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The article considers minimization of the expectation of convex function. Problems of this type often arise in machine learning and a variety of other applications. In practice, stochastic gradient descent (SGD) and similar procedures are usually used to solve such problems. We propose to use the ellipsoid method with mini-batching, which converges linearly and can be more efficient than SGD for a class of problems. This is verified by our experiments, which are publicly available. The algorithm does not require neither smoothness nor strong convexity of the objective to achieve linear convergence. Thus, its complexity does not depend on the conditional number of the problem. We prove that the method arrives at an approximate solution with given probability when using mini-batches of size proportional to the desired accuracy to the power −2. This enables efficient parallel execution of the algorithm, whereas possibilities for batch parallelization of SGD are rather limited. Despite fast convergence, ellipsoid method can result in a greater total number of calls to oracle than SGD, which works decently with small batches. Complexity is quadratic in dimension of the problem, hence the method is suitable for relatively small dimensionalities.

Non-destructive acoustic emission testing is an effective and cost-efficient way to examine pressure vessels for hidden defects (cracks, laminations etc.), as well as the only method that is sensitive to developing defects. The sound velocity in the test object and its adequate definition in the location scheme are of paramount importance for the accurate detection of the acoustic emission source. The acoustic emission data processing method proposed herein comprises a set of numerical methods and allows defining the source coordinates and the most probable velocity for each signal. The method includes pre-filtering of data by amplitude, by time differences, elimination of electromagnetic interference. Further, a set of numerical methods is applied to them to solve the system of nonlinear equations, in particular, the Newton – Kantorovich method and the general iterative process. The velocity of a signal from one source is assumed as a constant in all directions. As the initial approximation is taken the center of gravity of the triangle formed by the first three sensors that registered the signal. The method developed has an important practical application, and the paper provides an example of its approbation in the calibration of an acoustic emission system at a production facility (hydrocarbon gas purification absorber). Criteria for prefiltering of data are described. The obtained locations are in good agreement with the signal generation sources, and the velocities even reflect the Rayleigh-Lamb division of acoustic waves due to the different signal source distances from the sensors. The article contains the dependency graph of the average signal velocity against the distance from its source to the nearest sensor. The main advantage of the method developed is its ability to detect the location of different velocity signals within a single test. This allows to increase the degree of freedom in the calculations, and thereby increase their accuracy.

We now review the main points of mean-field approximation (MFA) in its application to multicomponent stochastic reaction-diffusion systems.

We present the chemical reaction model under study — brusselator. We write the kinetic equations of reaction supplementing them with terms that describe the diffusion of the intermediate components and the fluctuations of the concentrations of the initial products. We simulate the fluctuations as random Gaussian homogeneous and spatially isotropic fields with zero means and spatial correlation functions with a non-trivial structure. The model parameter values correspond to a spatially-inhomogeneous ordered state in the deterministic case.

In the MFA we derive single-site two-dimensional nonlinear self-consistent Fokker–Planck equation in the Stratonovich's interpretation for spatially extended stochastic brusselator, which describes the dynamics of probability distribution density of component concentration values of the system under consideration. We find the noise intensity values appropriate to two types of Fokker–Planck equation solutions: solution with transient bimodality and solution with the multiple alternation of unimodal and bimodal types of probability density. We study numerically the probability density dynamics and time behavior of variances, expectations, and most probable values of component concentrations at various noise intensity values and the bifurcation parameter in the specified region of the problem parameters.

Beginning from some value of external noise intensity inside the ordered phase disorder originates existing for a finite time, and the higher the noise level, the longer this disorder “embryo” lives. The farther away from the bifurcation point, the lower the noise that generates it and the narrower the range of noise intensity values at which the system evolves to the ordered, but already a new statistically steady state. At some second noise intensity value the intermittency of the ordered and disordered phases occurs. The increasing noise intensity leads to the fact that the order and disorder alternate increasingly.

