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2 TASK Quarterly : scientific bulletin of Academic Computer Centre in Gdansk

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Neural network with single hidden layer for air traffic volume prediction in uncontrolled airspace

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Paszyński P. , Gnyś P.

TASK Quarterly : scientific bulletin of Academic Computer Centre in Gdansk

2022

tom Vol. 26, No 3

1--7

This article presents a model enabling more efficient air traffic management achieved by better data use . Appropriate resource allocation is possible if it is based on a high quality air traffic volume forecast. The proposed approach is inspired by procedures used in flow management in air traffic control. Staff planning in controlled airspace is easier because almost all operations are communicated in the submitted flight plan. Short-term prediction of the number of operations in uncontrolled airspace is a much more challenging task. It is correlated with weather parameters and moreover, it naturally fluctuates throughout the day and the season. The relationship between General Aviation (GA) traffic volume and meteorological conditions were modeled using neural network. The obtained results confirm that it is possible to use the decision support system to plan the number of operational sectors. The described results open a scientific discussion about designing tools predicting air traffic volume in uncontrolled air space. The accuracy of the model can be improved by processing data from additional sources, but it is associated with a significant increase in the complexity of the solution.

Application of long short term memory neural networks for GPS Satellite clock bias prediction

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Gnyś P. , Przestrzelski P.

2021

tom Vol. 25, No 4

381--395

Satellite-based localization systems like GPS or Galileo are one of the most com-monly used tools in outdoor navigation. While for most applications, like car navigation orhiking, the level of precision provided by commercial solutions is satisfactory it is not alwaysthe case for mobile robots. In the case of long-time autonomy and robots that operate in re-mote areas battery usage and access to synchronization data becomes a problem. In this paper,a solution providing a real-time onboard clock synchronization is presented. Results achievedare better than the current state-of-the-art solution in real-time clock bias prediction for mostsatellites.