Bulletin of National Technical University KhPI Series System Analysis Control and Information Technologies
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Published By National Technical University Kharkiv Polytechnic Institute

2410-2857, 2079-0023
Updated Wednesday, 29 December 2021

Ruslan Babudzhan ◽  
Konstantyn Isaienkov ◽  
Danilo Krasiy ◽  
Oleksii Vodka ◽  
Ivan Zadorozhny ◽  

The paper investigates the relationship between vibration acceleration of bearings with their operational state. To determine these dependencies, a testbench was built and 112 experiments were carried out with different bearings: 100 bearings that developed an internal defect during operation and 12bearings without a defect. From the obtained records, a dataset was formed, which was used to build classifiers. Dataset is freely available. A methodfor classifying new and used bearings was proposed, which consists in searching for dependencies and regularities of the signal using descriptive functions: statistical, entropy, fractal dimensions and others. In addition to processing the signal itself, the frequency domain of the bearing operationsignal was also used to complement the feature space. The paper considered the possibility of generalizing the classification for its application on thosesignals that were not obtained in the course of laboratory experiments. An extraneous dataset was found in the public domain. This dataset was used todetermine how accurate a classifier was when it was trained and tested on significantly different signals. Training and validation were carried out usingthe bootstrapping method to eradicate the effect of randomness, given the small amount of training data available. To estimate the quality of theclassifiers, the F1-measure was used as the main metric due to the imbalance of the data sets. The following supervised machine learning methodswere chosen as classifier models: logistic regression, support vector machine, random forest, and K nearest neighbors. The results are presented in theform of plots of density distribution and diagrams.

Mariia Kozulia ◽  
Vladyslava Sushko

Currently, the question of state, formation and development of the information source interaction system, the scientific interaction and users' requestsin certain fields of activity remains relevant under the conditions of the development of the use of Internet services. Recommendation systems are oneof the types of artificial intelligence technologies for predicting parameters and capabilities.Due to the rapid increase in data on the Internet, it is becoming more difficult to find something really useful. And the recommendations offered by theservice itself may not always correspond to the user's preferences. The relevance of the topic is to develop a personal recommendation system forsearching books, which will not only reduce time and amount of unnecessary information, but also meet the user's preferences based on the analysis oftheir assessments and be able to provide the necessary information at the right time. All this makes resources based on referral mechanisms attractiveto the user. Such a system of recommendations will be of interest to producers and sellers of books, because it is an opportunity to provide personalrecommendations to customers according to their preferences.The paper considers algorithms for providing recommender systems (collaborative and content filtering systems) and their disadvantages.Combinations of these algorithms using a hybrid algorithm are also described. It is proposed to use a method that combines several hybrids in onesystem and consists of two elements: switching and feature strengthening. This made it possible to avoid problems arising from the use of each of thealgorithms separately.A literature web application was developed using Python using the Django and Bootstrap frameworks, as well as SQLite databases, and a system ofrecommendations was implemented to provide the most accurate suggestion. During the testing of the developed software, the work of the literatureservice was checked, which calculates personal recommendations for users using the method of hybrid filtering. The recommendation system wastested successfully and showed high efficiency.

Oleksii Vodka ◽  
Serhii Pohrebniak

In the XXI century, neural networks are widely used in various fields, including computer simulation and mechanics. This popularity is due to the factthat they give high precision, work fast and have a very wide range of settings. The purpose of creating a software product using elements of artificialintelligence, for interpolation and approximation of experimental data. The software should work correctly, and yield results with minimal error. Thedisadvantage of using mathematical approaches to calculating and predicting hysteresis loops is that they describe unloading rather poorly, thus, weobtain incorrect data for calculating the stress-strain state of a structure. The solution tool use of elements of artificial intelligence, but rather neuralnetworks of direct distribution. The neural network of direct distribution has been built and trained in this work. It has been trained with a teacher (ateacher using the method of reverse error propagation) based on a learning sample of a pre-experiment. Several networks of different structures werebuilt for testing, which received the same dataset that was not used during the training, but was known from the experiment, thus finding a networkerror in the amount of allocated energy and in the mean square deviation. The article describes in detail the mathematical interpretation of neuralnetworks, the method for training them, the previously conducted experiment, structure of network that was used and its topology, the training method,preparation of the training sample, and the test sample. As a result of the robots carried out, the software was tested in which an artificial neuralnetwork was used, several types of neural networks with different input data and internal structures were built and tested, the error of their work wasdetermined, the positive and negative sides of the networks that were used were formed.