Thus, the scenario of the noise induced order–disorder transition in the system under study consists in the intermittency of the ordered and disordered phases.

The paper develops a new mathematical method of the joint signal and noise parameters determination at the Rice statistical distribution by method of moments based upon the analysis of data for the 1-st and the 3-rd raw moments of the random rician value. The explicit equations’ system have been obtained for required parameters of the signal and noise. In the limiting case of the small value of the signal-to-noise ratio the analytical formulas have been derived that allow calculating the required parameters without the necessity of solving the equations numerically. The technique having been elaborated in the paper ensures an efficient separation of the informative and noise components of the data to be analyzed without any a-priori restrictions, just based upon the processing of the results of the signal’s sampled measurements. The task is meaningful for the purposes of the rician data processing, in particular in the systems of magnetic-resonance visualization, in ultrasound visualization systems, at the optical signals’ analysis in range measuring systems, in radio location, etc. The results of the investigation have shown that the two parameter task solution of the proposed technique does not lead to the increase in demanded volume of computing resources compared with the one parameter task being solved in approximation that the second parameter of the task is known a-priori There are provided the results of the elaborated technique’s computer simulation. The results of the signal and noise parameters’ numerical calculation have confirmed the efficiency of the elaborated technique. There has been conducted the comparison of the accuracy of the sought-for parameters estimation by the technique having been developed in this paper and by the previously elaborated method of moments based upon processing the measured data for lower even moments of the signal to be analyzed.

We present a simulation methodology for the prediction of ЃgunexpectedЃh bottlenecks, i.e., the bottlenecks that occur suddenly and unexpectedly for drivers on a highway. Such unexpected bottlenecks can be either a moving bottleneck (MB) caused by a slow moving vehicle or a motionless bottleneck caused by a stopped vehicle (SV). Based on simulations of a stochastic microscopic traffic flow model in the framework of KernerЃfs three-phase traffic theory, we show that through the use of a small share of probe vehicles (FCD) randomly distributed in traffic flow the reliable prediction of ЃgunexpectedЃh bottlenecks is possible. We have found that the time dependence of the probability of MB and SV prediction as well as the accuracy of the estimation of MB and SV location depend considerably on sequences of phase transitions from free flow (F) to synchronized flow (S) (F→S transition) and back from synchronized flow to free flow (S→F transition) as well as on speed oscillations in synchronized flow at the bottleneck. In the simulation approach, the identification of F→S and S→F transitions at an unexpected bottleneck has been made in accordance with Kerner's three-phase traffic theory. The presented simulation methodology allows us both the prediction of the unexpected bottleneck that suddenly occurs on a highway and the distinguishing of the origin of the unexpected bottleneck, i.e., whether the unexpected bottleneck has occurred due to a MB or a SV.

The gradual incorporation of automated vehicles into the global transport networks leads to the need to develop tools to assess the impact of this process on various aspects of traffic. This implies a more organized movement of automated vehicles which can form uniformly moving platoons. The influence of the formation and movement of these platoons on the dynamics of traffic flow is of great interest. The currently most developed traffic flow models are based on the cellular automaton approach. They are mainly developed in the direction of increasing accuracy. This inevitably leads to the complication of models, which in their modern form have significantly moved away from the original philosophy of cellular automata, which implies simplicity and schematicity of models at the level of evolution rules, leading, however, to a complex organized behavior of the system. In the present paper, a simulation model of connected automated vehicles platoon dynamics in a heterogeneous transport system is proposed, consisting of two types of agents (vehicles): human-driven and automated. The description of the temporal evolution of the system is based on modified rules 184 and 240 for elementary cellular automata. Human-driven vehicles move according to rule 184 with the addition of accidental braking, the probability of which depends on the distance to the vehicle in front. For automated vehicles, a combination of rules is used depending on the type of nearest neighbors, regardless of the distance to them, which brings non-local interaction to the model. At the same time, it is considered that a group of sequentially moving connected automated vehicles can form an organized platoon. The influence of the ratio of types of vehicles in the system on the characteristics of the traffic flow during free movement on a circular one-lane and two-lane roads, as well as in the presence of a traffic light, is studied. The simulation results show that the effect of platoon formation is significant for a freeway traffic flow; the presence of a traffic light reduces the positive effect by about half. The movement of platoons of connected automated vehicles on two-lane roads with the possibility of lane changing was also studied. It is shown that considering the types of neighboring vehicles (automated or human-driven) when changing lanes for automated vehicles has a positive effect on the characteristics of the traffic flow.

