=Paper=
{{Paper
|id=Vol-2631/paper15
|storemode=property
|title=The Diagnostics Methods for Modern Communication Tools in the Armed Forces of Ukraine Based on Neural Network Approach
|pdfUrl=https://ceur-ws.org/Vol-2631/paper15.pdf
|volume=Vol-2631
|authors=Oleg Klymovych,Volodymyr Hrabchak,Oleksandr Lavrut,Tetiana Lavrut,Vasyl Lytvyn,Victoria Vysotska
|dblpUrl=https://dblp.org/rec/conf/momlet/KlymovychHLLLV20
}}
==The Diagnostics Methods for Modern Communication Tools in the Armed Forces of Ukraine Based on Neural Network Approach==
https://ceur-ws.org/Vol-2631/paper15.pdf
The Diagnostics Methods for Modern Communication
Tools in the Armed Forces of Ukraine Based on Neural
Network Approach
Oleg Klymovych1[0000-0003-3863-4984]1, Volodymyr Hrabchak2[0000-0003-3569-0681], Oleksandr
Lavrut3[0000-0002-4909-6723], Tetiana Lavrut4[0000-0002-1552-9930], Vasyl Lytvyn5[0000-0002-9676-
0180]
, and Victoria Vysotska6[0000-0001-6417-3689]
1-4 Hetman Petro Sahaidachnyi National Army Academy, Lviv, Ukraine
5-6 Lviv Polytechnic National University, Lviv, Ukraine
Vasyl.V.Lytvyn@lpnu.ua
Abstract. Timely diagnostics of the technical state and technical support of com-
plex multifaceted systems that provide comprehensive automation of control pro-
cesses requires the development of new, high-precision and reliable troubleshoot-
ing methods. Using one of these methods may include a neural network appa-
ratus.
Keywords: Communication Tools, Diagnostics Method, Armed Forces, Neural
Network.
1 Introduction
The level of preparedness of the Armed Forces of Ukraine (AFU) to perform assign-
ment directly depends on the availability of state-of-the-art weapons and military equip-
ment, but neither weapons nor any equipment will be able to ensure effective perfor-
mance of combat missions without quality command and weapons. Modern control and
communication systems must not only have high combat readiness, bandwidth, stabil-
ity, mobility, accessibility, controllability, but also ensure trouble-free operation of the
system and guarantee the quality of information exchange.
In recent years, the AFU have been expanding and improving the system of commu-
nication and the automatic control of forces. With the introduction of the widespread
use of modern digital radio stations of the Harris, Aselsan and Elbit companies, the
performance and functionality has increased, while the element base of electronic
equipment has become more complicated [1-2]. The complexity of the equipment re-
duces the reliability of modern radio electronic equipment, which increases the likeli-
hood of failure and complicates its search. The main contradiction of modern technol-
ogy is that the more complex the instrument, the less reliable it is, the more accurate
the measuring (diagnostic) tool, the more expensive it is. Thus, it is necessary to find
the optimal ratio of parameters for measurement during diagnostics and accuracy class
of measuring equipment.
Copyright © 2020 for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
The requirement of reliable operation of electronic equipment in the system of com-
plex automation of control processes with the use of complex multifaceted systems,
communication and automation tools (CAT) is especially acute. Therefore, the devel-
opment of new, high precision and reliable faultfinding methods is becoming increas-
ingly urgent. Considering the complexity of modern CAT, there is a need to automate
the process of diagnostics for troubleshooting of equipment. One such method may be
the development and use of a neural network apparatus [3-5].
2 Problem Formulation
In connection with the use in the Armed Forces of modern CATs, the improvement of
their diagnostics should carried out based on the use of scientifically grounded criteria,
modern diagnostic tools and the necessary specialized software. During the Joint Forces
Operation (JFO), the search for expedient ways of creating and improving a scientifi-
cally grounded, economically feasible system of diagnostics of the CAT is continued,
aimed at ensuring that the accumulated knowledge, scientific achievements work pri-
marily to ensure national security and defense of Ukraine.
The purpose of the article is to increase the efficiency of the diagnosis of CAT by
using a basic method of the neural network tool to assess the reliability and ensure the
guaranteed quality of operation of the means of this class.
3 Related Work
A variety of research methods can used in the diagnostic system, which include theories
of graphs, decision support systems, fuzzy sets, neural networks, multi-criteria optimi-
zation methods, expert methods [3-11].
