=Paper=
{{Paper
|id=Vol-2547/paper16
|storemode=property
|title=Methods of using mobile Internet devices in the formation of the general scientific component of bachelor in electromechanics competency in modeling of technical objects
|pdfUrl=https://ceur-ws.org/Vol-2547/paper16.pdf
|volume=Vol-2547
|authors=Yevhenii O. Modlo,Serhiy O. Semerikov,Stanislav L. Bondarevskyi,Stanislav T. Tolmachev,Oksana M. Markova,Pavlo P. Nechypurenko
|dblpUrl=https://dblp.org/rec/conf/aredu/ModloSBTMN19
}}
==Methods of using mobile Internet devices in the formation of the general scientific component of bachelor in electromechanics competency in modeling of technical objects==
https://ceur-ws.org/Vol-2547/paper16.pdf
217
Methods of using mobile Internet devices in the
formation of the general scientific component of bachelor
in electromechanics competency in modeling of technical
objects
Yevhenii O. Modlo1[0000-0003-2037-1557], Serhiy O. Semerikov2,3,4[0000-0003-0789-0272],
Stanislav L. Bondarevskyi3[0000-0003-3493-0639], Stanislav T. Tolmachev3[0000-0002-5513-9099],
Oksana M. Markova3[0000-0002-5236-6640] and Pavlo P. Nechypurenko2[0000-0001-5397-6523]
1 Kryvyi Rih Metallurgical Institute of the National Metallurgical Academy of Ukraine,
5, Stephana Tilhy Str., Kryvyi Rih, 50006, Ukraine
eugenemodlo@gmail.com
2 Kryvyi Rih State Pedagogical University, 54, Gagarina Ave., Kryvyi Rih, 50086, Ukraine
semerikov@gmail.com, acinonyxleo@gmail.com
3 Kryvyi Rih National University, 11, Vitaliy Matusevych Str., Kryvyi Rih, 50027, Ukraine
parapet1979@gmail.com, stan.tolm@gmail.com,
markova@mathinfo.ccjournals.eu
3 Institute of Information Technologies and Learning Tools of NAES of Ukraine,
9, M. Berlynskoho Str., Kyiv, 04060, Ukraine
Abstract. An analysis of the experience of professional training bachelors of
electromechanics in Ukraine and abroad made it possible to determine that one
of the leading trends in its modernization is the synergistic integration of various
engineering branches (mechanical, electrical, electronic engineering and
automation) in mechatronics for the purpose of design, manufacture, operation
and maintenance electromechanical equipment. Teaching mechatronics provides
for the meaningful integration of various disciplines of professional and practical
training bachelors of electromechanics based on the concept of modeling and
technological integration of various organizational forms and teaching methods
based on the concept of mobility. Within this approach, the leading learning tools
of bachelors of electromechanics are mobile Internet devices (MID) – a
multimedia mobile devices that provide wireless access to information and
communication Internet services for collecting, organizing, storing, processing,
transmitting, presenting all kinds of messages and data. The authors reveals the
main possibilities of using MID in learning to ensure equal access to education,
personalized learning, instant feedback and evaluating learning outcomes, mobile
learning, productive use of time spent in classrooms, creating mobile learning
communities, support situated learning, development of continuous seamless
learning, ensuring the gap between formal and informal learning, minimize
educational disruption in conflict and disaster areas, assist learners with
disabilities, improve the quality of the communication and the management of
institution, and maximize the cost-efficiency.
___________________
Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0).
218
Bachelor of electromechanics competency in modeling of technical objects is
a personal and vocational ability, which includes a system of knowledge, skills,
experience in learning and research activities on modeling mechatronic systems
and a positive value attitude towards it; bachelor of electromechanics should be
ready and able to use methods and software/hardware modeling tools for
processes analyzes, systems synthesis, evaluating their reliability and
effectiveness for solving practical problems in professional field.
The competency structure of the bachelor of electromechanics in the modeling
of technical objects is reflected in three groups of competencies: general
scientific, general professional and specialized professional. The implementation
of the technique of using MID in learning bachelors of electromechanics in
modeling of technical objects is the appropriate methodic of using, the
component of which is partial methods for using MID in the formation of the
general scientific component of the bachelor of electromechanics competency in
modeling of technical objects, are disclosed by example academic disciplines
“Higher mathematics”, “Computers and programming”, “Engineering
mechanics”, “Electrical machines”.
The leading tools of formation of the general scientific component of bachelor
in electromechanics competency in modeling of technical objects are augmented
reality mobile tools (to visualize the objects’ structure and modeling results),
mobile computer mathematical systems (universal tools used at all stages of
modeling learning), cloud based spreadsheets (as modeling tools) and text editors
(to make the program description of model), mobile computer-aided design
systems (to create and view the physical properties of models of technical
objects) and mobile communication tools (to organize a joint activity in
modeling).
