
Keyword: "machine learning"
Damir Albertovich Murzagulov, graduated from Tomsk Polytechnic University (TPU); Improvado.io Chief Analyst; Postgraduate Student of the Department of Fundamental Informatics and Information Technologies of Tomsk State University; an author of articles in the field of the process data anomaly detection. email: murzagulov.damir@gmail.comD. A. Murzagulov
Aleksandr Vladimirovich Zamiatin, Doctor of Sciences in Engineering, Associate Professor; graduated from Tomsk Polytechnic University (TPU); Head of the Department of Fundamental Informatics and Information Technologies of Tomsk State University; an author of articles and guides in the field of data mining. email: zamyatin@mail.tsu.ruA. V. Zamiatin 

The process signal anomaly detection using classifier ensemble and wavelet transforms
The ITinfrastructure development level of manufacturing plants allows to collect and storage technological information, thereby creating the possibilities to adapt datamining systems. The article deals with the problem of anomaly detection in process signals with a view to improving the quality of control object monitoring. The ensemble of base classifiers based on algorithms of machine learning and wavelet transforms is proposed to detect anomalies. Authors examine the process signal characteristics and wavelet analysis advantages for signal preprocessing. An approach to the anomaly detection was developed based on a model ensemble. This approach was previously tested on actual process signals.
Process signals, machine learning, wavelet transform, anomaly detection, ensemble.



Sections: Automated control systems
Subjects: Automated control systems. 
Maria Vladimirovna Stepanova,, graduated from the Faculty of Informatics and Control Systems of Bauman Moscow State Technical University (BMSTU) with Master’s degree in Computer Science; Postgraduate Student at the Department of Computer Systems and Networks of BMSTU, an author of scientific papers in the field of adaptive technologies in distributed systems and in education. email: stepanova@bmstu.ruM. V. Stepanova
Oleg Iurevich Eremin, Candidate of Sciences in Engineering; graduated from the Aerospace Faculty of Bauman Moscow State Technical University; Associate Professor of the Department of Computer Systems and Networks of the Faculty of Informatics and Control Systems at BMSTU; an author of articles in the field of adaptive technologies for processing unstructured and semistructured data. email: ereminou@bmstu.ruO. I. Eremin 

The assignment of tasks to the nodes of the iot distributed system based on reinforcement machine learning
The article describes issues of applying an adaptive approach based on reinforcement learning for assignment of the computing tasks to nodes of distributed Internet of Things (IoT) platform. The IoT platform consists of heterogeneous elements that are computing nodes. Classical approaches, methods, and algorithms for distributed and parallel systems are not suitable for task assignment in IoT systems due to its characteristics. The reinforcement learning method allows you to solve the problem of building a distributed system due to the adaptive formation of a sequence of computational nodes and the corresponding computational tasks. Thus, the article represents a method that makes IoT nodes capable of execution computing tasks, especially, which were previously designed for classical distributed and parallel systems.
Internet of Things, distributed systems and computations, computations nodes, programme code, parallel computing, machine learning, reinforcement learning, adaptive method.



Sections: Automated control systems
Subjects: Automated control systems. 
Alexander Viacheslavovich Mikheev, graduated from Ulyanovsk State Technical University; Programmer of the Ulyanovsk Regional Center of New Information Technologies at the Ulyanovsk State Technical University; an author of more than 15 research papers, a holder of the certificate of software registration; the area of his scientific interests relates to the machine learning. email: a.miheev@simcase.ruA.V. Mikheev
Kirill Valerevich Sviatov, Candidate of Sciences in Engineering; graduated from Ulyanovsk State Technical University; Dean of the Faculty of Information Systems and Technologies of UlSTU; an author of more than 40 articles, three monographs, three textbooks; a holder of four certificates of software registration; the area of his scientific interests relates to machine learning and robotics. email: k.svyatov@ulstu.ruK.V. Sviatov
Iurii Aleksandrovich Lapshov, Candidate of Sciences in Engineering; graduated from Ulyanovsk State Technical University; Associate Professor of the Computer Engineering Department at UlSTU; an author of 32 articles, a monograph; a holder of two certificates of software registration; the area of his scientific interests relates to computeraided design systems, workflow management. email: y.lapshov@ulstu.ruI.A. Lapshov
Vadim Georgievich Tronin, Candidate of Sciences in Engineering; graduated from Ulyanovsk State Technical University; Associate Professor of the Department of Information Systems at UlSTU; an author of more than 50 articles, a textbook; the area of his scientific interests relates to the project management and theory of solving inventive problems. email: v.tronin@ulstu.ruV.G. Tronin


