Olga Dolinina & Igor Bessmertny 
Recent Research in Control Engineering and Decision Making 
Volume 2, 2020

This book constitutes the full research papers and short monographs developed on the base of the refereed proceedings of the International Conference: Information and Communication Technologies for Research and Industry (ICIT 2020).

The book brings accepted research papers which present mathematical modelling, innovative approaches and methods of solving problems in the sphere of control engineering and decision making for the various fields of studies: industry and research, energy efficiency and  sustainability, ontology-based data simulation, theory and use of digital signal processing, cognitive systems, robotics, cybernetics, automation control theory, image and sound processing, image recognition, technologies, and computer vision. The book contains also several analytical reviews on using smart city technologies in Russia.

The central audience of the book are researchers, industrial practitioners and students from the following areas: Adaptive Systems, Human–Robot Interaction, Artificial Intelligence, Smart City and Internet of Things, Information Systems, Mathematical Modelling, and the Information Sciences.

Table des matières

The Task of Controlling Robotic Technological Complexes of Arc Welding in Unstable States.- Longitudinal Waves in Two Coaxial Elastic Shells with Hard Cubic Nonlinearity.- Acceleration and Increase of Reliability of the Algorithm for Numerical Optimization of the PID.- PID Controller for Non-stationary Plants of Relative Second Order.- Adaptive Decision Support System for Scaling University Cloud Applications.- Application of a Combined Multi-Port Reflectometer to Precise Distance Measuring.- A Transformation-Based Approach for Fuzzy Knowledge Bases Engineering.- Mathematical Modeling of Hydroelastic Oscillations of Circular Sandwich Plate Resting on Winkler Foundation.- Numerical Simulation Results of the Optimal Estimation Algorithm for a Turn Table Angular Velocity.- Comparison of LSTM and GRU Recurrent Neural Network Architectures.