# Publications

We consider the technology of information processing and the use of neural networks to identify complex gas-air mixtures with a multi-sensor system of the “electronic nose” type equipped with semiconductor gas sensitive sensors. We also present the results of our experimental studies of the recognition of various gas mixtures based on the application of neural networks in the process of processing signals from a multi-sensor system of gas-sensitive sensors.

A radiation-resistant diamond-based detector for registration of fluxes of particles of cosmic radiation with low linear energy transfer is developed and investigated. The device may be used to register gamma radiation of water-moderated, water-cooled nuclear energy reactors. The characteristics of a detector when exposed to beta radiation are determined and modeling of the signals of the device when exposed to beta and gamma radiation is performed. The use of a multi-layer diamond structure makes it possible to increase the signal-to-noise ratio and expand the dynamic range of measurements of the linear energy transfer of cosmic radiation particles.

A mathematical model of an ionizing-radiation monitor based on diamond detectors has been developed. Experimental investigations of the monitor were carried out, and, using their results, the model was verified and optimized. In was demonstrated thet the developed model makes it possible to calculate data of sensor measurements with an accurancy of 10% or better. These results will be used to reconstruct cosmic-radiation spectra according to the output data of the monitor.

The issues of information support based on the use of artifical neural networks for the rapid recognition of odors using devices such as "elecronic nose" are considered. The variants the reducing the test sampl for an artifical neural network are proposed with the aim of increasing the stabilutyof computatijns and the speed of calculations. A method for the rapid recognition of odors in the presence of background odors is proposed.

The article deals with the features of creation of tools for monitoring and neuronet identification of complex gasair mixtures using devices such as 'electronic nose' equipped with semiconductor gas sensitive sensors in the form of matrix are considered. The results of experimental studies on the analysis and recognition of various gas mextures based on the use of artificial neural networks in the proctssing of streaming signals from a gas sensitive matrix.

For the space transport systems with a long uptime, consideration was given to the method of adaptive filtering in the problem of restoring the parameters of cosmic radiation flows from the measurement data. Proposed were a mathematical model and an algorithm for optimization of the nonstationary control systems whose state is measured against the noisy background. The algorithms of parametric optimization were based on a modified Wiener–Hopf equation and sensitivity functions.

The article shows that large artificial neural networks can be used for mutual ordering of a set of multi-dimensional patterns of the same nature (handwritten text, voice, smells, taste). Each neural network must be pre-trained to recognize one of the patterns. As a measure of ordering one can use the entropy of patterns "Strangers" that are input to a neural network trained to recognize only examples of the pattern "familiar". The neural network after training reduces the entropy of the examples of the pattern "Familiar" and increases the entropy of examples of pattern "Stranger." It is shown that the entropy measure of the ordering always has two global minima. The first minimum corresponds to the pattern "Familiar", the second to the inversion of the pattern "Familiar". It is also shown that the Hamming distance between the patterns belonging to two different groups (groups of the two global minima) is always as large as possible.

The mathematical model of ionizing radiation monitor based on diamond detectors has been developed. Experimental studies of the monitor have been carried out. The model of the monitor has been verified and optimized using the results of these studies. The model is shown to provide for the estimation of the output data of the monitor accurate to better than 10%. The results obtained would be used in recovering the cosmic radiation spectra by the monitor output data.

*Due to the complexity and large dimensions of the task of digital system design debugging decomposition by method of modeling as a whole, algebraic models of decomposition methods are proposed, namely, methods of vertical and horizontal structure decomposition, functional decomposition, decomposition based on error types. An algebraic model of the digital systems software is presented. The software is considered as a semi group of operators.*

A neural network approach for processing the output data from a spectrometr with diamond detectors on a spacecraft is discussed. A mathematical apparatus for obtaining differentiable data on fluxes of electrons, protons, and heavy charged particles in 21 energy bands is proposed.

Systems to monitor asteroids and space debris to predict and help prevent space-linked emergency situations are still in their infancy and this article presents an overview of methods, technologies and software used in creating a data analysis system for monitoring potentially dangerous asteroids and man-made space debris. A description of the system structure and its functional components are given. The components discussed allow for automatic operational assessment of potential space-borne threats and a prediction of the aftermath should any such objects collide with Earth

This paper addresses the issue of designing control systems for parallel computing structures. Designing methodology described grounds on Petri nets to model computing systems of different dimensionality. Then a description of the Petri nets models (PN-models) vertex projection procedure, which allows constructing new models with differing structural and dynamical properties, is presented. Afterwards the existence of scale system that enables us to compare different PN-models quantitatively is demonstrated. And a comparison criteria for structural and dynamical properties of PN-models is proposed.

A diamond-based single-element ultraviolet potodetector that may be used in spectrophotometric equipment is developed. The characteristics of the spectral sensitivity of the detector as a function of tha appllied voltage are presented. The capabilities gained from the used of similar devices for systems used in the analysis of the composition of multicomponent mixture are considered.

We demonstrate that classical quadratic forms are not able to solve the problem of recognizing highdimensional images. The "deep" GalushkinHinton neural networks can solve the problem of highdimensional image recognition, but their training has exponential computational complexity. It is technically impossible to train and retrain a "deep" neural network rapidly. For mobile "artificial nose" systems we proposed to employ a number of "wide" neural networks trained in accordance with (GOST R 52633.52011). This standardized learning algorithm has a linear computational complexity, i.e. for each new smell image a time of about 0.3 seconds is sufficient for creating and training a new neural network with 2024 inputs and 256 outputs. This leads to the possibility of the rapid training of the artificial intelligence "artificial nose" and a gradual expansion of its database consisting of 10 000 or more trained artificial neural networks.

This paper contains a description of methods and software tools for creation of the information-analytical system for monitoring hazardous space objects. The paper presents the structure of the system and a description of its functional components thet enable rapid assessment of the NEO hazard and forecast of the effects of dengerous celestial bodies colliding with the Earth. The results of the system's operation regarding the modeling the motion of spact objects are also included in this work.

A mathematical model is developed for a multichannel sensor unit based on diamond detectors in a device for monitoring the parameters of cosmic ray fluxes. The output signals from these sensors are modelled as they detect ionizing radiation from outer space in different spacecraft orbits with various levels of solar activity.