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:
| Datum: | 2019 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Artikel |
| Sprache: | English |
| Veröffentlicht: |
Інститут технічних проблем магнетизму НАН України
2019
|
| Schriftenreihe: | Електротехніка і електромеханіка |
| Schlagworte: | |
| Online Zugang: | http://dspace.nbuv.gov.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 |
irk-123456789-159032 |
|---|---|
| record_format |
dspace |
| spelling |
irk-123456789-1590322019-09-21T01:26:25Z 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 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. 2019 Article 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 http://dspace.nbuv.gov.ua/handle/123456789/159032 629.429.3:621.313 en Електротехніка і електромеханіка Інститут технічних проблем магнетизму НАН України |
| institution |
Digital Library of Periodicals of National Academy of Sciences of Ukraine |
| collection |
DSpace DC |
| language |
English |
| topic |
Електричні машини та апарати Електричні машини та апарати |
| spellingShingle |
Електричні машини та апарати Електричні машини та апарати Pliuhin, V. Sukhonos, M. Pan, M. Petrenko, O. Petrenko, M. Implementing of Microsoft Azure machine learning technology for electric machines optimization Електротехніка і електромеханіка |
| 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. |
| format |
Article |
| author |
Pliuhin, V. Sukhonos, M. Pan, M. Petrenko, O. Petrenko, M. |
| author_facet |
Pliuhin, V. Sukhonos, M. Pan, M. Petrenko, O. Petrenko, M. |
| author_sort |
Pliuhin, V. |
| 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 |
| publisher |
Інститут технічних проблем магнетизму НАН України |
| publishDate |
2019 |
| topic_facet |
Електричні машини та апарати |
| url |
http://dspace.nbuv.gov.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. |
| series |
Електротехніка і електромеханіка |
| work_keys_str_mv |
AT pliuhinv implementingofmicrosoftazuremachinelearningtechnologyforelectricmachinesoptimization AT sukhonosm implementingofmicrosoftazuremachinelearningtechnologyforelectricmachinesoptimization AT panm implementingofmicrosoftazuremachinelearningtechnologyforelectricmachinesoptimization AT petrenkoo implementingofmicrosoftazuremachinelearningtechnologyforelectricmachinesoptimization AT petrenkom implementingofmicrosoftazuremachinelearningtechnologyforelectricmachinesoptimization |
| first_indexed |
2025-07-14T11:32:53Z |
| last_indexed |
2025-07-14T11:32:53Z |
| _version_ |
1837621863154450432 |
| fulltext |
ISSN 2074-272X. Електротехніка і Електромеханіка. 2019. №1 23
© V. Pliuhin, M. Sukhonos, M. Pan, O. Petrenko, M. Petrenko
UDC 629.429.3:621.313 doi: 10.20998/2074-272X.2019.1.04
V. Pliuhin, M. Sukhonos, M. Pan, O. Petrenko, M. Petrenko
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 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. References 20, tables 3, figures 7.
Key words: electrical machines, optimization, algorithm, data set, machine learning, Microsoft Azure, cloud computing.
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. Библ. 20, табл. 3, рис. 7.
Ключевые слова: электрические машины, оптимизация, алгоритм, набор данных, машинное обучение, Microsoft
Azure, облачные расчеты.
Introduction. The task of electrical machine (EM)
optimal design or a series of EM can be represented as a
general non-linear mathematical problem. This problem
follows to finding the minimum or maximum of the
optimality criterion in the presence of a certain number of
independent variables and limiter functions, which are
technical or technological requirements-limitations to the
project [1-6].
In computer-aided design (CAD) systems, the
optimization of an electrical machine consists in multiple
calculations of the dependencies between the main
indicators given in the form of an equations system,
empirical coefficients and graphical dependencies, which
can be considered as a design equation [7]. The optimal
design of an EM can be represented as the search for
optimal parameters by solving this system of equations.
The complexity of the calculation algorithm complicates
the optimization task.
Reducing the number of independent variables by
increasing the number of stages for solving a design
problem makes it much easier to find the optimal variant.
However, this loses the accuracy of determining the
optimal value of the objective function.
Considering CAD in the context of electric
machines, it is possible to distinguish the following
system components that are used in modern electrical
engineering [1]:
1) automated design of an electric machine;
2) search for the optimal version of the designed
machine;
3) software implementation of design project and
search for optimum;
4) the choice of the optimal variant from the set of
effective one, which have been tested for restrictions.
