In this study we investigate information processing in deep neural network models. We demonstrate that unsupervised training of autoencoder models of certain class can result in emergence of compact and structured internal representation of the input data space that can be correlated with higher level categories. We propose and demonstrate practical...

This chapter presents opportunities to use Artificial Intelligence (AI) in aviation and aerospace industries. The AI used an innovative technology for improving the effectiveness of building aviation systems in each stage of the lifecycle for enhancing the security of aviation systems and the characteristic ability to learn, improve, and predict difficult situations. The AI is presented in Air Navigation Sociotechnical system (ANSTS) because the activity of ANSTS, is accompanied by a high degree of risk of causing catastrophic outcomes. The operator's models of decision making in AI systems are presented such as Expert Systems, Decision Support Systems for pilots of manned and unmanned aircraft, air traffic controllers, engineers, etc. The quality of operator's decisions depends on the development and use of innovative technology of AI and related fields (Big Data, Data Mining, Multicriteria Decision Analysis, Collaboration Decision Making, Blockchain, Artificial Neural Network, etc.).

The authors present a problem of the optimization of Unmanned Aerial Vehicles flights for different tasks in the city. Using Central Drone Repeater in conjunction with geometrical modeling covering as the most suitable configuration of Unmanned Aerial Vehicles team (group) in the city, authors suggest improving the versatility, maintainability and operational safety of Unmanned Aerial Vehicles group via the introduction of a functional framework in the form of a functional-protocol stack, in remote analogy with Open Systems Interconnection model in telecommunications