DOI: https://doi.org/10.20998/2074-272X.2019.1.04

IMPLEMENTING OF MICROSOFT AZURE MACHINE LEARNING TECHNOLOGY FOR ELECTRIC MACHINES OPTIMIZATION

V. E. Pliugin, M. Sukhonos, M. Pan, A. N. Petrenko, N. Ya. Petrenko

Анотація


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. 

Ключові слова


электрические машины; оптимизация; алгоритм; набор данных; машинное обучение; Microsoft Azure; облачные расчеты

Повний текст:

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Посилання


1. Sen S.K. Principles Of Electrical Machine Design With Computer Programs. Oxford, IBH Publishing Company Pvt. Limited, 2006. 415 p.

2. Rekleitis G., Reivindran A., Regsdel K. Optimizatsiia v tekhnike [Optimization in technology]. Мoscow, Mir Publ., 1986. 351 p. (Rus).

3. Goriagin V.F., Zagriadskii V.I., Sycheva T.A. Optimal'noe proektirovanie asinkhronnykh vzryvozashchishchennykh dvigatelei [Optimal design of asynchronous explosion-proof motors]. Kishinev, Shtiitsa Publ., 1980. 200 p. (Rus).

4. Zablodskiy N., Lettl J., Pliugin V., Buhr K., Khomitskiy S. Induction Motor Optimal Design by Use of Cartesian Product. Transactions on electrical engineering, 2013, no.2, pp. 54-58.

5. Zablodskiy N., Lettl J., Pliugin V., Buhr K., Khomitskiy S. Induction Motor Design by Use of Genetic Optimization Algorithms. Transactions on electrical engineering, 2013, no.3, pp. 65-69.

6. Zablodskii N.N., Pliugin V.E., Petrenko A.N. Using object-oriented design principles in electric machines development. Electrical engineering & electromechanics, 2016, no.1, pp. 17-20. doi: 10.20998/2074-272X.2016.1.03.

7. Papalambros P.Y., Wilde D.J. Principles of optimal design. Cambridge University Press, 2017. doi: 10.1017/9781316451038.

8. Severin V.P., Nikulina E.N. Metody odnomernogo poiska [Methods of one-dimensional search]. Kharkiv, NTU KhPI Publ., 2013. 124 p. (Rus).

9. Todorov E. Optimal control theory. Bayesian Brain: Probabilistic Approaches to Neural Coding, 2006, chap. 12, pp. 268-298. doi: 10.7551/mitpress/9780262042383.003.0012.

10. Kappen H.J. Optimal control theory and the linear Bellman equation. Bayesian Time Series Models, 2011, pp. 363-387. doi: 10.1017/cbo9780511984679.018.

11. Kanemoto Y. Theories of urban externalities.Holland, North-Holland Publ., 1980. 189 p.

12. Satit Owatchaiphong, Nisai Fuengwarodsakul. Multi-Objective Design for Switched Reluctance Machines Using Genetic and Fuzzy Algorithms. The ECTI Transactions on Electrical Engineering, Electronics, and Communications, 2013, vol.11, no.2, pp. 1530-1533.

13. Chappel D. Introduction Azure machine learning: a guide for technical professionals. Chappel & Associates, 2015.

14. Collier M., Shahan R. Microsoft Azure Essentials. Microsoft Press, 2016.

15. Bloch J. Effective Java.Canada, Sun Microsystems, 2008.

16. What is Azure machine learning studio? Available at: https://docs.microsoft.com/en-us/azure/machinelearning/studio/what-is-ml-studio (Accessed 10 May 2018).

17. Hayakawa S., Hayashi H. Using Azure Machine Learning for Estimating Indoor Locations. 2017 International Conference on Platform Technology and Service (PlatCon), Busan, 2017, pp. 1-4. doi: 10.1109/platcon.2017.7883736.

18. Azure Machine Learning. Available at: https://azure.microsoft.com/en-us/services/machine-learning-studio/ (Accessed 22 April 2018).

19. Dig Deep with Azure Machine Learning. Available at: https://studio.azureml.net/ (Accessed 16 February 2018).

20. Microsoft Azure Machine Learning: Algorithm Cheat Sheet. Available at: http://aka.ms/MLCheatSheet (Accessed 05 March 2018).