Known methods for searching the optimum version
of calculating object, such as the method of coordinate
descent, Nelder-Meade, the method of a deformable
polyhedron, etc., do not allow performing calculations
while changing all configuration variables [8]. As a rule,
many methods allow alternating variation of variables
with subsequent adjustment of the convergence
calculations region [9-12].
Thus, the issue of improving the search for the
optimal variation and reducing the time and technical
resources, spent on these tasks as well, becomes relevant.
In this regard in the paper the development of an
optimization model of electric machines, using cloud-
based machine learning technology provided by Microsoft
Azure services was considered [13, 14].
The aim of the work is the development of a
methodology for optimizing electrical machines using
Microsoft Azure machine learning technology.
Formulation of the optimization problem. At the
optimization stage we assume that the basic version of an
electric machine is already calculated (Table 1).
In this case, any electric machine, regardless of its
type, turns into a set of initial data (or dataset):
geometric dimensions;
winding parameters;
electrical and magnetic values;
loss, efficiency, etc.
24 ISSN 2074-272X. Електротехніка і Електромеханіка. 2019. №1
Table 1
Base machine parameters
Parameter name Parameter value
Rated power, kW 15
Line voltage, V 380
Rated speed, rev/min 1500
Frequency, Hz 50
Stator core length, mm 130
Stator core inner diameter, mm 185
Efficiency 0.884
The specified dataset, being placed in a one-
dimensional vector, can be changed with a given law,
obtaining various combinations of the same electric
machine. Thus, in order to obtain a machine with the
highest efficiency, it was required to find a solution to the
following equation:
u = f(х1, х2,..., хn), (1)
where х1, х2…xn – varied variables; u – target function.
The search for the optimal value was not limited to
finding the extremum of the objective function (1).
During the search, candidates were screened out that do
not pass the specified restrictions. The number of equality
constraints within one project can be arbitrary and is set
by the designer:
.0,...,,
...
,0,...,,
,0,...,,
21
212
211
nn
n
n
xxxg
xxxg
xxxg
(2)
Inequality constraints are also used:
.,...,,
...
,,...,,
,,...,,
21
22122
12111
knkk
n
n
bxxx
bxxx
bxxx
(3)
In general case for target function f(x1, x2, …, xn) the
minimum m is finding in restricted area D (x1, x2, …, xn D)
[2]. The considered task was replaced by unconditional
optimization (minimization) of a one-parameter family of
functions:
,,...,,),(
1
)(, 21 nxxxxxxfxF
(4)
where (х) – penalty function; β – penalty factor.
As a penalty function in (4) (х) was taken, that
become zero when the conditions (2) – (3) are fulfilled:
.0
,)(sign1)()(
1
1 1
22
I
i
J
j
jji xhxhxgx
(5)
In expression (5) the limitations of the equality and
inequality types are:
.,...,,,...;2,1,0)(
;,...,2,1,0)(
21 nj
i
xxxxjxh
Iixg
(6)
The additional (penalty) function φ(x) is chosen in
the way, when β→0, the solution of the auxiliary problem
tends to solve the original one, or that their minimums
coincide: min F(x, β) → m while β → 0.
To solve the optimization problem, a Java program
was written, the functionality of which made it possible to
solve the following problems [15]:
design of the base machine;
setting restrictions;
setting a set of varied variables with setting their
variation relative to the base value and the step of their
change;
selection of optimality criteria.
When changing only two variable values (stator core
length and its internal diameter) in the range ±20 % of the
base value, 710000 non-repeating combinations of
electric machines were found. Only 441 combinations
from this value were passed the restrictions, among which
the best option was found. On the Intel Core i3 2.54 GHz
processor and 8 Gb RAM, the calculation time was 9 min
and 8 s. The results of sampling the selected values are
shown on Fig. 1.
Fig. 1. Sampling combinations diagram of electrical machines in
the Java program: Efficiency along the vertical axis and the
number of the combination along the X-axis
The obtained optimization results were compared
with the experimental data, obtained on two machines
with the parameters of the basic and optimized versions,
manufactured at «SpesialEnergyService» LLD, Kharkiv,
Ukraine. The results of laboratory tests showed a
discrepancy with the theoretical no more than 7-8 %.
Performed tests, as well as software solutions of
classical optimization methods [2, 3], can be taken for
comparison with alternative approaches to optimization.
The disadvantage of the existing method is that the
total development time for a Java project was about
3 days (72 hours). In addition, the operating time of the
calculated algorithm increases significantly with a change
in the range and number of varied values. Fig. 2 shows a
comparative chart of the obtained results.