Oleksandr Mazmanishvili ◽  
Nikolay Reshetnyak ◽  
Ganna Sydorenko

The article presents the results of research and calculations on the formation of a radial electron beam by a magnetron gun with a secondary emission cathode in the electron energy range 35...65 keV and measuring its parameters during transportation in the total decreasing magnetic field of thesolenoid and the stray field of permanent magnets. The beam was transported in a system consisting of copper rings with an inner diameter of 66 mm,located at a distance of 85 mm from the exit of the magnetron gun. The dependence of the beam current on the amplitude and gradient of the fielddecay has been studied. The studies carried out have shown the possibility of stable formation of a radial electron beam with an energy of tens of keVin the decreasing magnetic field of the solenoid. By optimizing the distribution of the magnetic field (created by the solenoid and ring magnets) and itsdecay gradient, it is possible to achieve an increase in the incident of electrons on one ring (up to ~72% of the beam current). On the basis of themathematical model of the movement of the electron flow, a software tool has been synthesized that makes it possible to obtain and interpret thecharacteristics of the resulting flows. The obtained numerical dependences are in satisfactory agreement with the experimental results for a magneticfield with a large decay gradient. Various configurations of the magnetic field are considered. Solutions to the direct problem of modeling electrontrajectories for given initial conditions and parameters are obtained. Various configurations of the magnetic field are considered. It is shown that forthe selected initial conditions for the electron beam and the distributions of the longitudinal magnetic field along the axis of the gun and the transportchannel, the electron flux falls on a vertical section, the length of which is on the order of a millimeter. Thus, by changing the amplitude anddistribution of the magnetic field, it is possible to control the current in the radial direction along the length of the pipe, and, therefore, the place of theelectron irradiation.

Shepeliev Oleksandr ◽  
Mariia Bilova

The relevance of scientific work lies in the need to improve existing software designed to analyze the compliance of the results of software testing ofthe stated requirements. For the implementation of this goal, neural networks can be used by quality control specialists to make decisions aboutsoftware quality, or project managers as an expert system, for one of the quality indicators for the customer. The article deals with software testingwhich is a process of validation and verification of compliance of the software application or business program with the technical requirements thatguided its design and development, and work as expected, and identifies important errors or deficiencies classified by the severity of the program to befixed. Existing systems do not provide for or have only partial integration of systems of work with the analysis of requirements, which should ensurethe formation of expert assessment and provide an opportunity to justify the quality of the software product. Thus, a data processing model based on afuzzy neural network was proposed. An approach to allow determining the compliance of the developed software with functional and non-functionalrequirements was proposed, taking into account how successfully or unsuccessfully implemented this or that requirement. The ultimate goal ofscientific work is the development of algorithmic software analysis of compliance of software testing results to stated requirements for support in thedecisions taken. The following tasks are solved in scientific work: analysis of advantages and disadvantages of using existing systems when workingwith requirements; definition of general structure and classification of testing and requirements; characteristic main features of the use of neuralnetworks; designing architecture, the module of research of conformity of results of testing software to the stated requirements.