Keywords: mobile Internet devices, bachelor of electromechanics competency
in modeling of technical objects, a technique of using mobile Internet devices in
learning bachelors of electromechanics.
1 Introduction
In previous work [13] it has been established that despite the fact that mobile Internet
devices (MID) are actively used by electrical engineers, the methods of using them in
the process of bachelor in electromechanics training [4] is considered only in some
domestic scientific studies. The article [13] highlights the components of the methods
of using MID in the formation of the ICT component of the competence of the bachelor
in electromechanics in modeling of technical objects [7; 8], providing for students to
acquire basic knowledge in the field of Computer Science and modern ICT and skills
to use programming systems, math packages, subroutine libraries, and the like. For
processing tabular data, it was proposed to use various freely distributed tools that do
not significantly differ in functionality, such as Google Sheets, Microsoft Excel, for
processing text data – QuickEdit Text Editor, Google Docs, Microsoft Word. For 3D-
modeling and viewing the design and technological documentation, the proposed
comprehensive use of Autodesk tools in the training process.
219
According to the model of the use of mobile Internet devices in the formation of the
competence of the bachelor in electromechanics in the modeling of technical objects
[6], it is need to develop the methods of using mobile Internet devices in the formation
of the general scientific component of the competence of the bachelor in
electromechanics in the modeling of technical objects.
To achieve this goal, the following tasks must be solved:
1. Identify the leading mobile software tools for the development of competence in
applied mathematics and illustrate their use in the academic disciplines “Higher
Mathematics” and “Computing Engineering and Programming”.
2. Identify the leading mobile software tools for the development of competences in
fundamental sciences and illustrate their use in academic disciplines “Higher
Mathematics”, “Theoretical Mechanics and Electrical Machines”.
2 Results of the research
2.1 Use of mobile Internet devices in the formation of competence in
applied mathematics
The formation of such a general scientific component of the competence of the bachelor
in electromechanics in the modeling of technical objects, as the competence in applied
mathematics, involves understanding students of the basic facts, concepts, principles of
applied mathematics; mastering the methods of system analysis, construction and
research of models of applied problems using the modern ICT tools, establishing their
adequacy to real processes and phenomena; knowledge of numerical methods and
algorithms for their implementation; determination of the correctness of the applied
mathematics methods, the conditionality of the problems and the stability of the
algorithms to the errors of the input data; selection and rational use of ready-made
software (including computer mathematics systems) for computational experiments to
verify hypothetical statements, etc.
Formation of competence in applied mathematics occurs primarily in the study of
such disciplines as “Higher Mathematics” and “Computer Science and Programming”.
Thus, among the content modules of the “Higher Mathematics” one of the most
important for the formation of competence in applied mathematics is module 1
“Elements of linear algebra”, which, in particular, considers the concepts of matrix,
matrix types, actions on matrices and their properties, the notion and solution of a
system of linear algebraic equations by the Gaussian and the matrix methods.
In order to establish the interdisciplinary connections of the “Higher Mathematics”
and “Computer Science and Programming”, it is expedient to consider similar models
that are investigated by various means. Thus, before implementing the polynomial
model of the approximation of the function of one variable by means of Visual Basic
for Application in the third content module of the “Computer Engineering and
Programming” it is expedient to consider it in practical lesson on the academic
discipline “Higher Mathematics” in the matrix form, which students mastered in
module 1 “Elements of linear algebra”.
220
Output data for constructing a model is a value from the table of the form:
xexp yexp
x1 y1
x2 y2
... ...
xi yi
... ...
xn yn
The polynomial expression should be written in the form
y = apxp + ap–1xp–1 + ... + a2x2 + a1x + a0
Here n – is the number of pairs of values of the form (x; y) in the table, and p – is the
order of the polynomial (p << n).
After substituting each value from a table into a polynomial, we obtain a system of
n linear algebraic equations with p+1 unknown:
= + +⋯+ + +
⎧
⎪
⎪ = + +⋯+ + +
⋯
⎨ = + +⋯+ + +
⎪
⎪ ⋯
⎩ = + +⋯+ + +
The main matrices that characterize the system are:
A – is a matrix column of unknown coefficients of the polynomial:
⎡ ⎤
⎢ ⋯ ⎥
=⎢ ⎥
⎢ ⎥
⎢ ⎥
⎣ ⎦
X – the main matrix of the system:
⎡ ⋯ 1⎤
⎢ ⋯ 1⎥
⎢ ⋯⎥
= ⎢⋯ ⋯ ⋯ ⋯ ⋯
⎥
⎢ ⋯ 1⎥
⎢⋯ ⋯ ⋯ ⋯ ⋯ ⋯⎥
⎣ ⋯ 1⎦
221
Y – is a matrix column of values:
⎡ ⎤
⎢⋯⎥
= ⎢ ⎥
⎢ ⎥
⎢⋯⎥
⎣ ⎦
A shortened system can be written as a matrix equation:
Y = XA
The direct solution of such system by methods considered in the first module is
impossible due to the fact that the number of equations is greater than the number of
unknowns. The same can be said about solving the matrix equation: finding a matrix
inverse to the matrix X, is impossible because matrix X is not square. To get out of this
dead end, we suggest students apply the property of a transposed matrix, namely: the
product of a transposed matrix on the output is a square matrix, so we apply this
property to both parts of the matrix equation:
XTY = XTXA
The resulting equation contains a new matrix – XT, which is a transposed matrix X.
