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It is known that the sound speed in medium that contain highly compressible inclusions, e.g. air pores in an elastic medium or gas bubbles in the liquid may be significantly reduced compared to a homogeneous medium. Effective nonlinear parameter of medium, describing the manifestation of nonlinear effects, increases hundreds and thousands of times because of the large differences in the compressibility of the inclusions and the medium. Spatial change in the concentration of such inclusions leads to the variable local sound speed, which in turn calls the spatial-temporal redistribution of acoustic energy in the wave and the distortion of its temporal profiles and cross-section structure of bounded beams. In particular, focal areas can form. Under certain conditions, the sound channel is formed that provides waveguide propagation of acoustic signals in the medium with similar inclusions. Thus, it is possible to control spatial-temporal structure of acoustic waves with the introduction of highly compressible inclusions with a given spatial distribution and concentration. The aim of this work is to study the propagation of acoustic waves in a rubberlike material with non-uniform spatial air cavities. The main objective is the development of an adequate theory of such structurally inhomogeneous media, theory of propagation of nonlinear acoustic waves and beams in these media, the calculation of the acoustic fields and identify the communication parameters of the medium and inclusions with characteristics of propagating waves. In the work the evolutionary self-consistent equation with integro-differential term is obtained describing in the low-frequency approximation propagation of intense acoustic beams in a medium with highly compressible cavities. In this equation the secondary acoustic field is taken into account caused by the dynamics of the cavities oscillations. The method is developed to obtain exact analytical solutions for nonlinear acoustic field of the beam on its axis and to calculate the field in the focal areas. The obtained results are applied to theoretical modeling of a material with non-uniform distribution of strongly compressible inclusions.

The paper considers the problem of estimating the parameters of time series described by regression models with Markov switching of two regimes at random instants of time with independent Gaussian noise. For the solution, we propose a variant of the EM algorithm based on the iterative procedure, during which an estimation of the regression parameters is performed for a given sequence of regime switching and an evaluation of the switching sequence for the given parameters of the regression models. In contrast to the well-known methods of estimating regression parameters in the models with Markov switching, which are based on the calculation of a posteriori probabilities of discrete states of the switching sequence, in the paper the estimates are calculated of the switching sequence, which are optimal by the criterion of the maximum of a posteriori probability. As a result, the proposed algorithm turns out to be simpler and requires less calculations. Computer modeling allows to reveal the factors influencing accuracy of estimation. Such factors include the number of observations, the number of unknown regression parameters, the degree of their difference in different modes of operation, and the signal-to-noise ratio which is associated with the coefficient of determination in regression models. The proposed algorithm is applied to the problem of estimating parameters in regression models for the rate of daily return of the RTS index, depending on the returns of the S&P 500 index and Gazprom shares for the period from 2013 to 2018. Comparison of the estimates of the parameters found using the proposed algorithm is carried out with the estimates that are formed using the EViews econometric package and with estimates of the ordinary least squares method without taking into account regimes switching. The account of regimes switching allows to receive more exact representation about structure of a statistical dependence of investigated variables. In switching models, the increase in the signal-to-noise ratio leads to the fact that the differences in the estimates produced by the proposed algorithm and using the EViews program are reduced.

A simplified implicit Euler method was analyzed as an alternative to the explicit Euler method, which is a commonly used method in numerical modeling in electrophysiology. The majority of electrophysiological models are quite stiff, since the dynamics they describe includes a wide spectrum of time scales: a fast depolarization, that lasts milliseconds, precedes a considerably slow repolarization, with both being the fractions of the action potential observed in excitable cells. In this work we estimate stiffness by a formula that does not require calculation of eigenvalues of the Jacobian matrix of the studied ODEs. The efficiency of the numerical methods was compared on the case of typical representatives of detailed and conceptual type models of excitable cells: Hodgkin–Huxley model of a neuron and Aliev–Panfilov model of a cardiomyocyte. The comparison of the efficiency of the numerical methods was carried out via norms that were widely used in biomedical applications. The stiffness ratio’s impact on the speedup of simplified implicit method was studied: a real gain in speed was obtained for the Hodgkin–Huxley model. The benefits of the usage of simple and high-order methods for electrophysiological models are discussed along with the discussion of one method’s stability issues. The reasons for using simplified instead of high-order methods during practical simulations were discussed in the corresponding section. We calculated higher order derivatives of the solutions of Hodgkin-Huxley model with various stiffness ratios; their maximum absolute values appeared to be quite large. A numerical method’s approximation constant’s formula contains the latter and hence ruins the effect of the other term (a small factor which depends on the order of approximation). This leads to the large value of global error. We committed a qualitative stability analysis of the explicit Euler method and were able to estimate the model’s parameters influence on the border of the region of absolute stability. The latter is used when setting the value of the timestep for simulations a priori.