Volodymyr Sokol ◽  
Vitalii Krykun ◽  
Mariia Bilova ◽  
Ivan Perepelytsya ◽  
Volodymyr Pustovarov ◽  

The demand for the creation of information systems that simplifies and accelerates work has greatly increased in the context of the rapidinformatization of society and all its branches. It provokes the emergence of more and more companies involved in the development of softwareproducts and information systems in general. In order to ensure the systematization, processing and use of this knowledge, knowledge managementsystems are used. One of the main tasks of IT companies is continuous training of personnel. This requires export of the content from the company'sknowledge management system to the learning management system. The main goal of the research is to choose an algorithm that allows solving theproblem of marking up the text of articles close to those used in knowledge management systems of IT companies. To achieve this goal, it is necessaryto compare various topic segmentation methods on a dataset with a computer science texts. Inspec is one such dataset used for keyword extraction andin this research it has been adapted to the structure of the datasets used for the topic segmentation problem. The TextTiling and TextSeg methods wereused for comparison on some well-known data science metrics and specific metrics that relate to the topic segmentation problem. A new generalizedmetric was also introduced to compare the results for the topic segmentation problem. All software implementations of the algorithms were written inPython programming language and represent a set of interrelated functions. Results were obtained showing the advantages of the Text Seg method incomparison with TextTiling when compared using classical data science metrics and special metrics developed for the topic segmentation task. Fromall the metrics, including the introduced one it can be concluded that the TextSeg algorithm performs better than the TextTiling algorithm on theadapted Inspec test data set.

Natalia Marchenko ◽  
Ganna Sydorenko ◽  
Roman Rudenko

The article considers the study of methods for numerical solution of systems of differential equations using neural networks. To achieve this goal, thefollowing interdependent tasks were solved: an overview of industries that need to solve systems of differential equations, as well as implemented amethod of solving systems of differential equations using neural networks. It is shown that different types of systems of differential equations can besolved by a single method, which requires only the problem of loss function for optimization, which is directly created from differential equations anddoes not require solving equations for the highest derivative. The solution of differential equations’ system using a multilayer neural networks is thefunctions given in analytical form, which can be differentiated or integrated analytically. In the course of this work, an improved form of constructionof a test solution of systems of differential equations was found, which satisfies the initial conditions for construction, but has less impact on thesolution error at a distance from the initial conditions compared to the form of such solution. The way has also been found to modify the calculation ofthe loss function for cases when the solution process stops at the local minimum, which will be caused by the high dependence of the subsequentvalues of the functions on the accuracy of finding the previous values. Among the results, it can be noted that the solution of differential equations’system using artificial neural networks may be more accurate than classical numerical methods for solving differential equations, but usually takesmuch longer to achieve similar results on small problems. The main advantage of using neural networks to solve differential equations` system is thatthe solution is in analytical form and can be found not only for individual values of parameters of equations, but also for all values of parameters in alimited range of values.

Sergey Orekhov ◽  
Hennadiy Malyhon

An approach to the mathematical description of the criterion for the effectiveness of a new object of research – virtual promotion is presented in thepaper. The emergence of this new object of research is connected, on the one hand, with the classical theory of marketing, and on the other withmodern Internet technologies. Marketing is based on the 4P principle: product, price, location and promotion. Promotion is a component of thisprinciple. But in modern conditions, this phenomenon is changing under the influence of the Internet. Now this 4P component is becoming a fullyvirtual instrument. The traditional scheme of promotion functioning is as follows. A message is created to a potential buyer and the delivery channel ofthis message undergoes a change. It is based on the principle: money – goods – money. While the new sales scheme is described by the scheme: weattract a client, make money on a client, we spend money. In the new scheme, we deal with product knowledge in the form of the so-called semanticcore of web content. Knowledge describes for a potential client how a given product can cover his need for something. Using the logistic principles ofthe transfer of goods, this semantic core is loaded into the specified Internet nodes. That is, virtual promotion is formed as two channels: logistics andmarketing. The first one performs three operations: concentration, formatting and distribution of semantic cores on the Internet. The second managesthis process, forming a virtual promotion map. This map is a graph of Internet nodes. It is required to define such a tree of Internet nodes so that virtualpromotion has maximum efficiency. The paper analyzes modern metrics related to the processes of search engine optimization on the Internet.Unfortunately, these metrics evaluate only statistically after the fact of visiting a web resource or the budget of the Internet site in which theadvertising message about the product was placed. Therefore, based on the conversion metric, a criterion for the effectiveness of virtual promotion wasproposed in the work, which takes into account both the attractiveness of the semantic core and the attractiveness of the Internet site where thesemantic core will be located. The criterion reflects the income that we receive depending on the attractiveness of the semantic kernel and the Internetsite.