Software for determining features of a mobile robot’s path with the use of neural networks The article describes an approach to the implementation of a software package for determining the probability of a collision of a mobile robot with obstacles. This approach is based on a neural network model with attention. Key feature is the method of the training dataset generation: the labeling of obstacles and the values of the probability of collision with them is performed not in manual mode, but using a deterministic algorithm that uses the result of semantic segmentation using another pretrained neural network. This method allows to use a poorly detailed description of the external environment for training convolutional neural networks with attention on the example of recognizing obstacles when a mobile robot moves in simulation mode. At the same time, low detail allows to reduce the timeconsuming process of manual data labeling due to automatically generated sampling in the NVIDIA Isaac environment, and the attention mechanism allows to increase the interpretability of the analysis results.
Artificial intelligence, neural networks, machine learning, computer vision, attention networks, robotics.



Sections: Artificial intelligence
Subjects: Artificial intelligence. 
Viacheslav Andreevich Sergeev, Doctor of Sciences in Engineering, Professor; graduatedfromthe Faculty of Physics of Gorky State University n.a. Lobahevsky; Director of the Ulyanovsk Branch of the Kotel’nikov Institute of Radio Engineering and Electronics of the Russian Academy of Sciences; Head of the Department of Radioengineering, Opto and Nanoelectronics of Ulyanovsk State Technical University (UlSTU); an author of monographs, papers, and inventions in the field of modeling and researching semiconductor devices and integrated circuits characteristics and measuring its thermal parameters. email: sva@ulstu.ruV. A. Sergeev
Mikhail Iurevich Leontev, completed postgraduate studies at the Faculty of Mathematics, Information, and Aviation Technologies at Ulyanovsk State University; Junior Research Associate of the Scientific and Research Institute of Technology n.a. S.P. Kapitsa; Leading Engineer of the Ulyanovsk Branch of the Kotel’nikov Institute of RadioEngineering and Electronics of RAS; an author of eight articles, a certificate of registration for computer program. The area of his research interests relates to learning and robotics. email: ulstaer@gmail.comM. I. Leontev
Victoriia Iurevna Islenteva, a student of the Faculty of Information Systems and Technologies of Ulyanovsk State Technical University; an author of two articles. The area of her research interests relates to machine learning. email: viksaskin@yandex.ruV. I. Islenteva
Sergei Vladimirovich Sukhov, Candidate of Sciences in Physics and Mathematics; graduated from the Ulyanovsk Branch of Lomonosov Moscow State University; Senior Staff Scientist at the Ulyanovsk Branch of the Kotel’nikov Institute of RadioEngineering and Electronics of RAS; Associate Professor at the Department of Applied Mathematics and Informatics of UlSTU; an author of a monograph, 90 articles, two patents for inventions in the field of optics, computational neuroscience, and machine learning. email: ssukhov@ulireran.ruS. V. Sukhov


Evaluation of generative approaches to knowledge sharing in neural networks
The ability to interact and share knowledge among artificial neural networks (ANNs) is critical for further development of the artificial intelligence. One of the methods of knowledge sharing among ANNs may rely on the generation of training data using auxiliary generative models, which have recently become widespread. To make such methods more efficient, it is necessary to further improve the quality of artificial training data developed by generative models. The article proposes and tests several methods of enhancing characteristics of one of the classes of generative models, viz. variational autoencoders (VAE). It was improved both the VAE training procedure and changes in the model architecture. The improving methods of the quality of generated training data have been analyzed in terms of the efficiency of knowledge sharing among ANNs. To test the process of knowledge sharing among ANNs, the public training data sets have been used.
Generative networks, variational autoencoders, neural networks, machine learning, image classification, knowledge sharing.