Industrial imaging or “machine vision” is currently a key technology in many industries as it can be used to optimize various processes. The purpose of this work is to create a software and hardware complex for measuring the overall and weight characteristics of cargo based on an intelligent system using neural network identification methods that allow one to overcome the technological limitations of similar complexes implemented on ultrasonic and infrared measuring sensors. The complex to be developed will measure cargo without restrictions on the volume and weight characteristics of cargo to be tariffed and sorted within the framework of the warehouse complexes. The system will include an intelligent computer program that determines the volume and weight characteristics of cargo using the machine vision technology and an experimental sample of the stand for measuring the volume and weight of cargo.

We analyzed the solutions to similar problems. We noted that the disadvantages of the studied methods are very high requirements for the location of the camera, as well as the need for manual operations when calculating the dimensions, which cannot be automated without significant modifications. In the course of the work, we investigated various methods of object recognition in images to carry out subject filtering by the presence of cargo and measure its overall dimensions. We obtained satisfactory results when using cameras that combine both an optical method of image capture and infrared sensors. As a result of the work, we developed a computer program allowing one to capture a continuous stream from Intel RealSense video cameras with subsequent extraction of a three-dimensional object from the designated area and to calculate the overall dimensions of the object. At this stage, we analyzed computer vision techniques; developed an algorithm to implement the task of automatic measurement of goods using special cameras and the software allowing one to obtain the overall dimensions of objects in automatic mode.

Upon completion of the work, this development can be used as a ready-made solution for transport companies, logistics centers, warehouses of large industrial and commercial enterprises.

Social media is a crucial indicator of the position of assets in the financial market. The paper describes the rigid solution for the classification problem to determine the influence of social media activity on financial market movements. Reputable crypto traders influencers are selected. Twitter posts packages are used as data. The methods of text, which are characterized by the numerous use of slang words and abbreviations, and preprocessing consist in lemmatization of Stanza and the use of regular expressions. A word is considered as an element of a vector of a data unit in the course of solving the problem of binary classification. The best markup parameters for processing Binance candles are searched for. Methods of feature selection, which is necessary for a precise description of text data and the subsequent process of establishing dependence, are represented by machine learning and statistical analysis. First, the feature selection is used based on the information criterion. This approach is implemented in a random forest model and is relevant for the task of feature selection for splitting nodes in a decision tree. The second one is based on the rigid compilation of a binary vector during a rough check of the presence or absence of a word in the package and counting the sum of the elements of this vector. Then a decision is made depending on the superiority of this sum over the threshold value that is predetermined previously by analyzing the frequency distribution of mentions of the word. The algorithm used to solve the problem was named benchmark and analyzed as a tool. Similar algorithms are often used in automated trading strategies. In the course of the study, observations of the influence of frequently occurring words, which are used as a basis of dimension 2 and 3 in vectorization, are described as well.

The work is devoted to the study of subgradient methods with different variations of the Polyak stepsize for minimization functions from the class of weakly convex and relatively weakly convex functions that have the corresponding analogue of a sharp minimum. It turns out that, under certain assumptions about the starting point, such an approach can make it possible to justify the convergence of the subgradient method with the speed of a geometric progression. For the subgradient method with the Polyak stepsize, a refined estimate for the rate of convergence is proved for minimization problems for weakly convex functions with a sharp minimum. The feature of this estimate is an additional consideration of the decrease of the distance from the current point of the method to the set of solutions with the increase in the number of iterations. The results of numerical experiments for the phase reconstruction problem (which is weakly convex and has a sharp minimum) are presented, demonstrating the effectiveness of the proposed approach to estimating the rate of convergence compared to the known one. Next, we propose a variation of the subgradient method with switching over productive and non-productive steps for weakly convex problems with inequality constraints and obtain the corresponding analog of the result on convergence with the rate of geometric progression. For the subgradient method with the corresponding variation of the Polyak stepsize on the class of relatively Lipschitz and relatively weakly convex functions with a relative analogue of a sharp minimum, it was obtained conditions that guarantee the convergence of such a subgradient method at the rate of a geometric progression. Finally, a theoretical result is obtained that describes the influence of the error of the information about the (sub)gradient available by the subgradient method and the objective function on the estimation of the quality of the obtained approximate solution. It is proved that for a sufficiently small error $\delta > 0$, one can guarantee that the accuracy of the solution is comparable to $\delta$.

