МЕТОД УМЕНЬШЕНИЯ ДЕЙСТВИЯ СЛУЧАЙНОЙ КОМПОНЕНТЫ  ДЛЯ КОРОТКИХ ОДНОМЕРНЫХ ДАННЫХ,  ФОРМИРУЕМЫХ СЕНСОРНЫМИ СИСТЕМАМИ

Евгений Александрович Семенищев; Игорь Семёнович Шрайфель

doi:10.17308/sait/1995-5499/2025/4/20-33

Authors

Evgenii A. Semenishchev Moscow State University of Technology «STANKIN» https://orcid.org/0000-0001-9817-0021 (unauthenticated)
Igor S. Shraifel Institute of Services and Entrepreneurship (branch) of the Don State Technical University https://orcid.org/0009-0006-6121-8072 (unauthenticated)

DOI:

https://doi.org/10.17308/sait/1995-5499/2025/4/20-33

Keywords:

control systems, automation, data approximation, vector representation of signals, noise, primary processing, denoising

Abstract

The creation of industrial systems within the framework of the “Industry 4.0” con cept is impossible without precise control of the position of objects in space. Analysis of the information parameters of sensor units allows for the control of the position of robot elements, and in the case of machine vision — spatial objects and parts. Information on the position of objects and their components allows for the prediction of their movement based on accumulated knowledge and the embedded program. Analysis of the movement of a material point in space and the prediction of its position in the absence of a priori information about the process can be carried out based on machine learning or using computational methods. At the same time, the recorded data is subject to interference. The appearance of which is caused by many factors, such as: imperfections and the presence of impurities in the sensors; the influence of external fac tors, including electromagnetic interference, intersensory interaction, etc.; uneven accumulation and draining of charge, for example, for CCD matrices; failure; various data transformations, such as quantization noise, etc. To reduce the influence of noise, methods of data accumulation and averaging, neural networks, or computational methods can be used. This article discusses a method for reprocessing data based on the analysis of vector spaces and the generation of an optimal solution based on a combined criterion that minimizes the distance between the crite ria of maximum approximation, monotonicity, and minimum deviation of the difference in the norms of data vectors. An algorithm for the proposed approach to processing real-world data is presented. A description of the application of the resulting program implementation in process ing real-world data is provided for various practical applications, including forecasting, finding object boundaries in images, and filtering.

Author Biographies

Evgenii A. Semenishchev, Moscow State University of Technology «STANKIN»

Candidate of Technical Sciences, Associate Professor, Leading Researcher, Laboratory for the Development of Equipment for the Production of Electronic Component Base
Igor S. Shraifel, Institute of Services and Entrepreneurship (branch) of the Don State Technical University

Candidate of Physical and Mathematical Sciences, Associate Professor, Associate Professor of the Department of Mathematics and Applied Informatics

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