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• Содержание выпуска •
• Software and Hardware for Distributed Systems and Supercomputers •
• Mathematical Modelling •
• Supercomputing Software and Hardware •
• Mathematical Foundations of Programming •
• Methods for Optimal Control and Control Theory •
• Artificial Intelligence, Intelligence Systems, Neural Networks •
Artificial Intelligence, Intelligence Systems, Neural Networks
Responsible for the Section: doctor of technical Sciences Vyacheslav Khachumov.,
candidate of technical Sciences Eugene Kurshev.
On the left: assigned number of the paper, submission date, the number
of A5 pages contained in the paper,
and the reference to the full-text PDF
/r1/pdf.jpg) .
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Article # 32_2016
19
p.
/r2/pdf.jpg)
PDF |
submitted on 06th
Nov 2016 displayed on
website on 26th Dec
2016 Natalia Vlasova,
Alexey Podobryaev
Complex time expressions recognition problem in application to
automatic information extraction from Russian texts
We consider the problem of complex time expressions
recognition in Russian news texts with application to automatic
information extraction. We describe an algorithm for finding noun
phrases that contain time expressions. This algorithm has two parts:
the pre-segmentation and the selection of noun phrase borders inside
the segments via machine learning (CRF-model). We receive results of
experiments. (In Russian).
Key words: information extraction, named entities
recognition, noun phrase chunking, time expressions, CRF. |
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article citation |
http://psta.psiras.ru/read/psta2016_4_177-195.pdf |
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DOI |
https://doi.org/10.25209/2079-3316-2016-7-4-177-195 |
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Article # 33_2016
20
p.
PDF |
submitted on 16th
Nov 2016 displayed on
website on 26th Dec
2016 Elena
Suleymanova
On two types of
time-referring expressions
The paper suggests a view on categorizing text
expressions that are generally referred to by information extraction
community as time-point expressions, or temporal coordinates,. Two
types of expressions are identified which differ in the way they
refer to time. The issues of normalization (i. e. identifying the
absolute value) are addressed for both types of expressions. (In
Russian).
Key words: natural language processing, temporal information
extraction, normalization of context-dependent time expressions. |
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article citation |
http://psta.psiras.ru/read/psta2016_4_209-229.pdf |
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DOI |
https://doi.org/10.25209/2079-3316-2016-7-4-209-229 |
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Article # 35_2016
17
p.
PDF |
submitted on 16th
Nov 2016 displayed on
website on 26th Dec
2016 Natal’ya Lando
TimeML markup
language for Russian. Future outlook
The article discusses the possibility of applying the
TimeML markup language for annotating temporal and event expressions
in Russian. The author reveals some cases specific to Russian that
do not quite fit in the TimeML guidelines, and suggests possible
updates to get around the problem. The conclusion is that an updated
version of TimeML for Russian can serve both as a markup language
and as a storage format for automatically extracted temporal
information. (In Russian).
Key words: natural language processing, information
retrieval, annotation language, time expressions. |
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article citation |
http://psta.psiras.ru/read/psta2016_4_249-265.pdf |
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DOI |
https://doi.org/10.25209/2079-3316-2016-7-4-249-265 |
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Article # 36_2016
20
p.
PDF |
submitted on 16th
Nov 2016 displayed on
website on 26th Dec
2016 Seda Egikian,
Elena Suleymanova
The actuality modality in the framework of the information
extraction for texts written in a natural language.
The article deals with the ”actuality” of the
information extracted from the texts written in a natural language.
The first part of the article is devoted to the basic notions we are
using, such as proposition, modality and the speaker. In the second
part the notion “actuality” is defined by describing its main
components. The third part contains the list of the most important
contexts for the basic case of “actuality”. (In Russian).
Key words: natural language processing, automatics
information extraction, modality, actuality. |
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article citation |
http://psta.psiras.ru/read/psta2016_4_267-286.pdf |
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DOI |
https://doi.org/10.25209/2079-3316-2016-7-4-267-286 |
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Article # 37_2016
17
p.
