Using Azure Machine Learning Studio with Python Scripts for Induction Motors Optimization Web-Deploy Project
| Autor: | Andrey Karyuk, Vladyslav Pliuhin, Maria Sukhonos, Mycola Pan, Volodymyr Korobka |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Electric machine
0209 industrial biotechnology business.product_category business.industry Computer science Cloud computing 02 engineering and technology Reuse Python (programming language) computer.software_genre Machine learning Metadata 020901 industrial engineering & automation Scripting language 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Web service business computer Induction motor computer.programming_language |
| Zdroj: | 2019 IEEE International Scientific-Practical Conference Problems of Infocommunications, Science and Technology (PIC S&T). |
| Popis: | The use of computers now in almost all areas of activity for solving a wide variety of tasks, including optimization tasks, leads to the need for a specialist in this field to be able to correctly set and solve problems of this kind. The use of cloud computing Microsoft Azure Machine Learning (AML) Studio allows to develop predictive analytic projects efficiently and with minimal developer influence on the quality of calculations. In this paper, we consider the deployment of an induction motor with squirrel cage rotor optimization project. Compared with the standard method offered by the Microsoft AML service, a set of results of experimental data of a series of electric machines is used to evaluate the quality of the model and predicted target values. In addition, in a separate metadata block, the Python script for an electric machine automated design is included, considering the range of initial variables. Other metadata blocks in Python are used in order to generate series of initial data. It is also shown how to implement reuse of the created project with the cloud web service and Microsoft Excel tables. |
| Databáze: | OpenAIRE |
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