Пристатейна бібліографія ГОСТ


1.     Sen S.K. Principles Of Electrical Machine Design With Computer Programs. Oxford, IBH Publishing Company Pvt. Limited, 2006. 415 p.
2.     Rekleitis G., Reivindran A., Regsdel K. Optimizatsiia v tekhnike [Optimization in technology]. Мoscow, Mir Publ., 1986. 351 p. (Rus).
3.     Goriagin V.F., Zagriadskii V.I., Sycheva T.A. Optimal'noe proektirovanie asinkhronnykh vzryvozashchishchennykh dvigatelei [Optimal design of asynchronous explosion-proof motors]. Kishinev, Shtiitsa Publ., 1980. 200 p. (Rus).
4.     Zablodskiy N., Lettl J., Pliugin V., Buhr K., Khomitskiy S. Induction Motor Optimal Design by Use of Cartesian Product. Transactions on electrical engineering, 2013, no.2, pp. 54-58.
5.     Zablodskiy N., Lettl J., Pliugin V., Buhr K., Khomitskiy S. Induction Motor Design by Use of Genetic Optimization Algorithms. Transactions on electrical engineering, 2013, no.3, pp. 65-69.
6.     Zablodskii N.N., Pliugin V.E., Petrenko A.N. Using object-oriented design principles in electric machines development. Electrical engineering & electromechanics, 2016, no.1, pp. 17-20. doi: 10.20998/2074-272X.2016.1.03.
7.     Papalambros P.Y., Wilde D.J. Principles of optimal design. Cambridge University Press, 2017. doi: 10.1017/9781316451038.
8.     Severin V.P., Nikulina E.N. Metody odnomernogo poiska [Methods of one-dimensional search]. Kharkiv, NTU KhPI Publ., 2013. 124 p. (Rus).
9.     Todorov E. Optimal control theory. Bayesian Brain: Probabilistic Approaches to Neural Coding, 2006, chap. 12, pp. 268-298. doi: 10.7551/mitpress/9780262042383.003.0012.
10.  Kappen H.J. Optimal control theory and the linear Bellman equation. Bayesian Time Series Models, 2011, pp. 363-387. doi: 10.1017/cbo9780511984679.018.
11.  Kanemoto Y. Theories of urban externalities.Holland, North-Holland Publ., 1980. 189 p.
12.  Satit Owatchaiphong, Nisai Fuengwarodsakul. Multi-Objective Design for Switched Reluctance Machines Using Genetic and Fuzzy Algorithms. The ECTI Transactions on Electrical Engineering, Electronics, and Communications, 2013, vol.11, no.2, pp. 1530-1533.
13.  Chappel D. Introduction Azure machine learning: a guide for technical professionals. Chappel & Associates, 2015.
14.  Collier M., Shahan R. Microsoft Azure Essentials. Microsoft Press, 2016.
15.  Bloch J. Effective Java.Canada, Sun Microsystems, 2008.
16.  What is Azure machine learning studio? Available at: https://docs.microsoft.com/en-us/azure/machinelearning/studio/what-is-ml-studio (Accessed 10 May 2018).
17.  Hayakawa S., Hayashi H. Using Azure Machine Learning for Estimating Indoor Locations. 2017 International Conference on Platform Technology and Service (PlatCon), Busan, 2017, pp. 1-4. doi: 10.1109/platcon.2017.7883736.
18.  Azure Machine Learning. Available at: https://azure.microsoft.com/en-us/services/machine-learning-studio/ (Accessed 22 April 2018).
19.  Dig Deep with Azure Machine Learning. Available at: https://studio.azureml.net/ (Accessed 16 February 2018).
20.  Microsoft Azure Machine Learning: Algorithm Cheat Sheet. Available at: http://aka.ms/MLCheatSheet (Accessed 05 March 2018).




Copyright (c) 2019 V. E. Pliugin, M. Sukhonos, M. Pan, A. N. Petrenko, N. Ya. Petrenko


This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

ISSN 2074–272X (Print)
ІSSN 2309–3404 (Online)