In order to improve the chart recognition speed and the recognition success rate of the large field-of-view high-sensitivity star sensitizer, an improved artificial bee colony algorithm combined with the three anti-perturbation parameters is proposed as a chart recognition method. The method is based on the improved artificial bee colony algorithm to find the optimal paths for all the stars in the main star feature region; then, the optimal path length is used to find addresses in the navigation feature library, and the interstellar angular distances of the first three navigation stars in the optimal paths, d12/d23 , are taken as the main recognition features for matching and identification; finally, the three perturbation parameters are taken as the auxiliary recognition features to reduce the redundancy of the matching and to serve as a validation of the recognition.
In order to better describe the flight support vehicle scheduling system, a fuzzy colored Petri net (FCPN) model is proposed, and its formal definition and specific inference algorithm are listed. A dispatching model with active strategies is developed using FCPN. An application example yields that the FCPN model significantly reduces the model complexity and is able to more accurately describe the vehicle scheduling system.
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