According to the graph theory, it is possible to solve many problems in the field of
economics, technology and many other areas of human activity. An abstract oriented
graph is the basis for planning the actions of a human operator, changing the order of
the sequence of his actions, performing various operations. The setting of network plan-
ning tasks begins with the identification of the nodal moments (events) of the operation,
which determine the beginning and end of its main stages, after which the measures
(work) are defined that lead to the implementation of each event and are accompanied
by the cost of time and resources. Then the events and the corresponding stages of work
are presented in the form of some logical sequence, namely a project graph that reflects
all the ways to achieve the goal of the operation. In the general case, the network graph
(Fig. 1) is a graph which nodes а x is events and oriented arcs Axy is the stages of work,
x y . Thus in the graph any arc reflects only one stage of work, and in a case of a
situation when two stages of work have the same output and input nodes (events) an
additional fake stage of work is added, not related to the time and other resources.
а2
А12 А24
А01 А13 А34 А46 А67 А78
а0 а1 а3 а4 а6 а7 а8
А35 А57
а5
Fig. 1. Project graph
One possible sequence of events and workflows from the original event to the final
event is the path of the project graph, and the path that has the maximum execution time
of each of the work stages and the total execution time of the entire operation is a critical
path. Decision Support Systems (DSS) are special interactive information systems that
use hardware, software, data, model databases and manager work to support all stages
of decision making in the analytical modeling process. In other words, DSS is a set of
software tools that includes a set of different decision support algorithms, a model base,
a database, ancillaries, and a management program (Fig. 2). The leading program pro-
vides the decision-making process based on the specificity of the problem [12-18]. DSS
is used to support various decision-making activities specifically for:
Facilitating interaction between data, data analysis and processing procedures and
decision-making models, on the one hand, and the decision-maker, as a user of these
systems, on the other;
Providing supporting information, especially for performing non-structured or semi-
structured tasks, for which it is difficult to determine in advance the data and proce-
dures of the relevant decisions.
Fig. 2. Structural diagram of the interactive information system
Decision-making in problem-oriented information systems and systems of control is
carried out in the conditions of a priori uncertainty due to inaccuracy or incompleteness
of input data, stochastic nature of external influences, and lack of adequate mathemat-
ical model of functioning, fuzzy purpose and human factor [4, 19-28]. Uncertainty in
the system increases the risk of inefficient decision making, which can result in negative
economic, technical and social consequences. The uncertainties in decision-making
systems are the outcome of the variety of artificial intelligence methods. Methods based
on the rules of fuzzy logic are used for effective decision-making in case of uncertain
conditions of system operation. Such methods are based on fuzzy sets and use linguistic
quantities and utterances to describe decision-making strategies [3, 7, 9, 10, 29-38].
4 The Diagnostics Methods
The method of expert evaluation gives an objective description of the qualitative and
quantitative aspects of the object of forecasting based on processing and analysis of a
set of individual opinions of experts.
The quality of the experts’ review, its reliability and validity are crucially dependent
on the developed methodology and processing of individual expert values, which in-
cludes the following steps:
Selection of experts and assessment of their competence;
Preparation of questionnaires for expert interviews;
Obtaining expert opinions;
Assessing the consistency of expert opinions;
Evaluation of the reliability of the results.
The advantage of expert methods is their relative simplicity for predicting almost any
situation, including in the context of incomplete information. An important feature of
these methods is the ability to predict the qualitative characteristics of the process for
the diagnosis of CAT. The disadvantages of expert methods include the subjectivity of
the opinions of experts and the limitations of their judgment.
Multi-criteria optimization or programming is the process of simultaneously opti-
mizing two or more conflicting target functions in each given areas. A multi-criteria
problem is often understood not as a verbal description of the problem itself, but rather
by its model, namely: a multi-criteria task is a mathematical model of making the opti-
mal decision by several criteria. These criteria may reflect evaluations of the various
qualities of the object or process of diagnosis of a protected area that is being decided.
Formal instrument for processing expert information is a mathematical apparatus of
fuzzy sets, which allows you to formulate decision rules for the diagnosis of CAT. It is
possible to formalize the relevant information using the linguistic variables "A", "B",
"C", "D". The linguistic variables "A", "B", "C" are the input variables, the linguistic
variable "D" is the output variable. Affiliation functions are built for each linguistic
variable, a fuzzy knowledge base is formed based on production rules, which is neces-
sitated by the need to account for the real time scale and convenience of presenting
information on procedures and conditions of their execution. Therefore, the solution of
the problem of determining the order of diagnostics of system nodes is based on the use
of the instrument of fuzzy sets theory [3, 7]. In the future, the result of the evaluation
of the diagnostic system of the CAT can used with the application of the theory of
neural networks. The essence of a neural network (NN) is to approximate the functions
of many variables through linear operations. A set of values is given to the network
input and a corresponding set of output values is set in parallel (Fig. 3) [3, 4].
