ISSN 1991-2927
 

ACP № 2 (56) 2019

Author: "Vadim Georgievich Tronin"

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.



Vadim Georgievich Tronin, Ulyanovsk State Technical University, Candidate of Engineering; Head of the R&D Department at Ulyanovsk State Technical University; Associate Professor at the Department of Information Systems of Ulyanovsk State Technical University; interested in the field of scientometrics, modeling networks at the application layer, and effective management techniques. [e-mail: v.tronin@ulstu.ru]V. Tronin,

Valeria Sergeevna Avvakumova, Federal Research-and-Production Center Joint Stock Company ‘Research and Production Association ‘Mars’, Candidate for the Master’s Degree; graduated from the Faculty of Information Systems and Technologies of Ulyanovsk State Technical University and got the Bachelor’s Degree in Applied Informatics; graduated from the Humanitarian Faculty of Ulyanovsk State Technical University in the specialty of translation; Specialist of the Military-and-Technical Policy Department of Federal Research-and-Production Center Joint Stock Company ‘Research and Production Association ‘Mars’; interested in the field of computational linguistics, CRM-systems, and content localization. [e-mail: valeria.avvakumova73@gmail.com]V. Avvakumova,

Irina Nikolaevna Sheianova, Ulyanovsk State Technical University, Candidate for the Master’s Degree; graduated from the Faculty of Information Systems and Technologies of Ulyanovsk State Technical University and got the Bachelor’s Degree in Applied Informatics; Technical Support Engineer at Ecwid; interested in the field of Data Mining, classification and forecasting, researching and developing decision support systems. [e-mail: irene.sheyanova@gmail.com]I. Sheianova

One-criterion Optimization of a Research Submarine Crew Scheduling 000_12.pdf

The article considers the main mathematical models and algorithms using in scheduling a research submarine crew (Rsc) work. The authors review present decision methods for the task of optimal planning and scheduling. By the example of research submarine roads, system analysis and initial data formation were hold. The mathematical formulation of the Rsc scheduling problem in case of one-criterion optimization is described. The authors use the methods of integer optimization, system analysis, decision principle, scheduling theory, simulation modeling, expert analysis. As an experiment, the authors propose to use the modified genetic algorithm in order to use it as the main mathematical tool of the Rsc work planning system. Also the authors conduct researches on the given genetic algorithm effectiveness. The article derives attention of specialists working at large military and research enterprises involved in planing activities of crews of such complex objects as an aircraft, a spaceship, a submarine, or a deep-water diving suite.

Research submarine, genetic algorithms, scheduling theory, crossover, greedy algorithm.

2016_ 3

Sections: Information systems

Subjects: Information systems, Automated control systems, Mathematical modeling, Architecture of ship's system.


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