Activities for the Implementation of Machine-Readable Standards in the Field of Military and Space Activities Using Ontological Engineering Methods

In modern conditions of standardization, new tasks arise, which involve the transition of international and regional standards to the digital format of "smart" standards that can be used as full-fledged guidelines for industrial facilities. The introduction of artificial intelligence and m...

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Bibliographic Details
Published in:Journal of electrical engineering & technology Vol. 19; no. 1; pp. 799 - 807
Main Authors: Utegenova, Anar, Yermoldina, Gulnaz, Suimenbayeva, Zhanna, Aden, Alisher, Naumenko, Vitaliy, Kang, Jeong Won
Format: Journal Article
Language:English
Published: Singapore Springer Nature Singapore 01.01.2024
Springer Nature B.V
대한전기학회
Subjects:
Electrical Engineering
Electrical Machines and Networks
Electronics and Microelectronics
Engineering
Instrumentation
Original Article
Power Electronics
전기공학
Algorithm for determining keywords
Categorization of standards
National standards
Smart-standards
Aerospace industry
Harmonization of standards
ISSN:1975-0102, 2093-7423
Online Access:Get full text
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Summary:In modern conditions of standardization, new tasks arise, which involve the transition of international and regional standards to the digital format of "smart" standards that can be used as full-fledged guidelines for industrial facilities. The introduction of artificial intelligence and machine learning in the standardization process will increase the level of automation and allow analyzing data presented in the form of a database, which contributes to better compatibility between systems and equipment of different manufacturers. This article presents a method of ontological design, which automates partial methodological examination of documents in accordance with the National Standardization Plan. As part of the experiment, an independent tool was developed from the existing repositories of regulatory documents. This approach is based on the extraction of qualitative data from textual information using an unsupervised automatic keyword extraction algorithm. The algorithm is based on statistical characteristics of local text extracted from individual documents. The advantage of the algorithm is that it does not require the creation of a learning corpus and can be applied to any text, and also has no limitations on the volume of processed data. The following components are presented in the article: model classification standards, entity extraction module, categorization module, thesaurus generation module and monitoring and analysis module. The calculation was carried out using formalization, definition of the target function, establishment of the similarity measure, as well as description of the features of the document. The obtained results were tested, based on standards and rules in the field of the aerospace industry.
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ISSN:1975-0102
2093-7423
DOI:10.1007/s42835-023-01704-8