PDF |
submitted on 26th
Nov 2016 displayed on
website on 28th Dec
2016 Dmitry Stepanov
Parallel program system for HIL experiments on visual navigation of
unmanned aerial vehicles
The article is devoted to the development of a
program system designed to solve the navigation problem for unmanned
aerial vehicles using the methods and algorithms of computer vision,
image processing and analysis. The system operates on a cluster
computer. The sources of data for solving the problem are the visual
navigation are the HIL data — the results of generation of video
sequences from virtual UAV. A subsystem of flight simulation and
video generation is developed. Also the algorithms for solving the
problem of visual navigation of UAV in flight over flat terrain and
terrain with relief are developed. The results of experiments
demonstrating the effectiveness of cluster computer in the problem
of preliminary processing of reference images of terrain for
subsequent visual navigation solutions, as well as in the problem of
parallel processing of multiple independent video sequences coming
from different UAVs are presented. (In Russian).
Key words: visual navigation, program system, cluster
computer, UAV, modeling, GPU, parallel computing. |
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article citation |
http://psta.psiras.ru/read/psta2016_4_287-303.pdf |
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DOI |
https://doi.org/10.25209/2079-3316-2016-7-4-287-303 |
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Article # 38_2016
12
p.
PDF |
submitted on 24th
Nov 2016 displayed on
website on 28th Dec
2016 Egor Ivanov
Data-flow
processing of data from surveillance cameras for objects detection
using distributed data processing system by image segmentation
The paper describes image processing methods that was
realized in the distributed data processing system. Basic processing
method is image segmentation. Convolutional neural network is used
for detection of color and texture information. Results of image
segmentation are applied in background regions detection task. (In
Russian).
Key words: Image segmentation, Data-flow processing, Neural
networks, Surveillance cameras. |
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article citation |
http://psta.psiras.ru/read/psta2016_4_305-316.pdf |
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DOI |
https://doi.org/10.25209/2079-3316-2016-7-4-305-316 |
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Article # 39_2016
13
p.
PDF |
submitted on 26th
Nov 2016 displayed on
website on 28th Dec
2016 Anna Kiryushina
Fire safety
signs detection and classification by applying neural network
The paper describes a method for
detection of fire safety signs, taken from a photo and a video which
are received from cameras standing on board of an unmanned aerial
vehicle or mobile device. An algorithm of fire safety signs
allocation by applying a scanning window is highlighted. Also the
paper gives the results of
convolutional neural network studying and of characters classifying.
(In Russian).
Key words: UAV, mobile vehicle, image scaling, convolutional
neural network, object recognition, scanning window, object
classification. |
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article citation |
http://psta.psiras.ru/read/psta2016_4_317-329.pdf |
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DOI |
https://doi.org/10.25209/2079-3316-2016-7-4-317-329 |
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Article #
40_2016
16
p.
PDF |
submitted on 24th
Nov 2016 displayed on
website on 28th Dec
2016 Aleksandr
Smirnov, Artem Bezzubtsev
Bypass obstacles
mobile technical unit using stereo vision
In the article offered the obstacle avoidance method
in the way of mobile technical unit (MTU) using stereovision
algorithms and distributed data block parallel processing system.
The article also describes the algorithm developed card generating
the test room, considered the use of A* algorithm to calculate the
bypass path, and put forward the concept of creating a real MTU for
testing algorithms. (In Russian).
Key words: mobile technical unit (MTU), depth map,
stereovision, rectification, A*, distributed system, Raspberry Pi. |
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article citation |
http://psta.psiras.ru/read/psta2016_4_331-346.pdf |
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DOI |
https://doi.org/10.25209/2079-3316-2016-7-4-331-346 |
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• Software and Hardware for Distributed Systems and Supercomputers •
• Mathematical Modelling •
• Supercomputing Software and Hardware •
• Mathematical Foundations of Programming •
• Methods for Optimal Control and Control Theory •
• Artificial Intelligence, Intelligence Systems, Neural Networks •
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