The learning path to neural network industrial application in distributed environments

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27230%2F21%3A10248432" target="_blank" >RIV/61989100:27230/21:10248432 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/2227-9717/9/12/2247" target="_blank" >https://www.mdpi.com/2227-9717/9/12/2247</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/pr9122247" target="_blank" >10.3390/pr9122247</a>

Result language

angličtina

Original language name

The learning path to neural network industrial application in distributed environments

Original language description

Industrial companies focus on efficiency and cost reduction, which is very closely related to production process safety and secured environments enabling production with reduced risks and minimized cost on machines maintenance. Legacy systems are being replaced with new systems built into distributed production environments and equipped with machine learning algorithms that help to make this change more effective and efficient. A distributed control system consists of several subsystems distributed across areas and sites requiring application interfaces built across a control network. Data acquisition and data processing are challenging processes. This contribution aims to present an approach for the data collection based on features standardized in industry and for data classification processed with an applied machine learning algorithm for distinguishing exceptions in a dataset. Files with classified exceptions can be used to train prediction models to make forecasts in a large amount of data. (C) 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20301 - Mechanical engineering

Project

<a href="/en/project/EF16_019%2F0000867" target="_blank" >EF16_019/0000867: Research Centre of Advanced Mechatronic Systems</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Processes

ISSN

2227-9717

e-ISSN

—

Volume of the periodical

9

Issue of the periodical within the volume

12

Country of publishing house

CH - SWITZERLAND

Number of pages

16

Pages from-to

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UT code for WoS article

000737407900001

EID of the result in the Scopus database

2-s2.0-85121731731