Sections: Artificial intelligence
Subjects: Artificial intelligence. 
Dmitrii Vladimirovich Kurganov, Candidate of Sciences in Physics and Mathematics; graduated from Samara University; Associate Professor at the Department of Oil and Gas Engineering of Samara State Technical University; an author of articles, patents in the field of the modeling of oil and gas field development processes. email: Dmitri.Kourganov@inbox.ruD.V. Kurganov


The calculation of the bottomhole treatment success probability using machine learning techniques
Machine learning is the most widely used branch of science and engineering nowadays. The availability of electronic real wide information is an important condition for implementing the machine learning. Through the long history of exploitation of oil fields, a significant database related with the wells’ development and applied techniques stimulating production was created and accumulated. The article deals with one of the machinelearning methods of analyzing the prediction of geological and engineering operations implemented in the producing oil wells. In particular, by the example of database data including the results of hydrochloricacidandmudacid jobs implemented in the oil fields of UralVolga region, as well as in terms of specific decisiontree models, the probability of successful geological and engineering operations is calculated, and recommendations on the selection of factors allowing to optimize these operations are given. The openness, average number of permeable intervals, reservoir temperature, actual well watering, fluid properties are parameters influencing the geological and engineering operations efficiency. In many cases, it is difficult to predict the influence in the future especially if other factors are available. The existing analytical models could not describe fully the factor variety in processes running in a bottomhole zone, especially in the context of nonlinear flotations, physical and chemical interaction between formation fluids and injection solutions. The described technique allows using any number of key factors and any combination of them, as well as detecting the most important of them including the parameters described, but not limited to. In this case, the application of decision tree models is an intuitive way allowing to select neatly sampling attributes at each level by the use of algorithms. The article also describes in detail the algorithm of sampling attribute calculation. Decision tree methodology can be used for solving other problems in the oil producing industry with significant practical experience.
Hydrochloric acid treatment, mud acid treatment, bottomhole treatment, yield, crude oil, oil well, probability, machine learning, decision tree, modeling.



Sections: Information systems
Subjects: Information systems. 
Vladimir Nikolaevich Naumov, Doctor of Military Sciences, Professor, Honored Scientist of the Russian Federation; graduated from the Faculty of Electronic Computer Equipment of the A.S. Popov Higher Naval School of Radioengineering; Head of the BusinessInformatics Department of the NorthWest Institute of Management, the Branch of the Russian Presidential Academy of National Economy and Public Administration in St. Petersburg; an author of articles, monographs, tutorials in the field of systemoriented analysis of information systems, data analysis and machine learning. email: naumov122@list.ruV.N. Naumov,
Pavel Vladimirovich Naumov, graduated from Peter the Great St. Petersburg Polytechnic University; Chief Developer of organizational and executive documentation at Shipbuilding&Shiprepair Technology Center; an author of articles in the field of risk management. email: naumov@sstc.spb.ruP.V. Naumov


Forecasting the risks of information system design
The article considers the problem of forecasting the risks of designing information systems based on the use of machine learning methods. The ensemble of methods allows us to estimate the quality of the solution of a problem using a set of indicators and select the best one. To solve it, the language R, the statistical platform SPSS, and the Orange Canvas system are used. Machine learning is based on the use of a synthetic training set, which includes categorical factors of possible design risks. The sample is formed and verified using the methods of cluster analysis and the principal component method.
Forecasting, information systems design, risks, machine learning, cluster analysis, principal component method, classification methods, classification quality indicators.



Sections: Information systems
Subjects: Information systems. 
Dmitrii Anatolevich Zhukov, graduated from the Faculty of Information System and Technologies of Ulyanovsk State Technical University; Database Specialist of the Ulyanovsk Branch of Tupolev Design Bureau, PJSC; Postgraduate Student at the Department of Applied Mathematics and Informatics of UlSTU; an author of publications in the field of statistical methods and machine learning. email: zh.dimka17@mail.ru.D. Zhukov,


Analysis Of Classification Quality Criteria For Diagnostics Of Technical Object Operation
When predicting the technical object health based on the known indicators of its previous operation, the problem of binary classification of the object state is solved. This problem may be solved using the machine learning methods, and due to imbalance of classes, the most reliable diagnostics quality measure is the Fmeasure. As a rule, the healthystate data amount contained in the set of the use cases based on the results of object operation exceeds the unhealthy state data amount. Values of this measure are random variables as they are estimated using validation dataset formed in a random manner. The study showed that the distribution of this measure both for basic classifiers and for aggregated classifiers is close to standard one. A specific example demonstrates that the average value of Fmeasure in aggregation process exceeds the similar value obtained using the basic classifiers. Technical diagnostics, healthy state, performance indexes, machine learning, aggregated approach, classification quality criteria.



