Software for the operator identification subsystem in a mobile simulator based on a neural network
Abstract
Software for the operator identification subsystem in a mobile simulator based on a neural network
Incoming article date: 27.03.2025The article presents the results of a study devoted to the development of an identification subsystem for an industrial process operator in a mobile simulator used for training and monitoring professional skills. The functional requirements for the operator identification subsystem based on neural network technologies, the processes of user interaction with the personality recognition subsystem, and loading a reference image for further identification of the operator during training and monitoring on the simulator are formalized using visual models in UML notation. A prototype of the subsystem has been developed based on the Kotlin programming language and the TensorFlow library. The developed image analysis subsystem has a high speed of face detection and initialization, reaching less than 0.5 s, which makes it especially effective for real-time tasks where performance plays a key role. Local data processing on mobile devices ensures protection of user privacy by eliminating data transfer to remote servers, which minimizes the risks of information leaks. Optimization of power consumption ensures long-term operation on devices with limited battery capacity, which makes the system convenient and practical to use. The considered subsystem is planned to be adapted for monitoring the formation of skills for working on equipment during operator training on mobile simulators. The subsystem, based on VR/AR technologies, as well as a trained neural network, will analyze data on user reactions in real time.
Keywords: mobile simulators, neural networks, user identification, professional training, UML diagrams, TensorFlow