Using the associativity properties of multiplication of matrices, we obtain the following
equivalent equation:
(XTX)A = XTY
It corresponds to the normal system of linear algebraic equations, for solving which one
can use any of the mastered methods – Cramer’s rule, Gaussian elimination or matrix
inversion. To use the latter, we make a left multiplication of both parts of the matrix
equation on the matrix inversed to the product XTX:
(XTX)–1(XTX)A = (XTX)–1XTY
Get the next equivalent equation:
IA = (XTX)–1XTY,
where I – is an identity matrix of dimension (p+1, p+1):
A = (XTX)–1XTY
To find the solution, we first suggest using the traditional methods of “manual” solution
in order to make sure that the time spent on such work is incommensurate with the time
spent on the mathematical description of the model. It pushes for the use of ICT. For
matrix models we are propose an electronic spreadsheets. Google Sheets provides to
students and teachers the opportunity to put together experimental data and
corresponding formulas.
222
To do this, make a spreadsheet accessible to all students in the academic group and
invite each of them to fill in a line corresponding to the student number in the group’s
journal with a pair “weight – height”.
To select a polynomial order, we visualize the entered values and suggest
justification of the choice. As a result of the discussion, we agree with the assumption
that the line to be held at the smallest distance from all points can be a parabola, so our
model will have the quadratic form y = a2x2 + a1x + a0. Accordingly, it is necessary to
construct the matrix X from the elements of the column xexp in the second, first and zero
degrees, and the matrix Y – from the elements of the column yexp. The XT matrix is
constructed using the transpose function, giving it the parameter range of the values of
the matrix X (E3:G32), and the products of XTX and XTY – by calling
mmult(M3:AP5;E3:G32) and mmult(M3:AP5;I3:I32) respectively. We find the inverse
to the XTX matrix with a minverse(M8:O10) call and make the left multiplication of it
to the XTY matrix by calling mmult(M13:O15;Q8:Q10). As a result we obtain A, the
matrix of polynomial coefficients (fig. 1).
Fig. 1. Mobile Internet device while working with the model on higher mathematics classes
In fig. 1 shows an updated graph of the ratio of weight and height of students, where
the approximated growth values were added to the experimental points, calculated
using the formula ycalc = -0,0148x2 + 2,3669x + 87,4835. The simulation results make
it possible to draw an important conclusion that this model can be simplified to linear
without losing adequacy. Since the factor of the second power x does not make a
significant contribution to the calculated value of growth; therefore, the ratio of weight
and height of students can be described not parabolic, but linear dependence. Such task
can be offered for self-study.
The considered algorithm of approximation can be illustrated also on the material of
other modules. As shown in [12], the course of higher mathematics in technical
universities traditionally ends with the Fourier transform and Fourier series as its partial
223
case. Considering the high practical significance of this topic for the further
professional activity of future engineers-electromechanics, when studying the module
16 “Trigonometric series and their applications”, it is expedient for students to propose
solution of the approximation problem by a fragment of a trigonometric series of
sequence of points corresponding to the financial series. As a data source, we use the
Google Finance online service, which provides the ability to export data to Google
Sheets using the function =googlefinance("currency:usduah"; "close"; date(2013;1;9);
date(2018;9;9)), the first parameter of which is the currency pair, “US dollar –
Ukrainian hryvnia”, the second is the closing price of exchange rates of given currency
pair, the third is start date and fourth is end date of the time interval l.
The mathematical model of the renewal dependence is a cosine decomposition of an
unknown function f(x) on an interval [0; l):
( )= ,
where n – is the harmonic number, N+1 – is the number of harmonics, an – is the Fourier
coefficients.
We put the measurement vector (value of the price) in the column matrix Y of the
l×1 dimension. Due to find the column matrix A of the Fourier coefficients of by
dimension (N+1)×1 we perform calculation A=(XTX)–1XTY, where X – is the matrix
of the plan of the dimension l×(N+1), containing the cosine Fourier coefficients:
Xxn = . As the value of the independent variable, apply the measurement number
x = 0...l–1, and the harmonic number n =0...N.
