ISSN 1991-2927
 

ACP № 3 (65) 2021

Keyword: "neural networks"

Nikita Aleksandrovich Pchelin, Candidate of Sciences in Engineering; graduated from the Ulyanovsk Higher Military Command School of Communications; completed his postgraduate studies at Ulyanovsk State Technical University; Chief Designer at Federal Research and Production Center Joint-Stock Company ‘Research-and- Production Association ‘Mars’; an author of articles and RF patents in the field of error-correcting coding. e-mail: pna3@yandex.ruN.A. Pchelin

Mohammed A. Y. Damdam, Postgraduate Student of the Department of Telecommunications of Ulyanovsk State Technical University majoring in Informatics and Computer Engineering; graduated from UlSTU; an author of publications in the field of error-correcting coding and information protection. e-mail: dam_love@mail.ruM.A.Y. Damdam

Ali S.A. Al-Mesri, Postgraduate Student of the Department of Telecommunications of Ulyanovsk State Technical University majoring in Informatics and Computer Engineering; graduated from UlSTU; an author of publications in the field of error-correcting coding and information protection. e-mail: ali_almassry@mail.ruA.S.A. Al-Mesri

Aleksandr Aleksandrovich Brynza, Master’s Student at the Department of Telecommunications of Ulyanovsk State Technical University majoring in Infocommunication Technologies and Communication Systems; an author of publications in the field of error-correcting coding and information protection. e-mail: abrynza73@gmail.comA.A. Brynza

The paradigm of neural network decoding of non-binary redundant codes63_8.pdf

The use of noise-tolerant coding in modern communication systems remains the only means of increasing the efficient energy of such systems. This parameter tends to increase in conditions when the receiver of the communication system is able to correct errors of a large multiplicity. At the same time, the existing experience of using various methods for decoding the received data to achieve such a goal in the format of algebraic or iterative procedures does not give a noticeable effect and leads to a large time cost and an exponential increase in the complexity of implementing the decoder processor. The reason for this situation is the passive position of the receiver, which, when processing each code vector, remains a fixator of the picture that occurred in the communication channel and, in general, by compiling a system of linear equations and then solving it, tries to identify the error vector. Some exceptions are permutation decoding systems, which, by selecting and using reliable characters from the number received at the reception, simulate the operation of their transmitter and compare the received (almost error-free) result of such encoding with the received combination [1, 2]. With the growing influence of destructive factors, such methods are ineffective. A natural question arises: are modern solutions in neural network technologies capable of improving the characteristics of code vector recognition systems in order to obtain acceptable machine time costs in order to achieve an increase in the energy characteristics of communication systems.

Neural networks, redundant codes, cluster, pattern recognition.

2021_ 1

Sections: Mathematical modeling

Subjects: Mathematical modeling.



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. e-mail: 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. e-mail: 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 computer-aided design systems, workflow management. e-mail: 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. e-mail: v.tronin@ulstu.ruV.G. Tronin

Software for determining features of a mobile robot’s path with the use of neural networks61_8.pdf

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 pre-trained 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 time-consuming 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.

2020_ 3

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. e-mail: 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 Radio-Engineering 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. e-mail: 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. e-mail: 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 Radio-Engineering 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. e-mail: ssukhov@ulireran.ruS. V. Sukhov

Evaluation of generative approaches to knowledge sharing in neural networks60_8.pdf

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.

2020_ 2

Sections: Artificial intelligence

Subjects: Artificial intelligence.



Vitalii Evgenevich Dementev, Candidate of Sciences in Engineering; graduatedfromthe Faculty of Radioengineering of Ulyanovsk State Technical University; Head of the Department of Telecommunications of UlSTU; an author of articles and patents in the field of statistical processing of multidimensional signals and information protection. e-mail: dve@ulntc.ruV.E. Dementev,

Dmitrii Sergeevich Kondratev, Postgraduate Student at the Department of Telecommunications of UlSTU; graduated from Radioengineering Faculty of UlSTU; an author of articles in the field of artificial intelligence and image processing. e-mail: kondratev.dmitriy@gmail.comD.S. Kondratev,

