Prognosis Method of Unfavorable Airborne Events During Flight Based on Convolutional and Recurrent Neural Networks

Abstract

This paper contains formal problem definition of predicting unfavorable airborne events during flight. Restrictions and assumptions are put into the prognosis method of unfavorable airborne events during flight. Mathematical apparatus used to build prognosis method is suggested. As a basic mathematical apparatus it is suggested to use, recurrent neural networks (RNN) based on LSTM modules and convolutional neural networks (CNN). Analysis of these neural networks has shown that RNN based on LSTM modules are mostly effective when analyzing structured text, such as report of investigation of airborne accidents. In its turn, CNN are effective when analyzing unstructured text, such as text messages about the flight situation based on the information from external sources. Prognosis method of unfavorable airborne events during flight based on convolutional and recurrent neural networks is developed. In case of solving the task of prediction of unfavorable airborne events during flight RNN are used for initial setup of the Embedding layer of the structured training data in the process of hybrid neural network training. CNN are used during the direct operation of hybrid neural network model of prediction of unfavorable airborne events during flight. K e ywor d s : deep neural network; convolutional neural network; recurrent neural network; prognosis; unfavorable airborne event; hyperparameter; accuracy factor.

Sports Monitoring Method of National Sports Events Based on Wireless Sensor Network

“…Compared to other worlds, people in the tropics are more prone to warmer climates, which leads to the very strong operation of the heating system, therefore produces long-distance running, long jump, and other projects, which leads to the detection time to pay attention to speed and athletes' cardiopulmonary conditions; while the people in the cold zone, their sports are to drive the cold and hunt, so they are very adaptable to cold weather and are more good at sports on ice, which makes it necessary to consider the athlete's body temperature when testing. Situation: people living near water sources, most of their daily food sources come from the water, which leads to their very good water quality, and they are more talented in swimming, diving, and other sports than other places [25].…”