Handling the large amount of information from aircraft trajectories that are produced daily from air traffic control radar systems requires models for representing trajectories in a compact, easy to calculate, representative and distinctive form. These models should permit to perform clustering and classification operations efficiently and effectively. The Fourier descriptors have these characteristics and this article presents the results obtained on actual aircraft trajectories including approach and takeoff operations over a terminal area. Clustering and classification techniques in the feature space of Fourier descriptors were able to correctly separate the various types of operations. Additionally, based on the results of the clusters obtained, a method is presented for the classification of trajectories in progress based on kernel density estimation. An interesting result from the point of view of air traffic control for the detection of anomalous traffic is demonstrated.
Understanding decision‐making in complex and dynamic environments is relevant for designing strategies targeting safety improvements and error rate reductions. However, studies evaluating brain dynamics in realistic situations are scarce in the literature. Given the evidence that specific microstates may be associated with perception and attention, in this work we explored for the first time the application of the microstate model in an ecological, dynamic and complex scenario. More specifically, we evaluated elite helicopter pilots during engine‐failure missions in the vicinity of the so called “dead man's curve,” which establishes the operational limits for a safe landing after the execution of a recovery maneuver (autorotation). Pilots from the Brazilian Air Force flew a AS‐350 helicopter in a certified aerodrome and physiological sensor data were synchronized with the aircraft's flight test instrumentation. We assessed these neural correlates during maneuver execution, by comparing their modulations and source reconstructed activity with baseline epochs before and after flights. We show that the topographies of our microstate templates with 4, 5, and 6 classes resemble the literature, and that a distinct modulation characterizes decision‐making intervals. Moreover, the source reconstruction result points to a differential activity in the medial prefrontal cortex, which is associated to emotional regulation circuits in the brain. Our results suggest that microstates are promising neural correlates to evaluate realistic situations, even in a challenging and intrinsically noisy environment. Furthermore, it strengthens their usage and expands their application for studying cognition under more realistic conditions.
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