Anastasiia Sergeevna Streltsova, Postgraduate Student at the Department of Telecommunications of UlSTU; graduated from the Faculty of Mathematics, Informatics, and Aviation Technologies; Software Engineer at FRPC JSC ‘RPA Mars’, an author of articles in the field of thematic mapping the multi-zone satellite imagery as well as in information protection by applying the steganographic methods. e-mail: nastya94strel@mail.ruA.S. Streltsova

The object-trajectory tracking algorithm based on the combination of bayesian neural networks and nonlinear kalman filtration procedures58_6.pdf

The paper aims to develop software and algorithmic decisions allowing to detect automatically and track objects on video sequences obtained from unmanned aerial vehicles. For this purpose, authors studied the most productive neural network algorithms focused on the image segmentation and selection of various objects shown in these images. The article concluded that the advantages of Bayesian neural network procedures are important, which allow combining their operation results with other algorithms. The effectiveness of such combinations is quantified. Special attention is paid to the assessment of the possible object location on subsequent frames, taking into considerations its motion characteristics. To improve the object trajectory tracking, it is proposed to combine the results of Bayesian neural network algorithms and nonlinear Kalman filtering procedures aimed to track the objects moved including with variable acceleration. The article also deals with problems of software implementation of the proposed combination of algorithms. A comparative analysis of the obtained trajectory tracking algorithm with known solutions implemented in public OpenCV libraries is performed. The results of this comparison allow us to conclude that the proposed algorithm has significant advantages, so it opens us the possibility to recommend its application in real image-data processing systems.

Image segmentation, neural networks, nonlinear filter, object detecting, trajectory tracking.

2019_ 4

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.[e-mail: vvk28061955@mail.ru]V. Kozhevnikov

The Method of Mathematical Modeling of Cognitive Digital Automata 56_12.pdf

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 "NOT-AND-OR" 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 "NOT-AND-OR" 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.

2019_ 2

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. [e-mail: 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. [e-mail: 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. [e-mail: dan-kan@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 [e-mail: 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 problem-solving. [e-mail: v.tronin@ulstu.ru]V. Tronin

The Scene Segmentation in the Tasks for Self-driving Vehicle Navigation By Using Neural Network Models With Attention 55_5.pdf

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 self-driving 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 self-driving 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 data-labeling costs significantly.

Artificial intelligence, neural networks, machine learning, computer vision, attention networks.

2019_ 1

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. [e-mail: 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. [e-mail: 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. [e-mail: 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. [e-mail: dan-kan@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. [e-mail: 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. [e-mail: p.vollkkoff@yandex.ru]P. Volkov

Changing the Lighting in the Scenes By Using Neural Networks Cascades 54_7.pdf

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 three-dimensional 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 two-dimensional object and contains only indirect information about three-dimensional 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.

2018_ 4

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. [e-mail: 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. [e-mail: 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. [e-mail: m.shlyamov@simcase.ru]M. Shliamov

Software Package for Visual Monitoring of the Cargo Transportation 53_7.pdf

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 open-top trucks with bulk non-metallic materials (sand, gravel, coal). Currently, the problem of road-freight transport weight-checking 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.

Computer-aided design, artificial intelligence, neural networks, machine learning, computer vision.

2018_ 3

Sections: Information systems

Subjects: Information systems, Artificial intelligence.


Aleksandr Alekseevich Emelianov, Federal Research-and-Production Center Joint Stock Company ‘Research-and-Production Association ‘Mars’, Candidate of Engineering; graduated from F.E. Dzerzhinsky Military Academy; Deputy Chief Engineer for Quality Assurance and Engineering Support - Head of the Management Department of Federal Research-and-Production Center Joint Stock Company ‘Research-and-Production Association ‘Mars’; an author of publications in the field of creation of the quality management and information security systems, statistical analysis of supplier appraisal. [e-mail: mars@mv.ru]A. Emelianov,

Iuliia Aleksandrovna Radionova, Federal Research-and-Production Center Joint Stock Company ‘Research-and-Production Association ‘Mars’, Candidate of Engineering; graduated from the Faculty of Mathematics and Mechanics of Ulyanovsk State University; finished her postgraduate study at Ulyanovsk State Technical University; Lead Software Engineer at FRPC JSC ‘RPA ‘Mars’; an author of articles in the field of automated workflow systems, intelligent technical documentation storages, and statistical analysis of supplier appraisal. [e-mail: julia-owl@mail.ru]I. Radionova,

Aleksandr Leonidovich Savkin, Federal Research-and-Production Center Joint Stock Company ‘Research-and-Production Association ‘Mars’, Candidate of Military Sciences, Associate Professor; graduated from Ulyanovsk Higher Military Command School of Communications, Marshal Budjonny Military Academy of Signal Corps, completed postgraduate studies in the Military Academy of Communications; Head of Science and Engineering Support Department of FRPC JSC ‘RPA ‘Mars’; an author of scientific works, textbooks, and articles in the field of development and modelling of communication control systems and statistical analysis of supplier appraisal. [e-mail: mars@mv.ru]A. Savkin

Information and Analytical Model of Product Testing for Counterfeit 52_4.pdf

The article considers a problem of counterfeit products and the existing methods for solving this problem. An information and analytical method for the optimization of an expert product evaluation is proposed, which uses a parameter set and allows to automate the final decision-making process. The set of parameters takes into account not only indicators for suppliers, but also possible consequences of using counterfeit products for different groups of consumers. The method is based on the application of a neural network using a certain base of expert knowledge. Network training is provided during analyzing process with the possibility of correction by an expert. Modeling of the analysis process with different sets of input parameters and various parameters of the neural network was carried out. The results are stored in the database; the neural network performance is evaluated based on these results. The database structure, input parameters structure and the visualization of experimental results are presented.

Counterfeit, modeling, evaluation parameter, database, software, neural network.

2018_ 2

Sections: Information systems

Subjects: Information systems.


Irina Aleksandrovna Sedykh, Lipetsk State Technical University, Candidate of Physics and Mathematics, graduated from the Faculty of Automatization and Information Technologies of Lipetsk State Technical University (LSTU); Associate Professor at the Department of Mathematics of Lipetsk State Technical University; an author of monographs and articles, holds State Registration Certificates of computer programs in the field of neighborhood modelling of dynamic systems. [e-mail: sedykh-irina@yandex.ru]I. Sedykh,

Dmitrii Sergeevich Demakhin, Lipetsk State Technical University, graduated from the Faculty of Physics and Technology of Lipetsk State Technical University; Candidate for the Master’s Degree at LSTU; an author of articles, holds State Registration Certificates of computer programs in the field of neighborhood modelling of dynamic systems. [e-mail: dima-demahin@mail.ru]D. Demakhin

Flexible Control of Traffic Lights System on the Basis of Neural Networks 000_13.pdf

The traditional algorithm of crossroad traffic management with fixed order and switching-on duration was described in the article. Neural networks-based kind of control for crossroad group of traffic lights was offered as an alternative. The appropriate algorithm has been developed and realized in C++. The basic characteristics of designed neural network for crossroad traffic lights control were described. They include the architecture, assignment of neurons for input and output layers, the number of intermediate layer neurons, the activation function of neurons, the learning method. The realized neural network-based algorithm allows to put in practice flexible control of coordination traffic lights in case of the non-fixed order of traffic lanes priorities. Herewith, the possibility of change in green time of traffic light signal within predetermined limits with the aim of increasing capacity of crossroads in most problematic directions of transport movements was foreseen. Also, restrictions for prevention of blocking segregate streams for too long including blocking pedestrians in crosswalks were provided.

Traffic lights systems, mathematical modelling, neural networks, crossroad traffic management.

2017_ 1

Sections: Artificial intelligence

Subjects: Artificial intelligence.