The call of the googlefinance function in cell A1 will provide two columns of values,
the first of which will contain a date, and the second is the price. To determine the value
of l we use the function of counting the number of column elements: count(A:A). We
assign the number of harmonics manually: N = 30. To the first cell of the plan matrix
X (N2) we enter the cos(pi()*N$1*$G2/$E$1) formula, which will be copied to all other
cells of the X. The matrix column А are described by the formula –
mmult(mmult(minverse(mmult(transpose(N2:AR1693);N2:AR1693));transpose(N2:
AR1693));H2:H1693). To calculate the predicted values уcalc we use the scalar product
of the line matrix to the column matrix A: mmult($N2:$AR2;$S$1696:$S$1726) to the
J2 cell and distribute it to the entire range.
To analyze the results we draw plots of currency values y obtained from Google
Finance and approximated data ycalc (fig. 2). The function plot indicates that 30
harmonics in the general case is quite a satisfactory amount for the function estimation,
but such amount is not enough to simulation of fast processes with a large amplitude of
fluctuations. An example of such phenomena in the electromechanics are the modes of
starting the engine, sharp loading of the load and short circuit.
Discussion of this model in several classes provides an opportunity to evaluate its
adequacy by forecasting future values of the currency pairs. So, it is advisable to
compare the currency values for the dates that were not used when constructing the plan
matrix, with the predicted values. To do this, we propose calculating at least three future
values of the currency and compare them with the real ones. In fig. 3, the last three
224
values (1692-1694) were not used in constructing the plan matrix, but they reflect the
tendency of the currency change.
Fig. 2. Dynamics of currency pair “US dollar – Ukrainian hryvnia” and its approximation by
Fourier series
Fig. 3. Forecast of dynamics of currency pair “US dollar – Ukrainian hryvnia” for the period
from September 10 till September 12, 2018
In studying the module 7 “Differential calculation of the function of several variables”
it is expedient to consider the problem of finding the extremum of a convex function
by a gradient descent method. To do this, you can return to the ratio of the two measured
values x and y, considered in the first module of the model, but write down its solution
using partial derivatives.
The function of communication of these values (hypothesis) is written in the form
h(x) = θ0 + θ1x, or
ℎ( ) = ⃗ ⃗ = [ ] 1,
225
where ⃗ = – column matrix (vector-column) of unknown coefficients ⃗ , and
1
⃗= – non-transposed element of the plan matrix.
Let us construct a function of value, which depends on the parameters of the
hypothesis:
1
( , )= (ℎ( ) − ) .
2
The function plot has a single extremum (fig. 4). The gradient descent method is based
on the fact that starting from a certain initial value of the vector ⃗ , we will take steps
on the surface of the paraboloid in the direction of minimizing the function value, that
is, the direction opposite to the gradient vector of the function: ∇ ( , ) = , .
Fig. 4. Function plot (left) and its contour plot (right); the cross indicates the global minimum
The partial derivatives of the function with the respect to the θ0 and θ1 are equal:
1
= (ℎ( ) − ),
1
= (ℎ( ) − ) ,
The algorithm of the gradient descent method will look:
⃗ = ⃗ − ∇ ( , ),
where – descent speed.
226
The software implementation of this numerical method can be performed in mobile
versions of Scilab [5], MATLAB or Octave. Thus, the function of the value of J can be
realized through the scalar product of the defined vectors:
function J = computeCostMulti(X, y, theta)
m = length(y);
J=1/(2*m)*(X*theta-y)'*(X*theta-y);
end
The number of iterations num_iters an additional parameter used when implementing
the method:
function [theta] = gradientDescentMulti(X, y, theta, alpha,
num_iters)
m = length(y);
for iter = 1:num_iters
temp=theta;
n=size(X,2);
s=zeros(n,1);
for j=1:n
for i =1:m
s(j,1)=s(j,1)+(X(i,:)*theta-y(i))*X(i,j);
end;
end;
temp=theta-alpha*(1/m)*s;
theta=temp;
end
end
As a result, we obtain the vector of the parameters ⃗ , and can visualize the results of
the simulation (fig. 5).
