ISSN 1991-2927
 

ACP № 2 (56) 2019

Keyword: "neural networks"

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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