Vadim Viktorinovich Shishkin, Ulyanovsk State Technical University, Candidate of Engineering, Associate Professor; graduated from the Faculty of Radio-Engineering at Ulyanovsk Polytechnical Institute; Professor at the Measuring-Computing Complexes Department of Ulyanovsk State Technical University, Dean of the Faculty of Information Systems and Technologies at UlSTU, an author of articles in the field of automated design of industrial products and intellectual data analysis. [e-mail: shvv@ulstu.ru]V. Shishkin,

Denis Igorevich Stenyushkin, Ulyanovsk State Technical University, graduated from the Faculty of Information Systems and Technologies at Ulyanovsk State Technical University; Post-Graduate Student at the Measuring-Computing Complexes Department of Ulyanovsk State Technical University, an author of articles in the field of automated design of industrial products and intellectual data analysis. [e-mail: denisstenyushkin@yandex.ru]D. Stenyushkin,

Michael Genrikhovich Bron, R&D in ScanMaster Systems (IRT) Ltd. (Israel), graduated from the Faculty of Radio-Engineering at Ulyanovsk State Technical University; Vice President of R&D in ScanMaster Systems (IRT) Ltd. (Israel); an author of articles in the field of ultrasonic inspection in industry. [e-mail: misha@scanmaster-irt.com]M. Bron

Mathematical Models and Methods for Real-time Analysis of Railway Rails Ultrasonic Defectograms 38_8.pdf

The article describes a system of mathematical models and methods devoted to a real-time analysis of the ultrasonic defectograms of railway rails during a testing process. The system includes models and methods for a preliminary ultrasonic data processing including a data reading, a range adjusting and a data combining for separate channels and also for a defect search and classification. The preliminary data processing is based on the ultrasonic data reading with a signal queue and their further algebraic modifications aiming to make them suitable for a further processing. The defect search and classification is based on an artificial neural network of Simplified Fuzzy ARTMAP architecture that is modified in order to deal with the input array elements with a wide value range. A decision making method based on a decision tree is introduced in order to solve the occurring conflicts. The introduced models and methods can be effectively implemented basing on modern parallel computing approaches. The tests showed out that the defect recognition rate is not less than 85%.

Defectogram analysis, rails defects detection, neural networks.

2014_ 4

Sections: Information systems

Subjects: Information systems, Mathematical modeling.


Ilya Vitalyevich Bondarenko, Military Training and Research Centre Naval Academy named after N. Kuznetsov, Candidate of Engineering; graduated from the Faculty of C2 Systems of the Naval Radio-Electronics Academy named after A. Popov; senior lecturer at the Military Training and Research Centre Naval Academy named after N. Kuznetsov; author of articles in the field of use of neuronet models in military fields. [e-mail: bondarenko@bk.ru]I. Bondarenko,

Sergey Petrovich Navoytsev, FRPC OJSC RPA Mars, Candidate of Engineering, senior staff scientist; laureate of the State Prize of the Russian Federation; graduated from the Faculty of Software of the Naval Radio-Electronics Academy named after A. Popov; specializes in the field of information technologies of special-purpose C2 systems; deputy chief designer at FRPC OJSC RPA Mars. [e-mail: navojcev@yandex.ru]S. Navoytsev,

Vladimir Nikolaevich Naumov, Military Training and Research Centre Naval Academy named after N. Kuznetsov, Doctor of Military Sciences, Professor; honoured worker in the science of the Russian Federation; graduated from the Faculty of Computers of the Naval Radio-Electronics Academy named after A. Popov; Professor at the Military Training and Research Centre 'Naval Academy named after N. Kuznetsov'; author of numerous publications in the field of automation of control processes of ship's facilities and systems. [e-mail: naumov122@list.ru]V. Naumov,

Yury Ivanovich Sineshchuk, Military Training and Research Centre Naval Academy named after N. Kuznetsov, Doctor of Engineering, Professor; honoured worker of the Higher School of the Russian Federation; graduated from the Faculty of Combat-Management Systems of the Naval Radio-Electronics Academy named after A. Popov; Professor at the Military Training and Research Centre Naval Academy named after N. Kuznetsov; specializes in the field of modeling of the features of the operation stability of complex systems. [e-mail: sinegal@rambler.ru]Y. Sineshchuk

Neuronet Model of Situation Categorization 25_5.pdf

The article cites a neuronet model to categorize situations which occur during the repulsion of anti-ship missiles. It also describes the membership functions of input and output variables. The authors brought to light required methods of variable phasing.

Anti-aircraft defense, anti-ship missiles, neural network, situation categorization.

2011_ 3

Sections: Artificial-intelligence systems

Subjects: Artificial intelligence, Automated control systems, Architecture of ship's system.


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