In the content module 9 “Definite and improper integrals” the competence in applied
mathematics can be developed on example of the problem of building plot of electrical
load. The corresponding code with teacher’s explanations can be offer in SageCell:
#Output data
data=[[1,6],[2,7],[3,7.5],[4,7.5],[5,7.5],[6,8],[7,8],[8,10],[9,
13],[10,15],[11,13.5],[12,13.5],[13,11],[14,12],[15,14],[16,11],
[17,10], [18,12],[19,14],[20,14],[21,15],[22,13],[23,12],[24,7]]
p=point((0,0))
S=vector([0,0,0]) # sums by the method of left and right
rectangles, and the trapezoid method
for i in range(len(data)-1):
p += line([data[i], data[i+1]], color='red')
227
p += polygon([data[i], [data[i+1][0], data[i][1]],
[data[i+1][0], 0], [data[i][0], 0], data[i]], color='lightblue')
dt = data[i+1][0]-data[i][0] # integration step
S += vector([data[i][1]*dt, data[i+1][1]*dt,
(1/2)*(data[i][1]+data[i+1][1])*dt])
show(p, figsize=[4,5])
html("Daily electricity consumption calculated:
- left
rectangular method - %s,
"
"- right rectangular method - %s,
"
"- trapezoidal rule - %s
"%(S[0],S[1],S[2]))
Fig. 5. Visualization of cloud points and regression lines in MATLAB Mobile
To enable students to conduct their own experiments, the teacher can generate a
SageCell QR code (fig. 6a), which is processed by a mobile Internet device (fig. 6b).
228
a)
b)
Fig. 6. The use of SageCell on the teacher (a) and student (b) devices in the process of learning
numerical integration of table-set values
229
In content module 14 “Linear differential equations of higher orders with constant
coefficients” we can illustrate the use of another mobile mathematical system – SMath
Studio [9], which allows both analytic and numerical solution of differential equations
(fig. 7).
Fig. 7. Using SMath Studio to analyze dynamic systems
2.2 The use of mobile Internet devices in the formation of competence
in the fundamental sciences
The formation of a general scientific component of the competence of the bachelor in
electromechanics in the modeling of technical objects, such as competencies in
fundamental sciences, provides for the acquisition of basic knowledge of the
fundamental branches of the natural sciences and mathematics to the extent necessary
to obtain the mathematical methods of the electromechanical branch of knowledge;
ability to use mathematical methods and methods of natural sciences in research and
applied professional activities.
In addition to the content of the “Higher Mathematics”, for the formation of
competence in the fundamental sciences, it is necessary to implement the substantive
component of the disciplines “Theoretical Mechanics” and “Electrical Machines”.
The purpose of studying the “Theoretical Mechanics” is the getting of knowledge
and the acquisition of skills necessary to study the general laws of mechanical
movement, the interaction of material bodies based on the laws of classical mechanics,
the acquisition of skills to perform calculations for strength, rigidity of structural
230
elements, machine parts, research and design of modern heavy machines, development
of textual and graphical design documentation.
As a result of studying the discipline, the student, should be able to: make up
mathematical models of material objects, solve problems related to the study of the
movement and equilibrium of certain material bodies under the action of forces applied
to them using ICT; analyze kinematic schemes of machines.
For the implementation of the first task, as in teaching higher mathematics, we
suggest using SMath Studio as a free and mobile equivalent of the Mathcad system. In
fig. 8 shows the sequence of screen copies of the implementation of the calculation
model for a beam with two supports.
a) problem statement
b) example of the calculation of displayed objects
231
c) simulation results
Fig. 8. The model for calculating beams on two supports in SMath Studio
To solve the problem of analysis of kinematic schemes of machines, we use the
augmented reality (AR) solutions for intuitive analysis and material design [10].
Dieter Weidlich, Sandra Scherer and Markus Wabner in [11] describe the experience
of improving the process of developing machine parts using the virtual and augmented
reality systems of Chemnitz Technical University, which developed new methods
visualization to study the result of modeling by the finite elements method. The main
purpose of software development was the visualization of the direction and stress
gradient by 3D glyphs. The finite element method is a numerical method of engineering
analysis, used for many types of tasks, such as determining loads and shifts in
mechanical objects, or heat transfer and flow dynamics.
Calculating mechanical stresses is fundamental to the analysis of strength behavior
in mechanical engineering. Glyphs are a way of graphically coding numeric
information. A glyph is a graphic unit that can communicate various data attributes by
its appearance (shape, color, orientation, position, and so on). A special characteristic
232
of glyphs, as compared to simpler concepts, is the number of data attributes that can be
communicated. Glyphs are primarily used for representing multidimensional data.
Glyph-based methods represent a multitude of tensor values by reflecting tensor
eigenvectors and values in terms of shape, size, orientation, and surface characteristics
of geometrical primitives such as cubes and ellipses (fig. 9).
Fig. 9. Visualization of the results of the finite element analysis: the outward glyph vertices
reflect tensile loads, and the inward glyphs represent the compression load
Various geometric primitives such as cuboids, tetraeders, spheres, and lines can be used
to represent multidimensional data. However, this requires a preliminary study into
which shape of glyph is suitable for the data to be represented. Tests have shown that
the tetraeder is well-suited for visualizing stress direction and gradient because the tip
of the tetraeder indicates an exact direction. In addition to stress direction, the 3D
glyphs can also reveal whether the stress is tensile or compressive.
