic control data between air and ground, and the integration of satellite-derived data into air traffic control.
Plan B for Eliminating Mode Confusion: An Interpreter Display
Published in International Journal of Human–Computer Interaction, 2021
The pilot does not understand how a mode will behave or is not aware of actions the autopilot is taking: Asiana Boeing 777 accident, San Francisco, 2013 – On approach to San Francisco, the pilot inappropriately selected a flight level change mode to descend more rapidly, but the airplane started climbing to the MCP altitude, which is how that mode works; this surprised the pilot. He then grabbed the thrust levers to stop the airplane from climbing. Manually positioning the thrust levers put the autothrottle into a mode (HOLD) that will not manage airspeed for the approach (even though the autothrottle was engaged); the flightcrew was unaware that they needed to manage airspeed. Airspeed decreased until the stick shaker was triggered. The flightcrew failed to recover from the stall.Aeroflot Nord Boeing 737–500 accident, Perm, Russia, 2008 – Late in the flight, the two thrust levers became miscalibrated – i.e., positioned at different angles to produce the same thrust. Unaware of this, the pilot matched the thrust levers (positioned them together), which produced more thrust on one engine than the other, but the autopilot was engaged, and it managed the unmatched thrust with other flight controls. The Captain was not aware that the autopilot was managing this situation. When the Captain later disengaged the autopilot, he could not handle the sudden difference in thrust. Inappropriate control inputs led to a crash.
A Novel Augmentative Backward Reward Function with Deep Reinforcement Learning for Autonomous UAV Navigation
Published in Applied Artificial Intelligence, 2022
Manit Chansuparp, Kulsawasd Jitkajornwanich
where denote the previous and current Euclidean distances between the agent and the goal, denotes an altitude of UAV, denote angles of the UAV’s head and position to goal, respectively, denotes an appropriate altitude or flight level. denote weight-parameters. If the agent arrives at the goal, it will receive reward from the above function and also all transitions in the episode will receive additional reward from ABR.
LifeGuard: An Improvement of Actor-Critic Model with Collision Predictor in Autonomous UAV Navigation
Published in Applied Artificial Intelligence, 2022
Manit Chansuparp, Kulsawasd Jitkajornwanich
where denote the previous and current Euclidean distances between the agent and goal, respectively, denotes altitude of UAV, denote angles of the UAV’s head and position to goal, respectively, denotes appropriate altitude or flight level, and denote weight parameters.