The article proposes a model for assessing the potential strength of a composite material based on modern fibers with brittle fracture.

Materials consisting of parallel cylindrical fibers that are quasi-statically stretched in one direction are simulated. It is assumed that the sample is not less than 100 pieces, which corresponds to almost significant cases. It is known that the fibers have a distribution of ultimate deformation in the sample and are not destroyed at the same moment. Usually the distribution of their properties is described by the Weibull–Gnedenko statistical distribution. To simulate the strength of the composite, a model of fiber breaks accumulation is used. It is assumed that the fibers united by the polymer matrix are crushed to twice the inefficient length — the distance at which the stresses increase from the end of the broken fiber to the middle one. However, this model greatly overestimates the strength of composites with brittle fibers. For example, carbon and glass fibers are destroyed in this way.

In some cases, earlier attempts were made to take into account the stress concentration near the broken fiber (Hedgepest model, Ermolenko model, shear analysis), but such models either required a lot of initial data or did not coincide with the experiment. In addition, such models idealize the packing of fibers in the composite to the regular hexagonal packing.

The model combines the shear analysis approach to stress distribution near the destroyed fiber and the statistical approach of fiber strength based on the Weibull–Gnedenko distribution, while introducing a number of assumptions that simplify the calculation without loss of accuracy.

It is assumed that the stress concentration on the adjacent fiber increases the probability of its destruction in accordance with the Weibull distribution, and the number of such fibers with an increased probability of destruction is directly related to the number already destroyed before. All initial data can be obtained from simple experiments. It is shown that accounting for redistribution only for the nearest fibers gives an accurate forecast.

This allowed a complete calculation of the strength of the composite. The experimental data obtained by us on carbon fibers, glass fibers and model composites based on them (CFRP, GFRP), confirm some of the conclusions of the model.

The effectiveness of communication and data transmission systems (CSiPS), which are an integral part of modern systems in almost any field of science and technology, largely depends on the stability of the frequency of the generated signals. The signals generated in the CSiPD can be considered as processes, the frequency of which changes under the influence of a combination of external influences. Changing the frequency of the signals leads to a decrease in the signal-tonoise ratio (SNR) and, consequently, a deterioration in the characteristics of the signal-to-noise ratio, such as the probability of a bit error and bandwidth. It is most convenient to consider the description of such changes in the frequency of signals as random processes, the apparatus of which is widely used in the construction of mathematical models describing the functioning of systems and devices in various fields of science and technology. Moreover, in many cases, the characteristics of a random process, such as the distribution law, mathematical expectation, and variance, may be unknown or known with errors that do not allow us to obtain estimates of the signal parameters that are acceptable in accuracy. The article proposes an algorithm for solving the problem of determining the characteristics of a random process (signal frequency) based on a set of samples of its frequency, allowing to determine the sample mean, sample variance and the distribution law of frequency deviations in the general population. The basis of this algorithm is the comparison of the values of the observed random process measured over a certain time interval with a set of the same number of random values formed on the basis of model distribution laws. Distribution laws based on mathematical models of these systems and devices or corresponding to similar systems and devices can be considered as model distribution laws. When forming a set of random values for the accepted model distribution law, the sample mean value and variance obtained from the measurement results of the observed random process are used as mathematical expectation and variance. The feature of the algorithm is to compare the measured values of the observed random process ordered in ascending or descending order and the generated sets of values in accordance with the accepted models of distribution laws. The results of mathematical modeling illustrating the application of this algorithm are presented.