Sections: Mathematical modeling
Subjects: Mathematical modeling. 
Vladimir Nikolaevich Kliachkin, Ulyanovsk State Technical University, Doctor of Science in Engineering; graduated from the Mechanical Faculty of the Ulyanovsk Polytechnic Institute; Professor at the Department of Applied Mathematics and Computer Science of Ulyanovsk State Technical University; an author of articles in the field of reliability issues and statistical methods. [email: v_kl@mail.ru]V. Kliachkin,
Dmitrii Anatolevich Zhukov, Ulyanovsk Branch of PJSC ‘Tupolev Design Bureau’, graduated from the Faculty of Information System and Technologies of Ulyanovsk State Technical University; a database engineer at the Ulyanovsk Branch of PJSC ‘Tupolev Design Bureau’; Postgraduate Student of the Department of Applied Mathematics and Computer Science; an author of articles in the field of the statistics methods and machine learning. [email: zh.dimka17@mail.ru]D. Zhukov


Algorithm of Diagnostics of Technical Object Operation Using Aggregated Classifiers
The paper addresses the issue of prediction of technical object’s state of health using the known indicators of its operation. The basic data are the known results of the object state estimation by information about previous service: the technical system is healthy or faulty with predetermined values of specified indicators. Such problem may be solved using the machine learning methods, it reduces to binary classification of states of the object. The study showed that diagnostics quality may be improved by means of the binary classification method including aggregated approach, as well as by means of selection of the volume of the validation set and the method of selection of relevant indicators of object operation. In the example (used for the algorithm testing), Fmeasure value, which is the most reliable diagnostics quality measure for unbalanced classes, has increased by 6% (from 0.86 to 0.91), compared with the Support Vector Machine, and by 2%, compared with the bagging of decision trees which is the best basic method for the example considered (F = 0.89). In some cases, this 2% may be of significance from the perspective of the object operation security. Technical diagnostics, healthy state, operation indicators, machine learning, aggregated approach, validation set, crossvalidation.



Sections: Information systems
Subjects: Information systems. 
Valerii Vladimirovich Kozhevnikov, Scientific Research Technological Institute of Ulyanovsk State University, Candidate of Science in Engineering; graduated from the Pushkin Higher Command School of Radioelectronics; Senior Researcher at the Scientific Research Technological Institute of Ulyanovsk State University; an author of articles in the field of microelectronic system design theory.[email: vvk28061955@mail.ru]V. Kozhevnikov


The Method of Mathematical Modeling of Cognitive Digital Automata
An approach to solving the problem of mathematical modeling of cognitive digital automata (CDA) is proposed. The task of formalizing the concept of the cognitive nature of the CDA mathematical model comes to the fore. The cognitiveness (cognition) of the mathematical model is determined by the possibility of learning and generating solutions that are not provided for in the learning process. A special feature of CDA is that the description of the neural network (NN) structure is used as a structural circuit of the automata, and the logical function "NOTANDOR" is used as the model of the neuron. In the case of the feedbacks formation from the output to the inputs of the neurons, the model of the neuron is a binary trigger with a logical function "NOTANDOR" at the input. As a tool for constructing a mathematical model of CDA, a mathematical apparatus of Petri nets (PNs) is proposed: marked graphs, inhibitory PNs and PNs with programmable logic. The mathematical model is builton the basis of the representation of the CDA in the form of the state equation of the PNs from the class of Murat equations (matrix equations) or a system of linear algebraic equations. The task of formalizing the concept of cognitiveness (cognition) is solved as a result of the logic synthesis (learning) of the initial structural circuit of CDA or the formation of the formula (network algorithm) of CDA. At the same time, the possibility of forming a formula (network algorithm) of CDA depends on the critical mass (quality) of training sets and training algorithms. Hence, the task of generating the minimum set of training sets for a given CDA function or experimentally determined function takes on particular importance. Forecasting or generation of solutions, in turn, is performed on the basis of the mathematical model of CDA obtained in the learning process. intellectual control system, cognitive digital automata, artificial intelligence, neural networks, machine learning, cognition, Petri nets, equation of states, mathematical modeling, synthesis, generation, analysis, logic.