Neural networks and fuzzy logic are universal approximations of complex (nonlin-
ear) functional dependencies in many intellectual cybernetics problems.
Input signals Output signals
Input layer Output layer
The inner layers
Fig. 3. Structure of the neural network
The main feature of neural networks is their ability to learn, which is implemented using
specially designed algorithms. No prior information about the structure of the requested
functional dependency is required for training the neural network. A training sample in
the form of experimental input-output pairs is. The advantage of fuzzy logic is the abil-
ity to use expert knowledge of the structure of an object in the form of linguistic ex-
pressions: if "inputs", then "output". However, the apparatus of fuzzy logic does not
contain learning mechanisms, so the results of fuzzy inference depend heavily on the
kind of membership functions that formalize fuzzy terms. The result of combining
fuzzy logic with neural networks is a neural-fuzzy network has two major intellectual
properties: linguistics, that is, the use of natural language knowledge, and the ability to
learn in real time [3]. The use of different methods of evaluation of the diagnostic sys-
tem determines the choice of the basic method. The analysis of the literature showed
that the use of neural networks for the diagnosis of communications (for example, dur-
ing routine of technical maintenance (TM), repair, as well as in a state of continuous
operation) is today a poorly understood and relevant direction that will allow to solve
this problem. There are several types of TM, each of which involves performing the
appropriate list of works. For virtually all types of TM work is carried out to check the
performance of the built-in control system or measurement of basic electrical parame-
ters using many measuring equipment, which has significant dimensions and high cost.
It takes even longer to find repair failures, measurement tools, deployment of special-
ized ports, and the involvement of highly qualified personnel.
It is possible to speed up and simplify the diagnosis process by using the neural
network apparatus, which have several important advantages:
High performance of complex logic operations;
The possibility of solving difficult formalized problems that share data of logically
incompatible nature (incomplete, incorrect);
Sustainability of work that is compatible with the expansion, transformation and im-
provement of knowledge;
The reliability that is provided by the many paths to logical inference and the ability
to recover lost data;
Ability to build self-learning and self-configuring systems;
It is well combined with the traditional "computing" information processing algo-
rithms, which allows building complex control systems with maximum reliability,
adaptability and minimum resources used.
To solve the problems logically, it is sufficient to use one of the following functions,
which determine the value of V excitation of a neuron, depending on the magnitude of
Vі and excitation of related neurons, weight ωi of these connections and threshold h [4]:
V : ( і Vіі h ) ,
1.
x, if x 0
( x) = ;
0 , otherwise
2. V : ( і Vіі h ) ,
V : if V A then A else V ;
3. V : і Vіі ,
V : if V h then V else 0 ;
4. V : і Vіі ,
V : if V h V 1 then V h else if V 1then 1 h else 0 ;
1
5. V :
n
і Vіі ,
V : if V h then V else 0 ;
where n is the amount of neuron’s active inputs.
Taking into account the advantages of the NN instruments, the task of diagnostics in
TM of different types of CAT can reduced to the process of pattern recognition, where
the image is the state of CAT as a whole or its individual components. In this case, the
decision on the status of the CAT is made using the NN apparatus and it is possible to
carry out both a quantitative assessment of the parameters and to predict its state for a
certain period.
Depending on the formulation of the problem, the input signals can be the basic
parameters (transmitter power, receiver sensitivity, etc.) and auxiliary (frequencies,
currents, and voltages at different control points of individual blocks) depending on the
problem statement. The output layer of the neural network characterizes the different
states of the system. In the course of training NN, the values of individual parameters
and the state of a particular CAT are generally memorized (image creation). The use of
this image in subsequent TM is the criterion for evaluation of "serviceable - faulty".
5 Experiment, Result and Discussion
As an example, a neural network (Fig. 4) is built to diagnose communication equipment
(an example of constructing a network for a task). The method of support vector net-
works or tracing is used during the construction. As inputs (standards) working effects
are used: those that get on the device during its intended use, and those that are its main
output parameters (you can also use specially generated test effects). At the output
layer, the expected response to the defined input standard is formed.
