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...
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| Datum: | 2019 |
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| Format: | Artikel |
| Sprache: | English |
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National Technical University "Kharkiv Polytechnic Institute" and Аnatolii Pidhornyi Institute of Power Machines and Systems of NAS of Ukraine
2019
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| Online Zugang: | http://eie.khpi.edu.ua/article/view/2074-272X.2019.1.04 |
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| Назва журналу: | Electrical Engineering & Electromechanics |
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eiekhpieduua-article-1568162019-02-17T17:29:46Z IMPLEMENTING OF MICROSOFT AZURE MACHINE LEARNING TECHNOLOGY FOR ELECTRIC MACHINES OPTIMIZATION IMPLEMENTING OF MICROSOFT AZURE MACHINE LEARNING TECHNOLOGY FOR ELECTRIC MACHINES OPTIMIZATION Pliugin, V. E. Sukhonos, M. Pan, M. Petrenko, A. N. Petrenko, N. Ya. electrical machines optimization algorithm data set machine learning Microsoft Azure cloud computing 629.429.3 621.313 электрические машины оптимизация алгоритм набор данных машинное обучение Microsoft Azure облачные расчеты 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. Рассмотрены проблемы оптимизации электрических машин при широком диапазоне варьирования многих переменных, наличии большого числа вычисляемых ограничений, в однокритериальных задачах оптимизационного поиска. Разработана структурная модель оптимизации электрических машин произвольного типа с применением технологии машинного обучения Microsoft Azure. Продемонстрированы результаты, полученные с использованием нескольких методов оптимизации из базы Microsoft Azure. Показаны преимущества облачных расчетов и оптимизации на базе удаленных серверов. Приведенные результаты касаются решения однокритериальной задачи оптимизации с двумя переменными. Даны результаты статистического анализа полученных результатов. Даны рекомендации по применению машинного обучения Microsoft Azure в проектировании и оптимизации электрических машин. National Technical University "Kharkiv Polytechnic Institute" and Аnatolii Pidhornyi Institute of Power Machines and Systems of NAS of Ukraine 2019-02-17 Article Article application/pdf http://eie.khpi.edu.ua/article/view/2074-272X.2019.1.04 10.20998/2074-272X.2019.1.04 Electrical Engineering & Electromechanics; No. 1 (2019); 23-28 Электротехника и Электромеханика; № 1 (2019); 23-28 Електротехніка і Електромеханіка; № 1 (2019); 23-28 2309-3404 2074-272X en http://eie.khpi.edu.ua/article/view/2074-272X.2019.1.04/156168 Copyright (c) 2019 V. E. Pliugin, M. Sukhonos, M. Pan, A. N. Petrenko, N. Ya. Petrenko https://creativecommons.org/licenses/by-nc/4.0 |
| institution |
Electrical Engineering & Electromechanics |
| baseUrl_str |
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| datestamp_date |
2019-02-17T17:29:46Z |
| collection |
OJS |
| language |
English |
| topic |
electrical machines optimization algorithm data set machine learning Microsoft Azure cloud computing 629.429.3 621.313 |
| spellingShingle |
electrical machines optimization algorithm data set machine learning Microsoft Azure cloud computing 629.429.3 621.313 Pliugin, V. E. Sukhonos, M. Pan, M. Petrenko, A. N. Petrenko, N. Ya. IMPLEMENTING OF MICROSOFT AZURE MACHINE LEARNING TECHNOLOGY FOR ELECTRIC MACHINES OPTIMIZATION |
| topic_facet |
electrical machines optimization algorithm data set machine learning Microsoft Azure cloud computing 629.429.3 621.313 электрические машины оптимизация алгоритм набор данных машинное обучение Microsoft Azure облачные расчеты 629.429.3 621.313 |
| format |
Article |
| author |
Pliugin, V. E. Sukhonos, M. Pan, M. Petrenko, A. N. Petrenko, N. Ya. |
| author_facet |
Pliugin, V. E. Sukhonos, M. Pan, M. Petrenko, A. N. Petrenko, N. Ya. |
| author_sort |
Pliugin, V. E. |
| title |
IMPLEMENTING OF MICROSOFT AZURE MACHINE LEARNING TECHNOLOGY FOR ELECTRIC MACHINES OPTIMIZATION |
| 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 |
| title_alt |
IMPLEMENTING OF MICROSOFT AZURE MACHINE LEARNING TECHNOLOGY FOR ELECTRIC MACHINES OPTIMIZATION |
| 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. |
| publisher |
National Technical University "Kharkiv Polytechnic Institute" and Аnatolii Pidhornyi Institute of Power Machines and Systems of NAS of Ukraine |
| publishDate |
2019 |
| url |
http://eie.khpi.edu.ua/article/view/2074-272X.2019.1.04 |
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2025-07-17T11:47:28Z |
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2025-07-17T11:47:28Z |
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