Sections: Mathematical modeling
Subjects: Mathematical modeling, Artificial intelligence. 
Aleksandr Viacheslavovich Mikheev, Ulyanovsk Regional Center of New Information Technologies of Ulyanovsk State Technical University, graduated from Ulyanovsk State Technical University; Software Engineer at the Ulyanovsk Regional Center of New Information Technologies of Ulyanovsk State Technical University; his research interests include machine learning. [email: a.miheev@simcase.ru]A. Mikheev,
Kirill Valerevich Sviatov, Ulyanovsk State Technical University, Candidate of Science in Engineering; graduated from Ulyanovsk State Technical University; Dean of the Faculty of Information Systems and Technologies at UlSTU; an author of 40 articles, 3 monographs, 1 textbook, and 4 State Registration Certificate of the Computer Program; his research interests are in the field of machine learning and robotics. [email: k.svyatov@ulstu.ru]K. Sviatov,
Daniil Pavlovich Kanin, Ulyanovsk State Technical University, a student of the Faculty of Information Systems and Technologies of the Computer Science Department at Ulyanovsk State Technical University, a winner of robotics competitions; his research interests are in the field of machine learning. [email: dankan@mail.ru]D. Kanin,
Sergei Vladimirovich Sukhov, Ulyanovsk Branch of the Kotel’nikov Institute of Radioengineering and Electronics of the Russian Academy of Sciences, Candidate at Science in Physics and Mathematics; graduated from the Ulyanovsk Branch of Lomonosov Moscow State University; Senior Researcher at the Ulyanovsk Branch of the Kotel’nikov Institute of Radioengineering and Electronics of the Russian Academy of Sciences; an author of one monograph, 70 articles, and 2 patents for inventions; his research interests are in the field of optics, computational neurobiology, machine learning [email: ssukhov@knights.ucf.edu]S. Sukhov,
Vadim Georgievich Tronin, Ulyanovsk State Technical University, Candidate of Science in Engineering; graduated from Ulyanovsk State Technical University; Associated Professor at the Department of Information Systems of UlSTU; an author of 50 articles, 1 textbook; his research interests are in the field of project management and theory of inventive problemsolving. [email: v.tronin@ulstu.ru]V. Tronin


The Scene Segmentation in the Tasks for Selfdriving Vehicle Navigation By Using Neural Network Models With Attention
The article deals with the designing process of software module for the road signs recognition. This module is designated for the use in the automated control system of selfdriving vehicles and is being developed at Ulyanovsk State Technical University.The creation of the training dataset sufficient to train neural network models is one of the major tasks to be solved when creating computer vision systems for selfdriving vehicles. In this case, the preparation of a large sample in the semantic scene segmentation task may require considerable efforts for “manual” labeling. Authors describe a convolutional network model with a soft attention mechanism. This network is trained in the classification task with the possibility of extracting an attention mask from the internal network state, which can be used for the semantic image segmentation. This approach allows to reduce datalabeling costs significantly. Artificial intelligence, neural networks, machine learning, computer vision, attention networks.



