Труды сотрудников ИВМ СО РАН

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Найдено документов в текущей БД: 10
   В19
   К43

    Об идентификации многозначных характеристик в задачах стохастического моделирования
[Текст] : статья / Е.С. Кирик // Труды конференции посвященной 90-летию со дня рождения А.А. Ляпунова. - Новосибирск, 2001
ГРНТИ

Аннотация: The paper deals with the problem of identification and restoration of ambiguous dependencies in the tasks of stochastic modelling. Nonparametrical approach is proposed. With some restriction on the learning sample decision of the task is described. Some ideas is presented for the common case of the absence of any addition information about view of function of restoration.

http://icm.krasn.ru/refextra.php?id=2133,
Полный текст

Держатели документа:
ИВМ СО РАН : 660036, Красноярск, Академгородок, 50, стр.44

Доп.точки доступа:
Kirik E.S.
   B18
   H22

    Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods and Techniques
[Текст] / ed. by E.S. Olivas, J.D.M. Guererro, M.M. Sober, J.R.M. Benedito, A.J.S. Lopes. - [Б. м.] : Information Science Reference, 2009. - 852 с. : il. - ISBN 978-1-60566–766-9 : Б. ц.
    Содержание:
Gorban, A. N. Principal Graphs and Manifolds / A. N. Gorban, A. Y. Zinovyev


Свободных экз. нет

    Nonparametric Pattern Recognition Systems in the Conditions of Large Learning Samples
[Текст] : статья / А.В. Лапко и др. // Pattern Recognition and Image Analysis. (Advances in Mathematical Theory and Applications). - 2010. - Т.20, №.2. - С. 129-136



Доп.точки доступа:
Lapko, V.A.; Лапко, Василий Александрович; Лапко, Александр Васильевич

    Constructing the integral OLAP-model for scientific activities based on FCA
/ T. Penkova, A. Korobko // Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - 2013. - Vol. 7828 LNAI: 16th International Conference on Knowledge Engineering, Machine Learning and Lattice Computing with Applications, KES 2012 (10 September 2012 through 12 September 2012, San Sebastian. - P163-170, DOI 10.1007/978-3-642-37343-5_17 . -
Аннотация: In this paper an original approach to analytical decision making support based on on-line analytical processing of multidimensional data is suggested. According to Dr. Codd's rules, the effectiveness of data analysis significantly depends on the data accessibility and transparency of an analytical model of domain. The method of constructing a conceptual OLAP-model as an integral analytical model of the domain is proposed. The method is illustrated by the example of the scientific activities domain. The integral analytical model includes all possible combinations of analyzed objects and gives them the opportunity to be manipulated ad-hoc. The suggested method consists in a formal concept analysis of measures and dimensions based on an expert knowledge about the structure of analyzing objects and their comparability. As a result, conceptual OLAP-model is represented as a concept lattice of multidimensional cubes. Concept lattice features allow the decision maker to discover the nonstandard analytical dependencies on the set of all actual measures and dimensions of the scientific activities domain. Conceptual OLAP-model implementation allows user makes better decisions based on on-line analytical processing of the scientific activity indicators. В© 2013 Springer-Verlag.

Scopus


Доп.точки доступа:
Penkova, T.; Пенькова, Татьяна Геннадьевна; Korobko, A.; Коробко, Анна Владимировна

    Hybrid systems of pattern recognition
[Text] : статья / A. V. Lapko, V. A. Lapko // Pattern Recognition and Image Analysis. - 2008. - Vol. 18. - pp. 7-13DOI 10.1134/S1054661808010021 . -

Аннотация: Hybrid systems of pattern recognition are proposed which ensure efficient use of a priori data about the form of decision functions and information of learning samples. The results obtained are generalized for processing heterogeneous data.

Полный текст на сайте издательства


Доп.точки доступа:
Lapko, V.A.; Лапко, Василий Александрович; Лапко, Александр Васильевич

    Construction of confidence boundaries for the decision function in a two-alternative problem of pattern recognition
/ A. V. Lapko, V. A. Lapko // Optoelectron. Instrum. Data Proces. - 2015. - Vol. 51, Is. 4. - P372-377, DOI 10.3103/S875669901504007X . - ISSN 8756-6990
Аннотация: A nonparametric estimate of the decision function in a two-alternative problem of pattern recognition is considered. The principle of expansion of the learning sample and the analysis of the probabilistic characteristics of the obtained sets of random variables are used to synthesize this estimate. On this basis, a technique of construction of confidence boundaries for the Bayesian equation of the separating surface is derived. The efficiency of this technique is proved by results of computational experiments. © 2015, Allerton Press, Inc.

