Matches in SemOpenAlex for { <https://semopenalex.org/work/W2943918897> ?p ?o ?g. }
Showing items 1 to 78 of
78
with 100 items per page.
- W2943918897 endingPage "28" @default.
- W2943918897 startingPage "23" @default.
- W2943918897 abstract "Purpose. To consider problems of electric machines optimization within a wide range of many variables variation as well as the presence of many calculation constraints in a single-criteria optimization search tasks. Results. A structural model for optimizing electric machines of arbitrary type using Microsoft Azure machine learning technology has been developed. The obtained results, using several optimization methods from the Microsoft Azure database are demonstrated. The advantages of cloud computing and optimization based on remote servers are shown. The results of statistical analysis of the results are given. Originality. Microsoft Azure machine learning technology was used for electrical machines optimization for the first time. Recommendations for modifying standard algorithms, offered by Microsoft Azure are given. Practical value. Significant time reduction and resources spent on the optimization of electrical machines in a wide range of variable variables. Reducing the time to develop optimization algorithms. The possibility of automatic statistical analysis of the results after performing optimization calculations." @default.
- W2943918897 created "2019-05-16" @default.
- W2943918897 creator A5002544097 @default.
- W2943918897 creator A5025528454 @default.
- W2943918897 creator A5027585613 @default.
- W2943918897 creator A5039734616 @default.
- W2943918897 creator A5079205346 @default.
- W2943918897 date "2019-02-17" @default.
- W2943918897 modified "2023-10-01" @default.
- W2943918897 title "IMPLEMENTING OF MICROSOFT AZURE MACHINE LEARNING TECHNOLOGY FOR ELECTRIC MACHINES OPTIMIZATION" @default.
- W2943918897 cites W205095828 @default.
- W2943918897 cites W2470547278 @default.
- W2943918897 cites W2545138826 @default.
- W2943918897 cites W2598455267 @default.
- W2943918897 cites W757745030 @default.
- W2943918897 doi "https://doi.org/10.20998/2074-272x.2019.1.04" @default.
- W2943918897 hasPublicationYear "2019" @default.
- W2943918897 type Work @default.
- W2943918897 sameAs 2943918897 @default.
- W2943918897 citedByCount "2" @default.
- W2943918897 countsByYear W29439188972019 @default.
- W2943918897 countsByYear W29439188972020 @default.
- W2943918897 crossrefType "journal-article" @default.
- W2943918897 hasAuthorship W2943918897A5002544097 @default.
- W2943918897 hasAuthorship W2943918897A5025528454 @default.
- W2943918897 hasAuthorship W2943918897A5027585613 @default.
- W2943918897 hasAuthorship W2943918897A5039734616 @default.
- W2943918897 hasAuthorship W2943918897A5079205346 @default.
- W2943918897 hasBestOaLocation W29439188971 @default.
- W2943918897 hasConcept C111919701 @default.
- W2943918897 hasConcept C127413603 @default.
- W2943918897 hasConcept C134306372 @default.
- W2943918897 hasConcept C146978453 @default.
- W2943918897 hasConcept C182365436 @default.
- W2943918897 hasConcept C204323151 @default.
- W2943918897 hasConcept C3019730874 @default.
- W2943918897 hasConcept C33923547 @default.
- W2943918897 hasConcept C41008148 @default.
- W2943918897 hasConcept C523788702 @default.
- W2943918897 hasConcept C79974875 @default.
- W2943918897 hasConcept C93996380 @default.
- W2943918897 hasConceptScore W2943918897C111919701 @default.
- W2943918897 hasConceptScore W2943918897C127413603 @default.
- W2943918897 hasConceptScore W2943918897C134306372 @default.
- W2943918897 hasConceptScore W2943918897C146978453 @default.
- W2943918897 hasConceptScore W2943918897C182365436 @default.
- W2943918897 hasConceptScore W2943918897C204323151 @default.
- W2943918897 hasConceptScore W2943918897C3019730874 @default.
- W2943918897 hasConceptScore W2943918897C33923547 @default.
- W2943918897 hasConceptScore W2943918897C41008148 @default.
- W2943918897 hasConceptScore W2943918897C523788702 @default.
- W2943918897 hasConceptScore W2943918897C79974875 @default.
- W2943918897 hasConceptScore W2943918897C93996380 @default.
- W2943918897 hasIssue "1" @default.
- W2943918897 hasLocation W29439188971 @default.
- W2943918897 hasLocation W29439188972 @default.
- W2943918897 hasLocation W29439188973 @default.
- W2943918897 hasLocation W29439188974 @default.
- W2943918897 hasLocation W29439188975 @default.
- W2943918897 hasOpenAccess W2943918897 @default.
- W2943918897 hasPrimaryLocation W29439188971 @default.
- W2943918897 hasRelatedWork W1749786217 @default.
- W2943918897 hasRelatedWork W2097699355 @default.
- W2943918897 hasRelatedWork W2371023786 @default.
- W2943918897 hasRelatedWork W2383532021 @default.
- W2943918897 hasRelatedWork W2909650725 @default.
- W2943918897 hasRelatedWork W2911113383 @default.
- W2943918897 hasRelatedWork W2981693706 @default.
- W2943918897 hasRelatedWork W3006227554 @default.
- W2943918897 hasRelatedWork W1749677908 @default.
- W2943918897 hasRelatedWork W2269917415 @default.
- W2943918897 hasVolume "0" @default.
- W2943918897 isParatext "false" @default.
- W2943918897 isRetracted "false" @default.
- W2943918897 magId "2943918897" @default.
- W2943918897 workType "article" @default.