IMPLEMENTING OF MICROSOFT AZURE MACHINE LEARNING TECHNOLOGY FOR ELECTRIC MACHINES OPTIMIZATION
Keywords:electrical machines, optimization, algorithm, data set, machine learning, Microsoft Azure, cloud computing
AbstractPurpose. 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.
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).
How to Cite
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.
Authors who publish with this journal agree to the following terms:
1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.