As can be seen from Fig. 2, even at 4 variable
variables and the range of their variation ±20 % from the
base value, the calculation time was about 8 h.
In real industrial projects of electric machines
optimization, it is necessary to vary about 32 parameters,
with a range from ±10 % to ±100 % of the base value [3].
ISSN 2074-272X. Електротехніка і Електромеханіка. 2019. №1 25
Fig. 2. Comparative chart of time spent on optimization
calculations
It is easy to assume that resources of the local PC are
not enough to solve such problems, and the debugging
time of the project becomes unattainable.
The solution to the problem of operations with large
amounts of data (also known as Big Data) and
computational operations is the parallelization of
calculations and the organization of high-performance
computing (HPC) on PC-cluster. However, parallel
computing will inevitably entail both changes to the code
of an existing program (and an inevitable increase in the
debugging time of the program), and will require the
presence of the HPC cluster itself.
One solution to this problem is to use the computing
power of the Microsoft cloud cluster and machine
learning technology based on the Microsoft Azure
service.
Developing Microsoft Azure model. Azure
Machine Learning enables computers to learn from data
and experiences and to act without being explicitly
programmed. Customers can build Artificial Intelligence
(AI) applications that intelligently sense, process, and act
on information – augmenting human capabilities,
increasing speed and efficiency, and helping
organizations achieve more [16].
Machine Learning finds patterns in large volumes of
data and uses those patterns to perform predictive
analysis. Microsoft offers Azure Machine Learning, while
Amazon offers Amazon Machine Learning and Google
offers the Google Prediction API. Software products such
as MATLAB support traditional, non-cloud-based ML
modeling. There are four steps in the process of finding
the best parameter set:
define the parameter space: for the algorithm, first
decide the exact parameter values you want to consider;
define the cross-validation settings: decide how to
choose cross-validation folds for the dataset;
define the metric: decide what metric to use for
determining the best set of parameters, such as
accuracy, root mean squared error, precision, recall, or
f-score;
train, evaluate and compare: for each unique
combination of the parameter values, cross-validation is
carried out by and based on the error metric you define.
After evaluation and comparison, you can choose the
best-performing model.
To iterate on your model design, you edit the
experiment, save a copy if desired, and run it again. When
you're ready, you can convert your training experiment to
a predictive experiment, and then publish it as a web
service so that your model can be accessed by others [17].
Elastic cloud infrastructure is the optimal choice for
solutions requiring large design capacities in short periods
of time. It allows you not to wait for training models for
weeks and at the same time not to keep «supercomputers»
on balance.
The source data vector (with parameters of the base
machine and its non-repeating combinations) for the
investigated electrical machine was saved into a .csv file
(comma separated data) and imported into a block of the
Microsoft Azure model. In this table (Table 2) for the test
task there were 10 variable values (columns) and 442
combinations (rows).
Table 2
Vector of initial data, imported to Microsoft Azure model
Combo Diameter Length Efficiency cos …
…
0 175 120 0.8824 0.8618 …
1 175 121 0.8831 0.8679 …
2 175 122 0.8838 0.8739 …
3 175 123 0.8844 0.8787 …
4 175 124 0.8848 0.8828 …
5 175 125 0.8852 0.8866 …
6 175 126 0.8855 0.8896 …
… … … … … …
Statistical analysis of the selected optimality
criterion (Efficiency) is performed automatically after
importing the source data table into the Microsoft Azure
workspace (Table 3).
Table 3
Efficiency statistical performance
Parameter name Parameter value
Average value 0.8801
Median 0.8817
Minimum value 0.8553
Maximum value 0.8865
Standard deviation 0.0059
Unique values 87
Lost Values 0
Type of analysis Numeric label
The Microsoft Azure database contains hundreds of
computational blocks from which a research task can be
made and the complexity of which is limited by the
designer’s skill [18-20]. Numerous examples of already
completed works are available in the Azure cloud. This
allow to choose selected one as the basis for the own
development.
In this example, the Microsoft Azure project
contained the following elements:
IM_Data – table of parameters;
Clean Missing Data – deleting of empty rows;
26 ISSN 2074-272X. Електротехніка і Електромеханіка. 2019. №1
Select Columns in Dataset – selection of columns of
variable parameters;
Split Data – initial dataset dividing (70% for model
teaching in left port and 30% for model analyses using
original data in right port);
Algorithm (Boosted Decision Tree, Multiclass
Neural Network);
Train Model – blocks for model teaching;
Score Model – block of selection and analysis of the
optimality criterion;
Evaluate Model – block for calculating of statistical
information.
