Vladimir Shiroky

Prediction of Relativistic Electrons Flux in the Outer Radiation Belt of the Earth Using Adaptive Methods

Prediction of the time series of relativistic electrons flux in the outer radiation belt of the Earth encounters problems caused by complexity and nonlinearity of the “solar wind—the Earth’s magnetosphere” system. This study considers such prediction by the parameters of solar wind and interplanetary magnetic field and by geomagnetic indexes, using different methods, namely, Artificial Neural Network, Group Method of Data Handling and Projection to Latent Structures (also known as Partial Least Squares). Comparison of quality indexes of predictions with horizon from one to twelve hours among each other and with that of trivial model is presented.

Horizon of Neural Network Prediction of Relativistic Electrons Flux in the Outer Radiation Belt of the Earth

The difficulty of prediction of the time series of relativistic electrons flux in the outer radiation belt of the Earth is caused by the complexity and nonlinearity of the magnetosphere of the Earth as a dynamic system, and by the properties of data obtained from space experiments. This study considers different approaches to neural network prediction of the values of relativistic electrons flux in the outer radiation belt of the Earth by the parameters of solar wind measured at the Earths orbit and by the values of geomagnetic indices. Comparison of quality indices of predictions with horizon from one to twelve hours among each other and with predictions of trivial models is performed.

The influence of solar flares on the near-Earth space radiation environment in March and April 2013: Possibilities of operational monitoring

This paper deals with the operational analysis of the influence of solar flares, which produced solar cosmic rays, on the near-Earth radiation environment in space during the period from March 1 to April 18, 2013.

Single Trial EEG Classification of Tasks with Dominance of Mental and Sensory Attention with Deep Learning Approach

In this paper, we present classification algorithms based on single-trial ElectroEncephaloGraphy (EEG) during the performance of tasks with the dominance of mental and sensory attention. Statistical data analysis showed numerous significant differences of EEG wavelet spectra density during this task at the group level. We decided to use wavelet power spectral density (PSD) computed in each channel for single trial as the source of feature extraction for the classification task. To obtain a low-dimensional representation of PSD image convolutional autoencoder (CNN) was trained. With this encoded representation binary classification for each subject with multilayer perceptron (MLP) were performed. The classification error varies depending on the subject with the average true classification rate is 83.4%, and the standard deviation is 6.6%. So this approach potentially could be used in the tasks where pattern classification is used, such as a clinical decision or in Brain-Computer Interface (BCI) system.

A Neural Network of Multiresolution Wavelet Analysis

Wavelet transformation is a powerful method of signal processing which uses decomposition of the studied signal over a special basis with unique properties, the most important of which are its compactness and multiresolution: wavelet functions are produced from the mother wavelet by transition and dilation. Wavelet neural networks (WNN) are a family of approximation algorithms that use wavelet functions to decompose the approximated function. If only approximation and no inverse transformation is needed, the values of transition and dilation coefficients may be determined during network training, and the windows corresponding to various wavelet functions may overlap, making the whole system much more efficient. Here we present a new type of a WNN – Adaptive Window WNN (AWWNN), in which window positions and wavelet levels are determined with a special iterative procedure. Two modifications of AWWNN are tested against linear model and multi-layer perceptron on Mackey-Glass benchmark prediction problem.