Персона: Ровнягин, Михаил Михайлович
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Институт интеллектуальных кибернетических систем
Цель ИИКС и стратегия развития - это подготовка кадров, способных противостоять современным угрозам и вызовам, обладающих знаниями и компетенциями в области кибернетики, информационной и финансовой безопасности для решения задач разработки базового программного обеспечения, повышения защищенности критически важных информационных систем и противодействия отмыванию денег, полученных преступным путем, и финансированию терроризма.
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Теперь показываю 1 - 4 из 4
- ПубликацияТолько метаданныеDeep learning approach for QRS wave detection in ECG monitoring(2019) Mitrokhin, M.; Kuzmin, A.; Mitrokhina, N.; Zakharov, S.; Rovnyagin, M.; Ровнягин, Михаил Михайлович© 2017 IEEE. Paper describes an approach of deep learning for QRS wave detection for using in mobile heart monitoring systems. Authors analyze a deep learning approach and its advantages in the field of feature extraction and detection, and deep network architecture. Two different variants of deep network are proposed. ECG data processing scheme that includes a neural network is described. It presumes preprocessing, filtering, windowing of ECG signal, buffering, QRS wave detection and analysis. Network training process is mathematically founded. Two variants of neural network are experimentally tested. Training sets and test sets are obtained from free ECG data bank PhysioN et.org. Experimental results show that network with decreasing number of neurons in hidden layers has a better generalization capability. Next steps of research will include experiments with training set size and determining of its' influence on the quality of detection.
- ПубликацияТолько метаданныеModeling NoSQL systems in many-nodes hybrid environments(2019) Kuzmin, A. V.; Rovnyagin, M. M.; Chernilin, F. N.; Guminskaia, A. V.; Kinash, V. M.; Myltsyn, O. V.; Orlov, A. P.; Ровнягин, Михаил Михайлович© 2017 IEEE. Data search is one of the most important problems in the field of computer science and computer facilities. Classical relational DBMSs (RDBMSs), unfortunately, are not suitable as data storage systems for Big Data. Therefore, the concept NoSQL is now widely spread. A common feature of such systems is a high throughput and linear scalability, depending on the number of storage servers used. One of the most productive NoSQL-systems, at the moment is Apache Cassandra. In this paper, we suggest ways to simulate the performance of such systems in hybrid computing environments.
- ПубликацияОткрытый доступМетоды и средства решения задач поиска и защищенного хранения данных с применением гибридных вычислительных технологий(НИЯУ МИФИ, 2015) Ровнягин, М. М.; Ровнягин, Михаил Михайлович; Васильев, Н. П.
- ПубликацияТолько метаданныеCloud computing architecture for high-volume ML-based solutions(2019) Rovnyagin, M. M.; Kirill, Timofeev, V.; Elenkin, A. A.; Shipugin, V. А.; Ровнягин, Михаил Михайлович© 2019 IEEE A large number of modern projects use machine learning technology to perform a variety of business calculations. There are two main ways to integrate machine-learning models into the logic of industrial applications. The first way is to rewrite models from the data analysis language (for example R or Python) to the industrial development language (for example Java, Go or Scala). The second way is to equip models with a web-interface and integrate it into the calculation. In this article, we explore the second method. A deployment architecture for machine learning in the clouds is proposed. The possibilities of the proposed scheme for scaling are described. Examples of practical use of the proposed architecture for organizing data storage with compression are also given.