Liliia Bodnar ◽  
Kateryna Shulakova ◽  
Liudmyla Gryzun

This work is devoted to the analysis of algorithmic support of multimedia content recommender systems and the development of a web service toincrease the efficiency of learning foreign languages using a recommender system that personalized the selection of educational content for the user.To form a list of necessary multimedia content, the main criteria of the recommender system were selected, the basic needs of users were identified,which the system should solve, since increasing the efficiency of learning a foreign language is achieved not only by choosing teaching methods, butalso by watching multimedia content, namely news, films, educational videos, clips, etc. Therefore, in order to form a list of the necessary multimediacontent, the main criteria of the recommender system were formed, the main needs of users were identified, which the system must solve. From theside of the method for implementing algorithmic support, various types of data filtering were considered, from modern technical methods to librariesto ensure the functionality of the system, and the algorithm based on hybrid filtering was chosen, in which known user ratings are used to predict thepreferences of another user. Functional requirements have been developed and a web service has been proposed that allows a comprehensive impact onuser learning when learning a foreign language, software implementation of which is made using Java Script, Python and additional libraries. Thisimplementation allows you to build a process for tracking changes in user requirements and transfer information to the database (DB) and, afteranalyzing the input data, change the proposed multimedia content to the user. In the course of further research, it is planned to conduct practicalexperiments, taking into account the specifics of certain methods of teaching foreign languages and the use of statistical data to assess the effectivenessof the algorithm of the proposed recommender system.

Alexander Pavlov

We substantiate the structure of the efficient numerical axis segment an active experiment on which allows finding estimates of the coefficients fornonlinear terms of univariate polynomial regression with high accuracy using normalized orthogonal Forsyth polynomials with a sufficiently smallnumber of experiments. For the case when an active experiment can be executed on a numerical axis segment that does not satisfy these conditions, wesubstantiate the possibility of conducting a virtual active experiment on an efficient interval of the numerical axis. According to the results of the experiment, we find estimates for nonlinear terms of the univariate polynomial regression under research as a solution of a linear equalities system withan upper non-degenerate triangular matrix of constraints. Thus, to solve the problem of estimating the coefficients for nonlinear terms of univariatepolynomial regression, it is necessary to choose an efficient interval of the numerical axis, set the minimum required number of values of the scalarvariable which belong to this segment and guarantee a given value of the variance of estimates for nonlinear terms of univariate polynomial regressionusing normalized orthogonal polynomials of Forsythe. Next, it is necessary to find with sufficient accuracy all the coefficients of the normalized orthogonal polynomials of Forsythe for the given values of the scalar variable. The resulting set of normalized orthogonal polynomials of Forsythe allows us to estimate with a given accuracy the coefficients of nonlinear terms of univariate polynomial regression in an arbitrary limited active experiment: the range of the scalar variable values can be an arbitrary segment of the numerical axis. We propose to find an estimate of the constant and ofthe coefficient at the linear term of univariate polynomial regression by solving the linear univariate regression problem using ordinary least squaresmethod in active experiment conditions. Author and his students shown in previous publications that the estimation of the coefficients for nonlinearterms of multivariate polynomial regression is reduced to the sequential construction of univariate regressions and the solution of the correspondingsystems of linear equalities. Thus, the results of the paper qualitatively increase the efficiency of finding estimates of the coefficients for nonlinearterms of multivariate polynomial regression given by a redundant representation.

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