The software developed by the authors of [11] allows switching between the results
of structural and thermal analysis and comparing them with a real physical object. In
fig. 10 shows the imposition of a finite element model on a real system: the more “hot”
the color is, the greater the load. A black and white marker in the user’s hand is
necessary for positioning the analysis results.
Michele Fiorentino, Giuseppe Monno and Antonio E. Uva in article [3] identify 6
main ways of using augmented reality in engineering, for each of which are defined
aspects like hardware configuration, add-on method, TUI/GUI interactivity level (TUI
– Tangible User Interface, material user interface; GUI – Graphical User Interface,
233
graphical user interface), viewpoint, physical collaboration support and remote
collaboration.
Fig. 10. Display of simulation results on a real object
1. Augmented user
The user wears see-through AR glasses connected to a wearable PC. See-through
displays allow the user to be aware of the real industrial environment. This
configuration allows maximum mobility for the user letting him work in a large
workspace with free hands. The interaction is achieved mostly by TUI with none or
limited GUI. Suggested applications for this setup are: inspection, training, etc.
Disadvantages may include the display resolution, the limited field of view and the
optical tracking robustness in hostile manufacturing environments. In another setup the
user holds a handheld (flashlight-like) camera and a wearable PC connected to the
network. The user is free to move in the industrial environment and to teleconference
with other users remotely logged. The difference compared to previous setup is the
viewpoint mobility. The user can move the camera in the industrial environment,
234
reaching potentially every location under wireless coverage. Local tracking is provided
by markers and broadcasted to the system. This scenario is particularly important in
maintenance, where remote experts can guide and assist the user. The user loads his
customized visualization of the model and broadcasts it remotely. The main advantage
of this configuration is the maximum mobility for point of view. This may also lead to
an unsteady point of view due to the fact that the user must hold the camera. TUI and
GUI interaction is also rather limited.
2. Mobile window
The user holds a tablet PC with a camera on the back side. Tablet displays allow the
user to be fully aware of the real industrial environment. This configuration allows a
good mobility for the user letting him work in a large workspace but it requires that at
least one hand holds the tablet. The interaction is achieved mostly by GUI with the
tablet pen. Suggested applications for this setup are: design review, inspection, etc.
Disadvantages may include the weight of the tablet and the single-handed interaction
limitation.
3. Augmented desktop
The user works on a desktop workstation with a camera pointing on a free area on the
desk, which will be the augmented workspace. The AR workspace is limited to the
user’s desktop and the model interaction is achieved by moving the TUI (augmented
technical drawings) and by the traditional desktop GUI with a mouse and a keyboard.
In normal use, the TUI is just a support to the ordinary GUI. For this reason, this
scenario is suggested for all tasks which involve an heavy use of keyboard entry of
numerical or text data: e.g. detailed design, engineering, numerical analysis, etc. The
main advantage of this setup is the similarity with the traditional working environment,
allowing an easy access even for a non technical user. Users, in fact, find much easier
and intuitive the navigation of 3D models using a tangible metaphor. A limiting factor
is that it must be implemented in an office-like environment.
4. Augmented workshop
This scenario is similar to the augmented desktop as regards the hardware setup, but it
is designed for a production stage environment instead of a clean office desk. The user
is on a workbench on the production line where no keyboard or mouse is present. The
user can interact by touch screen on industrial monitor and by tangible augmented
drawings. An industrial buttonbox can also be used. The main advantages are: both
hands free for the user, possibility to display high resolution rendering of the 3D model,
comfortable working environment, similar to a non augmented one. Ideal applications
may be quality check or guided assembly.
5. Augmented collaborative table
This scenario supports collaborative workspace at best. It consists of a meeting table
with the function of shared augmented area and of a large screen. The screen can be
vertical or horizontal and eventually have stereographic or holographic display. All
235
users can access to the augmented shared area with their tokens and they can annotate
the model using their own PC laptop for precise GUI input. Remotely located users can
join the group and participate with virtual meeting tools. The system will take care of
the synchronization of the digital master data including annotations, chat and history.
The main applications of this scenario are marketing and design review: the shared
workspace can contain virtual CAD models, real pre-production mock ups, on-line
technical content and simulation results for collaborative discussion. The main
advantages of this scenario are the high collaboration support, the coexistence of real
and virtual products and the social contact of real meetings.
6. Augmented presentation
This scenario considers a speaker who wants to present a solution to a large audience.
A large screen is the main visualization device. The data management is achieved
mainly by TUI in form of digital drawing or mock-up placed on the speaker’s stand.
The audience can access to the same digital data with personal visualization devices
and can add annotations, which are updated in real time for all the members of the
discussion.
The characteristics of each method proposed by the authors of [3] are summarized
in Table 1.