Sections: Information systems
Subjects: Information systems, Artificial intelligence. 
Aleksandr Viacheslavovich Mikheev, Ulyanovsk State Technical University, graduated from Ulyanovsk State Technical University; Software Engineer of Ulyanovsk Regional Center for New Information Technologies at Ulyanovsk State Technical University; his research interests include machine learning. [email: a.miheev@simcase.ru]A. Mikheev,
Kirill Valerevich Sviatov, Ulyanovsk State Technical University, Candidate of Science in Engineering; graduated from Ulyanovsk State Technical University; Dean of the Faculty of Information Systems and Technologies of Ulyanovsk State Technical University;an author of 40 articles, three monographs, one textbook, and four certificates of registration of computer software programs; his research interests include machine learning and robotics. [email: k.svyatov@ulstu.ru]K. Sviatov,
Sergei Vladimirovich Sukhov, Ulyanovsk Branch of the Kotel’nikov Institute of Radioengineering and Electronics of the Russian Academy of Sciences, Candidate of Science in Physics and Mathematics; graduated from the Ulyanovsk Branch of Lomonosov Moscow State University; Senior Staff Scientist at the Ulyanovsk Branch of the Kotel’nikov Institute of Radioengineering and Electronics of the Russian Academy of Sciences; an author of one monograph, 70 articles, two patents of inventions in the field of optics, computational neurobiology, machine learning. [email: ssukhov@knights.ucf.edu]S. Sukhov,
Daniil Pavlovich Kanin, Ulyanovsk State Technical University, Student of the Faculty of Information Systems and Technologies of Ulyanovsk State Technical University; Winner of robotics competitions; his research interests include machine learning. [email: dankan@mail.ru]D. Kanin,
Iakov Andreevich Akimov, Ulyanovsk State Technical University, Student of the Faculty of Information Systems and Technologies of Ulyanovsk State Technical University; Winner of robotics competitions; his research interests include machine learning. [email: yasha.akimov.73@gmail.com]I. Akimov,
Pavel Mikhailovich Volkov, Ulyanovsk State Technical University, Student of the Faculty of Information Systems and Technologies of Ulyanovsk State Technical University; Winner of robotics competitions; his research interests include machine learning. [email: p.vollkkoff@yandex.ru]P. Volkov


Changing the Lighting in the Scenes By Using Neural Networks Cascades
The article describes the process of creating the artificial illumination of surfaces taking into account shadows on convex and concave surfaces using machine learning algorithms and methods of working with threedimensional objects.Despite the variety of applications for photo processing (LightRoom, Photoshop, Snapchat, Instagram etc.), there is a problem associated with the correct modeling of lighting on surfaces, due to the peculiarity of the photo format. First, the photo is a twodimensional object and contains only indirect information about threedimensional objects depicted on it. Secondly, the photoimage captures a certain state of objects with a certain set of properties, which are not enough for modeling shadows and lighting. The missing information is difficult to recreate by classical algorithms or heuristics because of the large number of parameters for recognition of objects in the photo and their possible combinations. However, machine learning algorithms are able to approximate very complex functional relationships based on training samples. This article describes the solution to the problem of modeling artificial lighting and shadows in photography using several neural networks trained on synthetic data. Artificial intelligence, neural networks, machine learning, computer vision.



Sections: Artificial intelligence
Subjects: Artificial intelligence. 
Kirill Valerevich Sviatov, Ulyanovsk State Technical University, Candidate of Science in Engineering; graduated from Ulyanovsk State Technical University; Dean of the Faculty of Information Systems and Technologies at Ulyanovsk State Technical University; an author of articles, monographs, textbooks, and certifications of software registration in the field of machine learning and robotic engineering. [email: k.svyatov@ulstu.ru]K. Sviatov,
Aleksandr Viacheslavovich Mikheev, Ulyanovsk Regional Center of New Information Technologies at UlSTU, graduated from Ulyanovsk State Technical University; software engineer of the Ulyanovsk Regional Center of New Information Technologies at UlSTU; interested in the field of machine learning. [email: a.miheev@simcase.ru]A. Mikheev,
Maksim Andreevich Shliamov, Ulyanovsk State Technical University, Student of the Faculty of Information Systems and Technologies at Ulyanovsk State Technical University; interested in the field of machine learning. [email: m.shlyamov@simcase.ru]M. Shliamov


