ACCENTED VISUALIZATION IN INTELLIGENT SYSTEMS OF INDUSTIAL CONTROL 

Ivashchenko Anton Vladimirovich, doctor of technical sciences, professor, sub-department of computing science, Samara State Technical University (244 Molodogvardeyskaya street, Samara, Russia), E-mail: anton.ivashenko@gmail.com
Sitnikov Pavel Vladimirovich, candidate of technical sciences, project management director, SEC “Open code” (55 Yarmarochnaya street, Samara, Russia), E-mail: sitnikov@o-code.ru 

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Background. Research is devoted to the implementation of Augmented Reality technologies in automated systems of intellectual control of manual operations at modern production enterprises. The aim of the work is to improve the quality of recognition of details and their parts, fragments and components by amplification of artificial neural networks by a semantic analyzer that controls the course of the production process and considers the individual characteristics of the operator’s perception.
Мethods. Semantic analyzer is based on representation of engineering product data in Ontology as a knowledge base. The proposed solution implements the method of accented visualization, which provides formation of virtual objects, marks and explanations for attracting the attention of users by Augmented Reality interfaces. The method is based on formalization of the scenario, focus, context and overlay context, which allows to specify the situation and narrow down the list of possible solutions for the objects’ recognition.
Results. As an example of the successful implementation of such a solution, there are presented the results of the development
of an intelligent control system for manual operations in automotive industrial engineering. 
Conclusions. The proposed method provides management of the attention of users in augmented reality systems. The achieved results are recommended for use in intelligent systems of production control according to the concept of Industry 4.0 at the scientific and industrial enterprises of mechanical engineering. 

Industry 4.0, Augmented Reality, accented visualization, image recognition