This article presents the results of the optimization of steam generator control systems powered by mixtures of liquid fuels containing biofuels. The numerical model was based on the results of experimental research of steam generator operation in an open system. The numerical model is used to build control algorithms that improve performance, increase efficiency, reduce fuel consumption and increase safety in the full range of operation of the steam generator and the cogeneration system of which it is a component. In this research, the following parameters were monitored: temperature and pressure of the circulating medium, exhaust gas temperature, oxygen content in exhaust gas, percentage control of oil burner power. Two methods of controlling the steam generator were proposed: the classic one, using the PID regulator, and the advanced one, using artificial neural networks. The work shows how the model is adapted to the real system and the impact of the control algorithms on the efficiency of the combustion process. The example is considered for the implementation of advanced control systems in micro-, small- and medium-power cogeneration and trigeneration systems in order to improve their final efficiency and increase the profitability of implementation.

This article presents the application of a self-excited acoustic SAS system for non-destructive testing (NDT) for roof-bolt housings in laboratory and real mine conditions. The proposed system with a filtering mechanism was applied to the J64-27 composite anchors. The conducted tests allowed successful confirmation of the usefulness of the system in the detection of rod defects, damage of the mechanism coupling the anchor to the rock mass and testing of the stress state of the anchor itself. The proposed filtering system allowed eliminating the effect of jump change of frequency in the limit cycle of self-excited system. The proposed method is a novel solution for safety diagnostics of bolt housings in mining applications.

This paper presents the results of a neural convolutional system for recognizing the wearing of a mask by people entering a building. The algorithm is provided with input data thanks to cameras placed in the humanoid robot COVIDguard. The data collected by the humanoid -the temperature of people entering the facility, the location of the person, the way the protective mask was applied -are stored in the cloud, which enables the application of advanced image recognition algorithms and, consequently, the tracking of people within the range of the robot's sensory systems by the administrator and the verification of the security level in the given premises. The paper presents the architecture of the intelligent COVIDguard platform, the structure of the sensory system and the results of the neural network learning.

This paper presents an artificial intelligence algorithm responsible for the autonomy of a platform. The proposed algorithm allows the platform to move from an initial position to a set one without human intervention and with understanding and response to the dynamic environment. The implementation of such a task is possible by using a combination of a camera identifying the environment with a laser LIDAR sensor and a vision system. The signals from the sensors are analysed through convolutional neural networks. Based on AI inference, the platform makes decisions, including determining the optimal path for itself. A transfer learning method will be used to teach the neural network. This article presents the results of learning the applied neural algorithm.

This paper presents the results of research on a neural classifier system for the recognition of electrical drive disturbances and its speed. The classification task is performed by a convolutional neural network implemented on an artificial intelligence accelerator. The operation of the drive is monitored using intelligent accelerometers. Based on the sensor data, it is possible to create spectrograms of the signal using Short Time Fourier Transformation. Since convolutional neural networks are excellent at recognizing images and detecting objects, the signal spectrograms were used as input data for learning the network. Transfer learning method has been used, to create a model of neural network.