Table 1. The main ways of using augmented reality in engineering
Collaboration Remote
Scenario Viewpoint TUI level GUI level
level coworking
Augmented user mobile high high low medium
Mobile window mobile low high medium medium
Augmented desktop fixed medium high low high
Augmented workshop fixed medium low low low
Augmented collaboration table fixed high medium high medium
Augmented presentation fixed high low medium medium
The purpose of learning the “Electrical Machines” is the formation of students’
theoretical knowledge of the design, principle of operation, the field of use electrical
machines and transformers, as well as the acquisition of practical skills related to
connecting, operating and determining the parameters of electric machines and
micromachines. As a result, a student should be able to draw up electrical circuits and
calculate the parameters of the circuit elements, to turn on electrical machines and
micromachines, provide experiments to determine the parameters of electrical
machines, and perform calculations of the structural elements during the design and
repair of electrical machines.
In the process of acquiring theoretical knowledge of the design of electric machines,
it is advisable to use mobile augmented reality tools developed by SIKE Software.
Training simulator system with augmented reality technology makes it possible to form
a complex of knowledge about the structure of electric motors of various types and
acquire skills in identifying the component parts of electric motors and the safe, correct
and fast order of assembly and disassembly of electric motors (fig. 11). The system can
be applied in practical training, laboratory and independent work, examinations, etc.,
236
and in the process of industrial training – for theoretical interactive training of workers
involved in the installation and dismantling of industrial electrical equipment. The
program provides access to 3D models with a high degree of accuracy, repeating the
structure of real equipment.
Fig. 11. Training system-simulator SIKE Software
Each detail of the design has name, description, and the order of technological
operations corresponds to the actual process and is developed in conjunction with the
experts of leading industrial enterprises.
This system uses the QR code as a marker on a special card. Another approach is to
use a universal marker (fig. 12) or a scene marker containing a real object (fig. 13).
Fig. 12. The model of the Rossi motor reductor in the Augment system is tied to a universal
marker
237
Fig. 13. The asynchronous motor model, which tied to the scene containing the real object in
the HP Reveal system
When performing the calculation of a three-phase asynchronous motor after repair and
restoration works (module 7 “Three-phase asynchronous machines”) it is advisable to
use SMath Studio. Fig. 14 shows the beginning of the calculation of the natural
electromechanical and mechanical characteristics of the engine, and fig. 15 shows the
calculated mechanical characteristics diagram.
Fig. 14. Setting model parameters and starting the calculation
Models for all content modules of the “Electric Machines” course include Scilab on
cloud (https://cloud.scilab.in/). To select a model, you must select the main category
Electrical Engineering / Electronics & Telecommunication Engineering /
Instrumentation & Control Engineering, subcategory Electrical Devices / Machines and
238
the corresponding course manual (for example book [2] and its corresponding manual
[1]). An example of calculating the synchronous speed of a polyphase asynchronous
machine in Scilab on cloud:
// Example 1.60
clc;clear;close;
// Given data
PA=4;//no. of poles
PB=4;//no. of poles
f=50;//in Hz
V=440;//in volt
//calculations
//Independently with A
Ns=120*f/PA;//in rpm
disp(Ns,"Independently with A, Synchronous speed Ns in rpm is :
");
//Independently with B
Ns=120*f/PB;//in rpm
disp(Ns,"Independently with B, Synchronous speed Ns in rpm is :
");
//Running as cumulative cascaded
Ns=120*f/(PA+PB);//in rpm
disp(Ns,"Running as cumulative cascaded, Synchronous speed Ns in
rpm is : ");
Fig. 15. Diagram of the mechanical characteristics of the engine
239
The result of calculation according to example 1.60:
Independently with A, Synchronous speed Ns in rpm is : 1500.
Independently with B, Synchronous speed Ns in rpm is : 1500.
Running as cumulative cascaded, Synchronous speed Ns in rpm is :
750.
3 Conclusions
Thus, in the process of forming the general scientific component of the competence of
the bachelor in electromechanics in the simulation of technical objects, it is expedient
to use the following software of mobile Internet devices:
─ mobile augmented reality tools used to visualize the structure of objects and
simulation results;
─ mobile computer mathematical systems with object (SMath Studio) and character
input type (Scilab, Octave, MATLAB, SageCell), which is used at all stages of
modeling;
─ cloud-oriented tabular processors as modeling tools;
─ mobile communication tools for organizing joint modeling activities.
