ISSN 1991-2927
 

ACP № 2 (60) 2020

Author: "Alexander Vyacheslavovich Mikheev"

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.


Vadim Viktorinovich Shishkin, Ulyanovsk State Technical University, Candidate of Engineering, Associate Professor; graduated from the Faculty of Radio-Engineering at Ulyanovsk State 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,

Alexander Vyacheslavovich Mikheev, 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 measuring-computing system design and intellectual data analysis. [e-mail: sr.alex.anderson@gmail.com]A. Mikheev

Automated Design of Artificial Neural Network Based Classifiers 37_14.pdf

The article describes a system of models and methods for automated design of neural network-based classifiers. The system bases on the closed design cycle with feedback in form of analysis of gathered experience. The system’s goal is decrease of design process’ time costs. Parameters space is utilized as a formal system for experience representation. Parameters space is a space of points that represent sets of classifiers’ parameters. The space is constructed from parameters, which values are being specified through whole classifier’s life cycle from specification up to operation. This formalism allows to formulate the experience and analysis operations performed over it in terms of sets and operations over them. This results into representation improvement and automation costs decrease. Experience analysis bases on comparison of parameters space points’ projections on dedicated directions. It allows to detect classifier design solutions that are convenient to reuse. These solutions include classifier architectures, architectural parameters and neuron input weights. Using the detected parameters values as initial values in a new classifier’s training and setting up process lead to time costs decrease of 15%.

Artificial neural network-based classifiers, classifiers design, parameters space, closed design cycle.

2014_ 3

Sections: Computer-aided engineering

Subjects: Computer-aided engineering, Artificial intelligence.


© FRPC JSC 'RPA 'Mars', 2009-2018 The web-site runs on Joomla!