Road noise is one of the key issues in maintaining high environmental standards. At speeds between 50 and 120 km/h, tires are the main source of noise generated by a moving vehicle. It is well known that either the interaction between the tire tread and the road surface or some internal dynamic effects are responsible for tire noise and vibration. This paper discusses the application of a new method for modelling the generation and propagation of sound during tire motion, based on the application of the so-called acoustic-vortex decomposition. Currently, the application of the Lighthill equation and the aeroacoustics analogy are the main approaches used to model tire noise. The aeroacoustics analogy, in solving the problem of separating acoustic and vortex (pseudo-sound) modes of vibration, is not a mathematically rigorous formulation for deriving the source (righthand side) of the acoustic wave equation. In the development of the acoustic-vortex decomposition method, a mathematically rigorous transformation of the equations of motion of a compressible medium is performed to obtain an inhomogeneous wave equation with respect to static enthalpy pulsations with a source term that de-pends on the velocity field of the vortex mode. In this case, the near-field pressure fluctuations are the sum of acoustic fluctuations and pseudo-sound. Thus, the acoustic-vortex decomposition method allows to adequately modeling the acoustic field and the dynamic loads that generate tire vibration, providing a complete solution to the problem of modelling tire noise, which is the result of its turbulent flow with the generation of vortex sound, as well as the dynamic loads and noise emission due to tire vibration. The method is first implemented and test-ed in the FlowVision software package. The results obtained with FlowVision are compared with those obtained with the LMS Virtual.Lab Acoustics package and a number of differences in the acoustic field are highlighted.

The numerical methods developed by the author recently for calculating the molecular system based on the direct solution of the Schrodinger equation by the Monte Carlo method have shown a huge uncertainty in the choice of solutions. On the one hand, it turned out to be possible to build many new solutions; on the other hand, the problem of their connection with reality has become sharply aggravated. In ab initio quantum mechanical calculations, the problem of choosing solutions is not so acute after the transition to the classical format of describing a molecular system in terms of potential energy, the method of molecular dynamics, etc. In this paper, we investigate the problem of choosing solutions in the classical format of describing a molecular system without taking into account quantum mechanical prerequisites. As it turned out, the problem of choosing solutions in the classical format of describing a molecular system is reduced to a specific marking of the configuration space in the form of a set of stationary points and reconstruction of the corresponding potential energy function. In this formulation, the solution of the choice problem is reduced to two possible physical and mathematical problems: to find all its stationary points for a given potential energy function (the direct problem of the choice problem), to reconstruct the potential energy function for a given set of stationary points (the inverse problem of the choice problem). In this paper, using a computational experiment, the direct problem of the choice problem is discussed using the example of a description of a monoatomic cluster. The number and shape of the locally equilibrium (saddle) configurations of the binary potential are numerically estimated. An appropriate measure is introduced to distinguish configurations in space. The format of constructing the entire chain of multiparticle contributions to the potential energy function is proposed: binary, threeparticle, etc., multiparticle potential of maximum partiality. An infinite number of locally equilibrium (saddle) configurations for the maximum multiparticle potential is discussed and illustrated. A method of variation of the number of stationary points by combining multiparticle contributions to the potential energy function is proposed. The results of the work listed above are aimed at reducing the huge arbitrariness of the choice of the form of potential that is currently taking place. Reducing the arbitrariness of choice is expressed in the fact that the available knowledge about the set of a very specific set of stationary points is consistent with the corresponding form of the potential energy function.

In this paper, we propose an adaptive traffic signal control method invariant to the configuration of the traffic signal. The proposed method uses one neural network model to control traffic signals of various configurations, differing both in the number of controlled lanes and in the used traffic light control cycle (set of phases). To describe the state space, both dynamic information about the current state of the traffic flow and static data about the configuration of a controlled intersection are used. To increase the speed of model training and reduce the required amount of data required for model convergence, it is proposed to use an “expert” who provides additional data for model training. As an expert, we propose to use an adaptive control method based on maximizing the weighted flow of vehicles through an intersection. Experimental studies of the effectiveness of the developed method were carried out in a microscopic simulation software package. The obtained results confirmed the effectiveness of the proposed method in different simulation scenarios. The possibility of using the developed method in a simulation scenario that is not used in the training process was shown. We provide a comparison of the proposed method with other baseline solutions, including the method used as an “expert”. In most scenarios, the developed method showed the best results by average travel time and average waiting time criteria. The advantage over the method used as an expert, depending on the scenario under study, ranged from 2% to 12% according to the criterion of average vehicle waiting time and from 1% to 7% according to the criterion of average travel time.

This article presents an integrated dynamic model of eco-economic system of the Republic of Armenia (RA). This model is constructed using system dynamics methods, which allow to consider the major feedback related to key characteristics of eco-economic system. Such model is a two-objective optimization problem where as target functions the level of air pollution and gross profit of national economy are considered. The air pollution is minimized due to modernization of stationary and mobile sources of pollution at simultaneous maximization of gross profit of national economy. At the same time considered eco-economic system is characterized by the presence of internal constraints that must be accounted at acceptance of strategic decisions. As a result, we proposed a systematic approach that allows forming sustainable solutions for the development of the production sector of RA while minimizing the impact on the environment. With the proposed approach, in particular, we can form a plan for optimal enterprise modernization and predict long-term dynamics of harmful emissions into the atmosphere.