Take, for example, the neural network shown in Fig. 4. Suppose that all the same
neurons perform a single transfer function, and that the scales and thresholds realize
equal and general capabilities. We introduce the transfer function of an arbitrary j-th
neuron with the number of inputs m [4, 5]:
m
0 if х 0
V : (V jij hi ), ( x) ,
j 1 х if х 0
Fig. 4. A neural network built to diagnose military communications equipment
Table 1. The results of the calculation of the processing decisions
Stand- Output date
ard 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
А1,В1,С1 0.28 0 1.4 0.76 0.76 0.92 0.28 0.92 0.28 0.28 0.76 0.28 0.28 0 0 0
А2,В1,С1 0.28 0 0.76 1.4 0.76 0.92 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0 0 0
А3,В1,С1 0.28 0 0.76 0.76 1.4 0.92 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0 0 0
В1,С1 0.28 0 0.76 0.76 0.76 0.92 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0 0 0
В1,С3 0.76 0.28 0.28 0.28 0.28 0 0 0 0 0 0.28 0.28 0.28 0 0 0
В2,С3 0.28 0.76 0 0 0 0 0.28 0.28 0.28 0.28 0 0 0 0.28 0.28 0.28
А1,В2,С1 0 0.28 0.92 0.28 0.28 0.28 0.92 1.4 0.76 0.76 0 0 0 0.76 0.28 0.28
А2,В2,С1 0 0.28 0.28 0.92 0.28 0.28 0.92 0.76 1.4 0.76 0 0.28 0 0.28 0.76 0.28
А3,В2,С1 0 0.28 0.28 0.28 0.92 0.28 0.92 0.76 0.76 1.4 0 0 0.28 0.28 0.28 0.76
В2,С1 0 0.28 0.28 0.28 0.28 0.28 0.92 0.76 0.76 0.76 0 0 0 0.28 0.28 0.28
А1,В1,С2 0.28 0 0.76 0.28 0.28 0.28 0 0.28 0 0 1.4 0.76 0.76 0.28 0.28 0.28
А2,В1,С2 0.28 0 0.28 0.76 0.28 0.28 0 0 0.28 0 0.76 1.4 0.76 0.28 0.92 0.28
А3,В1,С2 0.28 0 0.28 0.28 0.76 0.28 0 0 0 0.28 0.76 0.76 1.4 0.28 0.28 0.28
А1,В2,С2 0 0.28 0.28 0 0 0 0.28 0.76 0.28 0.28 0.92 0.28 0.28 1.4 0.76 0.76
А2,В2,С2 0 0.28 0 028 0 0 0.28 0.28 0.76 0.28 0.28 0.92 0.28 0.76 1.4 0.76
А3,В2,С2 0 0.28 0 0 0.28 0 0.28 0.28 0.28 0.76 0.28 0.28 0.92 0.76 0.76 1.4
where Vj is the amount of excitation (of another neuron) that arrives at the j-th input; ωij
is weight of connections; hi – thresholds.
For example, let us define that ωij = 0,8; hi = 0,2 and submit the situation to the input
(standart) {А1, В1, С2} is "distortion of the signal of the 1st path within the normal
range". According to the inputs of neurons 2, 3, 8, they receive single impulses. The
energy data of impulses of the output neurons is calculated then considering the
weighted connections between the neurons. Because of the calculation, we obtain that
the maximum excitation value (1, 4) reached the output neuron 11 (Exit 11).
Based on the calculations for the construction of the network, we compose a Table
1, which reflects the correct operation of the network when making different decisions.
Analyzing the terms of the table, which correspond to reliable situations, it can seen
that the maximum excitation is determined correctly enough. This allows us to assert
that the neural network, designed directly for the task, is built correctly.
In [4, 5] it was shown the necessity of creating a measuring and diagnostic complex,
which may consist of a personal computer, a neural network (its software implementa-
tion) and a switching device.
For realization of this complex, it is necessary to use the device of neural networks
in carrying out diagnostics of other CATs with the use of a single package of applica-
tions. Thus, one workplace will provide diagnostics during TM and troubleshooting.
6 Conclusion
The analysis showed that the Armed Forces of Ukraine are undergoing a gradual re-
equipment of units for CATs, which allows providing a variety of information and tel-
ecommunication services. Timely diagnostics of the technical state and technical sup-
port of complex multifaceted systems that provide comprehensive automation of con-
trol processes requires the development of new, high-precision and reliable trouble-
shooting methods. Using one of these methods may include a neural network apparatus.
Technically, the process of diagnostics of various CATs can implemented by a single
measuring and diagnostic complex based on a personal computer (PC). With a switch-
ing device that connects the outputs of the equipment to the PC input, the status recog-
nition process takes seconds. The results of the construction of a neural network for
solving the problem of diagnostics of modern CAT are given, which will allow reducing
the time for conducting TM and the number of measuring instruments involved.
In the future, it is necessary to create a single package of applications for diagnostics
of different types of communication devices based on neural networks, as well as a
switching device by which the outputs of the equipment should connected with the in-
put of the PC of the operator's workplace.
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