Traditional geological search methods going to be ineffective. The exploration depth of kimberlite bodies and ore deposits has increased significantly. The only direct exploration method is to drill a system of wells to the depths that provide access to the enclosing rocks. Due to the high cost of drilling, the role of inter-well survey methods has increased. They allows to increase the mean well spacing without significantly reducing the kimberlite or ore body missing probability. The method of inter-well radio wave survey is effective to search for high contrast conductivity objects. The physics of the method based on the dependence of the electromagnetic wave propagation on the propagation medium conductivity. The source and receiver of electromagnetic radiation is an electric dipole, they are placed in adjacent wells. The distance between the source and receiver is known. Therefore we could estimate the medium absorption coefficient by the rate of radio wave amplitude decrease. Low electrical resistance rocks corresponds to high absorption of radio waves. The inter-well measurement data allows to estimate an effective electrical resistance (or conductivity) of the rock. Typically, the source and receiver are immersed in adjacent wells synchronously. The value of the of the electric field amplitude measured at the receiver site allows to estimate the average value of the attenuation coefficient on the line connecting the source and receiver. The measurements are taken during stops, approximately every 5 m. The distance between stops is much less than the distance between adjacent wells. This leads to significant spatial anisotropy in the measured data distribution. Drill grid covers a large area, and our point is to build a three-dimensional model of the distribution of the electrical properties of the inter-well space throughout the whole area. The anisotropy of spatial distribution makes hard to the use of standard geostatistics approach. To build a three-dimensional model of attenuation coefficient, we used one of machine learning theory methods, the method of nearest neighbors. In this method, the value of the absorption coefficient at a given point is calculated by $k$ nearest measurements. The number $k$ should be determined from additional reasons. The spatial distribution anisotropy effect can be reduced by changing the spatial scale in the horizontal direction. The scale factor $\lambda$ is one yet external parameter of the problem. To select the parameters $k$ and $\lambda$ values we used the determination coefficient. To demonstrate the absorption coefficient three-dimensional image construction we apply the procedure to the inter-well radio wave survey data. The data was obtained at one of the sites in Yakutia.

This paper considers various approaches to averaging the generalized travel costs calculated for different modes of travel in the transportation network. The mode of transportation is understood to mean both the mode of transport, for example, a car or public transport, and movement without the use of transport, for example, on foot. The task of calculating the trip matrices includes the task of calculating the total matrices, in other words, estimating the total demand for movements by all modes, as well as the task of splitting the matrices according to the mode, also called modal splitting. To calculate trip matrices, gravitational, entropy and other models are used, in which the probability of movement between zones is estimated based on a certain measure of the distance of these zones from each other. Usually, the generalized cost of moving along the optimal path between zones is used as a distance measure. However, the generalized cost of movement differs for different modes of movement. When calculating the total trip matrices, it becomes necessary to average the generalized costs by modes of movement. The averaging procedure is subject to the natural requirement of monotonicity in all arguments. This requirement is not met by some commonly used averaging methods, for example, averaging with weights. The problem of modal splitting is solved by applying the methods of discrete choice theory. In particular, within the framework of the theory of discrete choice, correct methods have been developed for averaging the utility of alternatives that are monotonic in all arguments. The authors propose some adaptation of the methods of the theory of discrete choice for application to the calculation of the average cost of movements in the gravitational and entropy models. The transfer of averaging formulas from the context of the modal splitting model to the trip matrix calculation model requires the introduction of new parameters and the derivation of conditions for the possible value of these parameters, which was done in this article. The issues of recalibration of the gravitational function, which is necessary when switching to a new averaging method, if the existing function is calibrated taking into account the use of the weighted average cost, were also considered. The proposed methods were implemented on the example of a small fragment of the transport network. The results of calculations are presented, demonstrating the advantage of the proposed methods.