Scopus

Держатели документа:
Institute of Computational Modeling, Siberian Branch, Russian Academy of Sciences, Akademgorodok 50, building 44, Krasnoyarsk, Russian Federation
Reshetnev Siberian State Aerospace University, pr. im. Gazety “Krasnoyarskii rabochii” 31, Krasnoyarsk, Russian Federation

Доп.точки доступа:
Lapko, V. A.; Лапко, Василий Александрович; Лапко, Александр Васильевич
519.7
П 63

    Построение доверительных границ для решающей функции в двуальтернативной задаче распознавания образов
[Текст] : статья / А. В. Лапко, В. А. Лапко // Автометрия. - 2015. - Т. 51, № 4. - С. 62-67 . - ISSN 0320-7102
   Перевод заглавия: CONSTRUCTION OF CONFIDENCE BOUNDARIES FOR THE DECISION FUNCTION IN A TWO-ALTERNATIVE PROBLEM OF PATTERN RECOGNITION
УДК

Аннотация: Рассматривается непараметрическая оценка решающей функции в двуальтернативной задаче распознавания образов. При её синтезе используются принцип декомпозиции обучающей выборки и анализ вероятностных характеристик получаемых множеств случайных величин. На этой основе разработана методика построения доверительных границ для байесовского уравнения разделяющей поверхности. Эффективность методики подтверждается результатами вычислительных экспериментов.
A nonparametric estimate of the decision function in a two-alternative problem of pattern recognition is considered. The principle of expansion of the learning sample and the analysis of the probabilistic characteristics of the obtained sets of random variables are used to synthesize this estimate. On this basis, a technique of construction of confidence boundaries for the Bayesian equation of the separating surface is derived. The efficiency of this technique is proved by results of computational experiments.

РИНЦ

Держатели документа:
Институт вычислительного моделирования СО РАН
Сибирский государственный аэрокосмический университет им. академика М. Ф. Решетнёва

Доп.точки доступа:
Лапко, Василий Александрович; Lapko V.A.; Lapko A.V.

    Multidimensional Free Interpolation Framework for High-precision Modeling of Slant Total Electron Contents in Mid-latitude and Equatorial Regions
/ S. P. Tsarev, V. V. Denisenko, M. M. Valikhanov // J. Sib. Fed. Univ.-Math. Phys. - 2018. - Vol. 11, Is. 6. - P781-791, DOI 10.17516/1997-1397-2018-11-6-781-791. - Cited References:10. - S. P. Tsarev was supported by the grant from the Ministry of Education and Science of the Russian Federation no. 1.8591.2017/6.7. . - ISSN 1997-1397. - ISSN 2313-6022
РУБ Mathematics

Кл.слова (ненормированные):
ionosphere -- total electron content -- GLONASS -- GPS -- interpolation -- machine -- learning

Аннотация: Standard models of ionospheric delays have errors of order 1-8 TECU (standard total electron content units). On the basis of the free interpolation framework we propose a new simple model of the slant TEC distributions approximating slant TEC distributions obtained from the three-dimensional ionospheric models NeQuick2 and IRI-2016 with RMS error < 0.05 TECU. The proposed model was tested for varios positions of receivers in mid-latitude and equatorial regions. Stability of the coefficients of the model with respect to the position of the receiver and time is substantiated.

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РИНЦ,
Scopus

Держатели документа:
Siberian Fed Univ, Inst Space & Informat Technol, Svobodny 79, Krasnoyarsk 660041, Russia.
Inst Computat Modeling SB RAS, Academgorodok 50, Krasnoyarsk 660036, Russia.
Siberian Fed Univ, Inst Math & Comp Sci, Svobodny 79, Krasnoyarsk 660041, Russia.
Siberian Fed Univ, Inst Engn Phys & Radioelect, Svobodny 79, Krasnoyarsk 660041, Russia.

