The paper analyzes the scientific work on fire protection, fire resistance, mathematical modeling of fire-proof properties, mathematical planning of experiments. The factors determining the efficiency of fire-proof coating have been determined. The experimental technique for determining fire-proof efficiency as an output parameter was selected. A factor space was constructed, and an experimental plan was drawn up. Experimental studies of the fire-proof effect of the coating based on the xerogel of the gel-forming system at all points of the factor space were carried out. A regression equation was obtained that describes the effect of the qualitative and quantitative composition of the coating on its fire-proof efficiency.

Production of waxes from spent perlite, which is a waste of sunflower oil winterization, is studied. Winterization is characterized by significant losses of oil with filter powders, and waste utilization is an environmental and economic problem. At the same time, winterization waste contains valuable components -wax and oil, which can be used in different ways. The content of waxes in spent perlite using hexane (18 %), as well as the quality indicators of the obtained wax: melting point 70 °C, saponification number 115 mg KOH/g, acid number 2.6 mg KOH/g, mass fraction of moisture 0,82 % are determined. Spent perlite was treated with a solution of sodium chloride during boiling, settling of the obtained mass, washing and drying of wax. The dependence of the yield and melting point of the extracted waxes on the processing parameters: the concentration of sodium chloride solution, temperature and duration of settling is found. Rational conditions for spent perlite processing are determined: the concentration of sodium chloride solution -7.5 %, settling temperature -20 °C, settling duration -10 hours. The experimentally determined wax yield at this point is 14.3 %. Quality indicators of the wax sample obtained under rational conditions are studied: melting point 68 °С, saponification number 110 mg KOH/g, acid number 2.8 mg KOH/g, mass fraction of moisture 0.85 %. These values correlate with the data for wax extracted using hexane, as well as with reference data on the quality of beeswax and sunflower wax. The data obtained allow recycling spent perlite without organic solvents, which makes the process more environmentally friendly and cost-effective, as well as solves environmental problems associated with the utilization of winterization waste

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Detection and recognition of objects in images is the main problem to be solved by computer vision systems. As part of solving this problem, the model of object recognition in aerial photographs taken from unmanned aerial vehicles has been improved. A study of object recognition in aerial photographs using deep convolutional neural networks has been carried out. Analysis of possible implementations showed that the AlexNet 2012 model (Canada) trained on the ImageNet image set (China) is most suitable for this problem solution. This model was used as a basic one. The object recognition error for this model with the use of the ImageNet test set of images amounted to 15 %. To solve the problem of improving the effectiveness of object recognition in aerial photographs for 10 classes of images, the final fully connected layer was modified by rejection from 1,000 to 10 neurons and additional two-stage training of the resulting model. Additional training was carried out with a set of images prepared from aerial photographs at stage 1 and with a set of VisDrone 2021 (China) images at stage 2. Optimal training parameters were selected: speed (step) (0.0001), number of epochs (100). As a result, a new model under the proposed name of AlexVisDrone was obtained. The effectiveness of the proposed model was checked with a test set of 100 images for each class (the total number of classes was 10). Accuracy and sensitivity were chosen as the main indicators of the model effectiveness. As a result, an increase in recognition accuracy from 7 % (for images from aerial photographs) to 9 % (for the VisDrone 2021 set) was obtained which has indicated that the choice of neural network architecture and training parameters was correct. The use of the proposed model makes it possible to automate the process of object recognition in aerial photographs. In the future, it is advisable to use this model at ground stations of unmanned aerial vehicle complex control when processing aerial photographs taken from unmanned aerial vehicles, in robotic systems, in video surveillance complexes and when designing unmanned vehicle systems