The presented study shows the impact of changes in the content of continuing education programs on the assessment of the quality of courses by students in the center of additional vocational education. The developed system of evaluation and input and output questionnaires made it possible to identify point changes that need to be implemented to improve the quality of both the course attendees and the customers. The presented system of impact on the quality of professional development through continuous balancing of the thematic content of the program of additional professional education allows managing the development of the competencies of employees. Point changes in the proportions of the content blocks of the programs made it possible to achieve the optimal set of topics that satisfies the needs of the customer in terms of developing certain competences in the employees, as well as the course participants themselves in terms of interest in learning and personal growth. The developed approach makes it possible for the company of the customer to introduce a system of continuous professional training, advanced training and retraining of personnel by controlling the level of competence of trained employees to improve performance.

The research is dedicated to the development of special devices (capsules) that can be used to control the mining ore behavior in the technological unit in order to increase processes efficiency. In the first part of the article, the choice of the discrete element method for gen-erating various particle trajectories in the unit (drum pelletizer) was substantiated. This part describes the specific technologies that were used to recognize the pelletizing mode. In par-ticular, conversation of paths to sensor readings is implemented using the Matlab Sensor Fusion and Tracking Toolbox. The obtained readings were processed using two neural network classifiers (DNN and LSTM). As a result, stable models for recognizing the pelletizing modes of the unit were obtained. LSTM recognition accuracy is greater than DNN. The developed approach can be used to recognize the operating modes of other technological units. In addition, data on particles trajectories can be used to improve DEM models of technological processes. Future work consists of the capsule physical implementation and testing the recognition algorithm on a real unit.

The paper presents a methodology for training neural networks for vision tasks on synthe-sized data on the example of steel defect recognition in automated production control systems. The article describes the process of dataset procedural generation of steel slab defects with a symmetrical distribution. The results of training two neural networks Unet and Xception on a generated data grid and testing them on real data are presented. The performance of these neural networks was assessed using real data from the Severstal: Steel Defect Detection set. In both cases, the neural networks showed good results in the classification and segmentation of surface defects of steel workpieces in the image. Dice score on synthetic data reaches 0.62, and accuracy—0.81.