References
1. Asif, M., Rijwan, M.: Scilab Textbook Companion for Special Electrical Machines by S. P.
Burman. https://scilab.in/textbook_companion/generate_book/1892 (2016). Accessed 21
Mar 2019
2. Burman, S.P.: Special Electrical Machines. S. K. Kataria & Sons, New Delhi (2012)
3. Fiorentino, M., Monno, G., Uva, A.E.: Tangible Interfaces for Augmented Engineering
Data Management. In: Maad, S. (ed.) Augmented Reality, pp. 113–128. IntechOpen.
https://cdn.intechopen.com/pdfs/6762.pdf (2010). Accessed 21 Mar 2019
4. Modlo, Ye.O., Semerikov, S.O., Shmeltzer, E.O.: Modernization of Professional Training
of Electromechanics Bachelors: ICT-based Competence Approach. In: Kiv, A.E., Soloviev,
V.N. (eds.) Proceedings of the 1st International Workshop on Augmented Reality in
Education (AREdu 2018), Kryvyi Rih, Ukraine, October 2, 2018. CEUR Workshop
Proceedings 2257, 148–172. http://ceur-ws.org/Vol-2257/paper15.pdf (2018). Accessed 21
Mar 2019
5. Modlo, Ye.O., Semerikov, S.O.: Xcos on Web as a promising learning tool for Bachelor’s
of Electromechanics modeling of technical objects. In: Semerikov, S.O., Shyshkina, M.P.
(eds.) Proceedings of the 5th Workshop on Cloud Technologies in Education (CTE 2017),
Kryvyi Rih, Ukraine, April 28, 2017. CEUR Workshop Proceedings 2168, 34–41.
http://ceur-ws.org/Vol-2168/paper6.pdf (2018). Accessed 21 Mar 2019
6. Modlo, Ye.O.: Model vykorystannia mobilnykh Internet-prystroiv u navchanni bakalavriv
elektromekhaniky modeliuvannia tekhnichnykh obiektiv (The model of using MID at
learning bachelors of electromechanics in modeling of technical objects). In:
Chashechnykova, O.S. (ed.) Proceedings of the 3rd International Scientific and Methodical
Conference on The development of intellectual skills and creative abilities of students and
240
students in the process of learning the disciplines of the natural-mathematical cycle (ITM *
plus – 2018). Sumy, Ukraine, November 8-9, 2018, vol. 2, pp. 47–48. FOP Tsoma S. P.,
Sumy (2018)
7. Modlo, Ye.O.: Proektuvannia systemy kompetentsii bakalavra elektromekhaniky v
modeliuvanni (Designing the system of competencies of the bachelor of electromechanics
in simulation). In: Information technology in education and science. 7, 111–116 (2015)
8. Modlo, Ye.O.: Zmist kompetentsii bakalavra elektromekhaniky v modeliuvanni
tekhnichnykh obiektiv (Contents of the competences of the bachelor of electromechanics
in the technical objects simulation). In: Cherkasy university bulletin, Pedagogical sciences.
17, 64–70 (2016)
9. Ivashov, A.: SMath Studio – SMath. https://en.smath.info/view/SMathStudio/summary
(2018). Accessed 21 Mar 2019
10. Syrovatskyi, O.V., Semerikov, S.O., Modlo, Ye.O., Yechkalo, Yu.V., Zelinska, S.O.:
Augmented reality software design for educational purposes. In: Kiv, A.E., Semerikov,
S.O., Soloviev, V.N., Striuk, A.M. (eds.) Proceedings of the 1st Student Workshop on
Computer Science & Software Engineering (CS&SE@SW 2018), Kryvyi Rih, Ukraine,
November 30, 2018. CEUR Workshop Proceedings 2292, 193–225. http://ceur-ws.org/Vol-
2292/paper20.pdf (2018). Accessed 21 Mar 2019
11. Weidlich, D., Scherer, S., Wabner, M.: Analyses Using VR/AR Visualization. IEEE
Computer Graphics and Applications. 28(5), 84–86 (2008). doi:10.1109/mcg.2008.89
12. Semerikov, S.O., Teplytskyi, I.O., Polishchuk, O.P.: Riady Furie v zadachakh vidnovlennia
zalezhnostei u kursi vyshchoi matematyky v tekhnichnomu VNZ (Fourier series in the
problems of renewal of dependencies in the course of higher mathematics in a technical
university). In: Proceeding of 5th Ukrainian scientific and methodical workshop on
Computer simulation in education, Kryvyi Rih, 6 April 2012, pp. 34–35. Vydavnychyi
viddil NMetAU, Kryvyi Rih (2012)
13. Modlo, Ye.O., Semerikov, S.O., Nechypurenko, P.P., Bondarevskyi, S.L., Bondarevska,
O.M., Tolmachev, S.T.: The use of mobile Internet devices in the formation of ICT
component of bachelors in electromechanics competency in modeling of technical objects.
In: Kiv, A.E., Soloviev, V.N. (eds.) Proceedings of the 6th Workshop on Cloud
Technologies in Education (CTE 2018), Kryvyi Rih, Ukraine, December 21, 2018. CEUR
Workshop Proceedings 2433, 413–428. http://ceur-ws.org/Vol-2433/paper28.pdf (2019).
Accessed 10 Sep 2019