The block-scheme of the project is shown on Fig. 3
Fig. 3. The project on the optimization of the electric machine
in Microsoft Azure workspace
After performing the calculations, related to the
system training, testing the sampling algorithm and
searching for the optimum, the final results were obtained.
We can get access to these results from «Evaluate Model»
block (Fig. 3) and receive various reports. The sample of
efficiency data is shown on Fig. 4 and Fig. 5.
C
om
bi
na
ti
on
n
um
be
r
Efficiency
Fig. 4 The dependence of the combinations number vs.
efficiency
D
is
tr
ib
ut
io
n
Efficiency
Fig. 5. Efficiency dispersion summary
Fig. 6 shows the report, obtained after analyzing
the constructed model in Microsoft Azure using the
method of multiclass neural networks. The generated
report in tabular format represents the source data sets,
followed by columns with calculated deviations from the
optimum, as well as statistical indicators (on Fig. 6 only
one column (rightmost one) is shown out of 10 available
for analysis).
Fig. 7 shows the user project view in the Microsoft
Azure workspace, where in addition to the neural network
method, the decision tree algorithm, the Poisson
regression analysis were included in the analysis of the
source data sample.
According to the calculation results, the optimal
combination No. 420 was chosen (the choice was made
according to the table, the first row of Fig. 6, where the
optimization results are sorted in order of increasing
error) with the following parameters: stator core length
120 mm, stator core diameter 195 mm, efficiency 0.884.
The computation time was only 1 min 45 s. The
metric estimation module built into Microsoft Azure
made it possible to determine the quality of the performed
calculations. Absolute error 0.000702, standard deviation
0.005926, relative absolute error 0.164582 and relative
square error 1.011483 (the lower the value, the better)
were obtained.
It should be noted that if the functionality of the
embedded Microsoft Azure tools is not enough for some
reason, researchers can write and execute their own
scripts on R or Python [13, 14].
Thus, the use of Microsoft Azure in optimizing
electrical machines has been demonstrated. In the shown
example, only one data vector was used and there were no
modules for intermediate processing and data transfer
between the modules.
Further research will be focus on creating own
Python calculating blocks and R scripts with a view to
transferring to the Microsoft Azure platform not only data
set (now this data set is forming based on the results of
separate calculations in Java program), but also creating a
population of source data based on the vector parameters
of the base machine.
ISSN 2074-272X. Електротехніка і Електромеханіка. 2019. №1 27
Fig. 6. Results of the neural network sampling algorithm in the Microsoft Azure report table
(screenshot of the project table in the browser workplace)
Fig. 7. Full project model in Microsoft Azure (Screenshot of the project model in the browser workplace)
28 ISSN 2074-272X. Електротехніка і Електромеханіка. 2019. №1
Conclusions.
The application of Microsoft Azure machine
learning technology in electrical machines optimizing is
shown for the first time.
As a result of the performed optimization using
Microsoft Azure cloud services, the computation time was
reduced by more than 300 times (from 480 minutes to
2 minutes) when solving the same task compared to
calculations on a stationary PC.
The project development time of an electric
machine, on the example of an induction motor with a
squirrel-cage rotor, was reduced from several working
days to 30 minutes.
The complexity of project developing (induction
motor parameters optimization task) has significantly
decreased compared to direct Java programming, due to
the use of ready-made analysis units provided by
Microsoft Azure.
The Microsoft Azure platform for the
implementation of machine learning technology can be
recommended in solving optimization problems of
various electric machine types.
REFERENCES
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).
Received 17.10.2018
V. Pliuhin1, Doctor of Technical Sciences, Professor,
M. Sukhonos1, Doctor of Technical Sciences, Professor,
M. Pan1, Candidate of Technical Sciences, Professor,
O. Petrenko1, Doctor of Technical Sciences, Associate
Professor,
M. Petrenko2, Candidate of Technical Sciences, Associate
Professor,
1 O.M. Beketov National University of Urban Economy
in Kharkiv,
17, Marshal Bazhanov Str., Kharkiv, 61002, Ukraine,
e-mail: petersanya1972@gmail.com
2 National Technical University «Kharkiv Polytechnic Institute»,
2, Kyrpychova Str., Kharkiv, 61002, Ukraine.
|