Software Package for Visual Monitoring of the Cargo Transportation
The article considers the process of designing and implementing a software package to check the completeness of trucks loading when passing checkpoints using technical vision algorithms and machine learning methods. Load completeness checking is used in the governmental supervision of the Russian roads operation as well as checking the cargo transportation volume by private companies when executing orders under the agreements for cargo transportation services. The article deals with the problem of supervising the volume of transported cargo in opentop trucks with bulk nonmetallic materials (sand, gravel, coal). Currently, the problem of roadfreight transport weightchecking is solved in most cases by the use of weighing units. Also, there are solutions when ultrasonic distance sensors are used that generate a signal corresponding to the shape of the vehicle when driving under several sensors. However, these solutions are quite expensive to install, they do not consider the difference between signals when various vehicle types or models are used on one route. In addition, the weight does not always reflect the completeness of the load body because some materials weigh much more than payload at low volume. And under different weather conditions (e.g. high humidity), the mass of certain goods can vary significantly within 15 percent. At the same time, the transportation efficiency is estimated based on the minimum possible number of passes for the export of cargo of the given volume. The vision systems allow to solve the problem on checking the transported bulk cargo volume. The article focuses on the development description of such a system. Computeraided design, artificial intelligence, neural networks, machine learning, computer vision.



Sections: Information systems
Subjects: Information systems, Artificial intelligence. 
Dmitrii Anatolevich Zhukov, Ulyanovsk State Technical University, graduated from the Faculty of Information System and Technologies of Ulyanovsk State Technical University; Postgraduate Student of the Department of Applied Mathematics and Computer Science; an author of proceedings in the field of the statistical methods and machine learning. [email: zh.dimka17@mail.ru]D. Zhukov,
Vladimir Nikolaevich Kliachkin, Ulyanovsk State Technical University, Doctor of Engineering; graduated from the Mechanical Faculty of Ulyanovsk Polytechnic Institute; Professor at the Department of Applied Mathematics and Informatics of Ulyanovsk State Technical University; an author of scientific papers in the field of reliability issues and statistical methods. [email: v_kl@mail.ru]V. Kliachkin


The Effect of the Control Sample Volume on the Quality of Diagnostics of the Technical Object State
He problem of predicting the serviceability of a technical object in terms of its performance is considered. The source data are the known results of the evaluation of the state of an object based on the results of the previous operation: If the specified values of controlled indicators technical system intact or defective. Such a problem can be solved by methods of machine learning, it reduces to a binary classification of the states of the object. The quality of diagnostics can significantly depend on many factors: the method of training, the correct allocation of factors characterizing the operation of the object, the volume of the sample, and others. The work studies the effect of the control sample volume on the quality of diagnosis, estimated by the number of mispredicted states using the crossvalidation method. The tests were carried out in the Matlab package, ten different training methods were used: logistic regression, support vector method, decision tree bugging, and others. It is shown that the correct choice of the proportion of the control sample can improve the diagnostic quality to 57%. Technical diagnostics, serviceability of the indicator of functioning, machine learning, control sample, crossvalidation.



Sections: Mathematical modeling
Subjects: Mathematical modeling. 
Venera Arifzianovna Alekseeva, Ulyanovsk State Technical University, Candidate of Engineering; graduated from the Faculty of Economics and Mathematics of Ulyanovsk State Technical University; Associate Professor at the Department of Applied Mathematics and Informatics at UlSTU; an author of scientific papers in the field of statistical methods. [email: v.a.alekseeva@bk.ru]V. Alekseeva


The Use of Machine Learning Methods for Binary Classification
The article deals with the problem of objects binary classification. To solve this problem, the use of machine learning methods should be provided. Machine learning is a subsection of artificial intelligence. It is a mathematical discipline using subsections of mathematical statistics, numerical optimization methods, probability theory, and discrete analysis. The goal of machine learning is a partial or complete automation of solutions of complex professional tasks in different fields of human activity such as speech recognition, image recognition, medical diagnostics, diagnostics of technical facilities etc. The use of the following methods for binary classification: decision trees, neural networks, nearestneighbor method, discriminant analysis, Bayesian classifier, a support vector machine, logistic regression, decision trees begging, method of the empirical function and fuzzy inference based on the basis of Sugeno model are proposed. Classification efficiency is assessed with the use of a number of characteristics: meansquare error, ROCcurve, AUC index, etc. In order to improve the accuracy of classes objects prediction, a comparative analysis of the effectiveness of these methods at various cutoffs and a combination of models (socalled aggregate classifier) are offered. Binary classification, machine learning, aggregate classifier, cutoff.



Sections: Mathematical modeling
Subjects: Mathematical modeling, Automated control systems. 