Доп.точки доступа:
Tsarev, Sergey P.; Denisenko, Valery V.; Valikhanov, Marat M.; Ministry of Education and Science of the Russian Federation [1.8591.2017/6.7]
527.62; 551.510.535; 621.37
В 93

    Высокоточная модель ионосферной задержки сигналов ГНСС на основе многомерной свободной интерполяции
[Текст] : статья / М. М. Валиханов, В. В. Денисенко, С. П. Царев // Успехи современной радиоэлектроники. - 2018. - № 12. - С. 90-94, DOI 10.18127/j20700784-201812-18 . - ISSN 2070-0784
   Перевод заглавия: Precise modeling of slant total electron contents with multidimensional free interpolation
УДК

Аннотация: Предложена новая интерполяционная модель ионосферных задержек, способная обеспечить точность определения наклонных ПЭС 0,02 TECU относительно соответствующих наклонных ПЭС, вычисленных с помощью современных трехмерных моделей ионосферы.
Standard models of ionospheric delays widely used for practical processing of navigation signals from global navigation satellite systems (GNSS) usually approximate the complicated three-dimensional electron density distribution in the Earth's ionosphere and plasmasphere with one- or two-layer distributions and have errors of order 1-8 TECU (total electron content units). Publicly available modern three-dimensional ionospheric models IRI-2016 and NeQuick2 are very complex, they are not easy to use in GNSS applications and have similar large deviations from real ionospheric data. We propose a new interpolation model of ionospheric delays which is much more precise than the one- or two-layer models but simple enough to be used in radiophysics and GNSS applications. It is based on our recent free interpolation framework used previously in finding positions of GNSS satellites from SP3 data with very high precision. The free interpolation framework is not limited to polynomial, trigonometric or spherical interpolating functions and uses a simple machine learning approach for definition of the interpolation coefficients. Our model approximates the slant TEC angular distributions obtained from modern three-dimensional ionospheric models with RMS error 0,02 TECU. The new model of ionospheric delays is universal, stable and have weak dependence on the position of the GNSS ground receiver. Experimental verification of our model was performed for two IGS stations: АМС4 (B=38,80312°, L= -104,524594°, H=1912,5 м) and PIE1 (B=34,301505°, L= -108,118927°, H = 2347,7 м) for STEC distributions calculated using both ionospheric models IRI-2016 and NeQuick2 for the years 2008 and 2017. We show that interpolation coefficients may be chosen equal for both stations and both years. Investigating deeper one may observe a small seasonal variations of the residuals of our model: RMS = 0,03 TECU for winter months and RMS = 0,05 TECU for summer months for the smallest number of parameters in our model N=7. For N=10 parameters one obtains RMS < 0,02 TECU. Further research is planned to test the free interpolation ionospheric STEC model on real measurements from GNSS stations. Integration of our model and methods for differential code biases (DCBs in satellite transmitters and ground receivers) separation is obviously necessary for this test. Possible inclusion of higher order ionospheric effects is under study.

РИНЦ

Держатели документа:
Институт вычислительного моделирования СО РАН
ФГАОУ ВО «Сибирский федеральный университет»

Доп.точки доступа:
Валиханов, М.М.; Valikhanov M.M.; Денисенко, В.В.; Denisenko V.V.; Царев, С.П.; Tsarev S.P.
004.932
В 94

    ВЫЧИСЛИТЕЛЬНАЯ МЕТОДИКА ОБРАБОТКИ МЕДИЦИНСКИХ ИЗОБРАЖЕНИЙ, ИСПОЛЬЗУЯ ВЕЙВЛЕТ И НЕЙРОСЕТИ
[Текст] : статья / Юсиф Ахмед Хамад [и др.] // Медицина и высокие технологии. - 2018. - № 3. - С. 5-13 . - ISSN 2306-3645
   Перевод заглавия: COMPUTATIONAL PROCESSING TECHNIQUE MEDICAL IMAGES USING WAVELET AND NEURAL NETWORKS
УДК

Аннотация: В статье представлен подход к диагностике опухоли молочной железы - вычислительная методика поэтапной классификации с использованием искусственной нейронной сети (машинное обучение) и выявление опухоли молочной железы для медицинской визуализации с помощью методов пороговой сегментации и метода нечеткой кластеризации С-средних.
This paper presents an innovative approach to the diagnosis of breast tumor - a computational methodology for stage classification using artificial neural network (learning machine) and to detect Breast Tumor through thresholding and fuzzy C-means clustering methods for medical imaging application.

РИНЦ

Держатели документа:
Институт вычислительного моделирования СО РАН
Институт космических и информационных технологий Сибирского федерального университета

Доп.точки доступа:
Хамад, Юсиф Ахмед; Hamad Yousif Ahmed; Кириллова, Светлана Владимировна; Kirillova Svetlana Vladimirovna; Курако, Михаил Александрович; Kurako Mikhail Aleksandrovich; Симонов, Константин Васильевич; Simonov Konstantin Vasilyevich