Chief Researcher, Federal State Budget Research Institution Institute of Cosmophysical Research and Radio Wave Propagation, Far Eastern Branch of the Russian Academy of Sciences (IKIR FEB RAS), Russia
Oksana V. Mandrikova was born in 1972. She graduated from Shevchenko Kyiv National University in 1995 and was awarded the title of Doctor of Technical Science in 2009. Currently, she serves as the Chief Researcher and Head of the Laboratory of System Analysis at the Institute of Cosmophysical Research and Radio Wave Propagation, Far Eastern Branch of the Russian Academy of Sciences. Additionally, she is a Professor at the Control System Department of Kamchatka State Technical University. Her scientific interests encompass intelligent techniques for geophysical data analysis, wavelets, neural networks, the ionosphere, the magnetosphere, and signal anomalies. She has authored over 150 publications, including books and papers. 📚
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- Hybrid Neural Network Approaches
- Developed hybrid neural network approaches to detect anomalies in complex natural data structures.
- Publications:
- Cited by: Article on advancements in geophysical anomaly detection.
- Generalized Multicomponent Model (GCCM)
- Proposed a generalized multicomponent model (GCCM) of the time series of ionospheric parameters for space weather applications.
- Publications:
- Cited by: Article on space weather forecasting models.
- Hybrid Model for Non-Stationary Time Series
- Proposed a hybrid model for non-stationary time series to detect ionospheric irregularities.
- Publication:
- Cited by: Article on ionospheric disturbance detection techniques.
- Nonlinear Approximating Scheme
- Developed a method for constructing a nonlinear approximating scheme in an orthonormal wavelet basis for pattern recognition problems.
- Publication:
- Cited by: Article on wavelet-based pattern recognition methodologies.
- Neural Network Methods for Galactic Cosmic Rays
- Developed neural network methods for detecting sporadic effects in the flow of galactic cosmic rays.
- Publications:
- Cited by: Article on cosmic ray detection using neural networks.
- Geomagnetic Disturbance Detection
- Developed a method for detecting geomagnetic disturbances using data from ground-based geomagnetic stations.
- Publications: