Implementing of Microsoft Azure machine learning technology for electric machines optimization
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...
Gespeichert in:
| Veröffentlicht in: | Електротехніка і електромеханіка |
|---|---|
| Datum: | 2019 |
| Hauptverfasser: | , , , , |
| Format: | Artikel |
| Sprache: | English |
| Veröffentlicht: |
Інститут технічних проблем магнетизму НАН України
2019
|
| Schlagworte: | |
| Online Zugang: | https://nasplib.isofts.kiev.ua/handle/123456789/159032 |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Назва журналу: | Digital Library of Periodicals of National Academy of Sciences of Ukraine |
| Zitieren: | Implementing of Microsoft Azure machine learning technology for electric machines optimization / V. Pliuhin, M. Sukhonos, M. Pan, O. Petrenko, M. Petrenko // Електротехніка і електромеханіка. — 2019. — № 1. — С. 23-28. — Бібліогр.: 20 назв. — англ. |
Institution
Digital Library of Periodicals of National Academy of Sciences of Ukraine| id |
nasplib_isofts_kiev_ua-123456789-159032 |
|---|---|
| record_format |
dspace |
| spelling |
Pliuhin, V. Sukhonos, M. Pan, M. Petrenko, O. Petrenko, M. 2019-09-20T19:31:38Z 2019-09-20T19:31:38Z 2019 Implementing of Microsoft Azure machine learning technology for electric machines optimization / V. Pliuhin, M. Sukhonos, M. Pan, O. Petrenko, M. Petrenko // Електротехніка і електромеханіка. — 2019. — № 1. — С. 23-28. — Бібліогр.: 20 назв. — англ. 2074-272X DOI: https://doi.org/10.20998/2074-272X.2019.1.04 https://nasplib.isofts.kiev.ua/handle/123456789/159032 629.429.3:621.313 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. en Інститут технічних проблем магнетизму НАН України Електротехніка і електромеханіка Електричні машини та апарати Implementing of Microsoft Azure machine learning technology for electric machines optimization Article published earlier |
| institution |
Digital Library of Periodicals of National Academy of Sciences of Ukraine |
| collection |
DSpace DC |
| title |
Implementing of Microsoft Azure machine learning technology for electric machines optimization |
| spellingShingle |
Implementing of Microsoft Azure machine learning technology for electric machines optimization Pliuhin, V. Sukhonos, M. Pan, M. Petrenko, O. Petrenko, M. Електричні машини та апарати |
| title_short |
Implementing of Microsoft Azure machine learning technology for electric machines optimization |
| title_full |
Implementing of Microsoft Azure machine learning technology for electric machines optimization |
| title_fullStr |
Implementing of Microsoft Azure machine learning technology for electric machines optimization |
| title_full_unstemmed |
Implementing of Microsoft Azure machine learning technology for electric machines optimization |
| title_sort |
implementing of microsoft azure machine learning technology for electric machines optimization |
| author |
Pliuhin, V. Sukhonos, M. Pan, M. Petrenko, O. Petrenko, M. |
| author_facet |
Pliuhin, V. Sukhonos, M. Pan, M. Petrenko, O. Petrenko, M. |
| topic |
Електричні машини та апарати |
| topic_facet |
Електричні машини та апарати |
| publishDate |
2019 |
| language |
English |
| container_title |
Електротехніка і електромеханіка |
| publisher |
Інститут технічних проблем магнетизму НАН України |
| format |
Article |
| description |
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.
|
| issn |
2074-272X |
| url |
https://nasplib.isofts.kiev.ua/handle/123456789/159032 |
| citation_txt |
Implementing of Microsoft Azure machine learning technology for electric machines optimization / V. Pliuhin, M. Sukhonos, M. Pan, O. Petrenko, M. Petrenko // Електротехніка і електромеханіка. — 2019. — № 1. — С. 23-28. — Бібліогр.: 20 назв. — англ. |
| work_keys_str_mv |
AT pliuhinv implementingofmicrosoftazuremachinelearningtechnologyforelectricmachinesoptimization AT sukhonosm implementingofmicrosoftazuremachinelearningtechnologyforelectricmachinesoptimization AT panm implementingofmicrosoftazuremachinelearningtechnologyforelectricmachinesoptimization AT petrenkoo implementingofmicrosoftazuremachinelearningtechnologyforelectricmachinesoptimization AT petrenkom implementingofmicrosoftazuremachinelearningtechnologyforelectricmachinesoptimization |
| first_indexed |
2025-12-07T17:52:36Z |
| last_indexed |
2025-12-07T17:52:36Z |
| _version_ |
1850872909557399552 |