A high pace of modern medical technologies development in the world requires the introduction of intelligent robotic equipment into Russian practice, which allows us to take the healthcare sector to a new level. Due to the progression of diseases of the patients’ musculoskeletal system and an increase in spinal injuries, rehabilitation equipment is becoming increasingly important. In addition, the development of domestic robotic rehabilitation complexes that match or exceed the functionality of foreign analogues is currently required. A fairly new area of modern medicine is a clinical analysis of biomechanical parameters and study of gait pathology using biomechanical models. Currently, to build parametric models, motion capture methods are being developed that make it possible to reconstruct and visualize the movement of human limbs, as well as to estimate various dynamic quantities, for example, motor forces or ground reaction force. The results obtained are used in rehabilitation complexes, video games, sports simulators, etc., where input data are user parameters recorded in real time. The results obtained allow us to conclude that it is necessary to further improve the characteristics of the rehabilitation complex by making changes to the mechanical design of the complex, as well as adjusting the mathematical model of the control system with additional calculations and modeling of the system structure. The developed experimental model of a mechatronic rehabilitation complex is based on the principles of subordinate regulation by actuators using PD regulators. And the desired coordinates of the movement of a person’s leg, obtained using a motion capture complex, are used as a driving signal for the control system.

The basic idea of constructing locally-adaptive regression models (LAR models) consists in the use of regressors defined on the local subregions of factor values. The belonging of factor values to a particular local subdomain is set by indicator functions. Indicator functions by their nature are close to the well-known concepts of membership functions from the theory of fuzzy systems (Fuzzy Systems). As a rule, to provide the required smoothness of the required dependence of the response on the acting factors such local subdomains are defined with overlapping - in the form of the so-called fuzzy partitions. Type or type of indicator functions may be very different: triangular, trapezoidal, and non-linear. Specifying one or another type of indicator function determines the scheme of weighing local models. Each indicator function must be defined for the entire range of the corresponding factor. Triangular-type functions are used as indicator functions in this work. Linear factor models are considered as local models. It is noted that in their original form the proposed LAR models are not identifiable. The issue of identification of such models in the case of joint estimation of all parameters is considered. The procedure of model reduction is introduced. The resulting model is written out in the space of functions that allow estimation. In the case of dividing the domain of factor determination into two, three or four fuzzy partitions we propose the basis of functions allowing evaluation. The results of computational experiment on regression dependence reconstruction by ordinary polynomials of different degrees and by LAR models are given. The efficiency of LAR models in comparison with polynomials of degree 3 and 4 is noted.

The algorithms based on the decomposition of a noisy image in an orthogonal basis of wavelet functions have been widely used to filter images (especially contrasting ones) over the past four decades. In this case, most wavelet filtering algorithms are of a threshold nature, namely: the decomposition coefficient smaller in an absolute value of a certain threshold value is reset to zero; otherwise the coefficient undergoes some (most often nonlinear) transformation. A certain (and very significant) drawback of threshold algorithms is that all coefficients of a certain decomposition level are processed with one identical threshold value (i.e., a constant value for all de-composition coefficients). This does not allow taking into account the “individual energy” of each decomposition coefficient for its more optimal processing. Therefore, we propose its own filtering factor for each coefficient, built on the basis of the optimal Wiener filtering and where a filtering parameter is introduced to compensate for incomplete a priori information on the value of the processed decomposition coefficients. In order to select a filtering parameter, a statistical approach has been proposed that makes it possible to estimate the optimal value of this parameter with acceptable accuracy. The performed computational experiment has shown the developed algorithm effectiveness for wavelet filtering of images.

Ensuring safety of people in the urban environment is an important area for improving the quality of people’s lives, and the development of modern intelligent technologies creates new opportunities to achieve this goal. The use of modern intelligent technologies can realize the potential of video surveillance systems, making possible the automatic recognition of dangerous situations in real time in order to take timely measures to handle them and provide aid to victims. Often, a dangerous situation is caused by deviant behavior of people – robbery, fight, vandalism, etc. But the existing works focused on recognizing deviant behavior are only focused on recognizing short-term distinguishing features, such as punches, falls or weapons in the hands of a person. At the same time, such features may often be absent, for instance, when a passerby is robbed but no fighting occurs, which is why the task of recognizing complex long-term scenes of deviant behavior remains unresolved. This paper proposes a method for automatic recognition of long-term human deviant behavior scenes, characterized by the integration of knowledge management and computer vision technologies for detecting and segmenting objects, estimating the three-dimensional human skeleton, tracking objects in video, estimating the ground plane normal to calculate the camera distance, and classification of short-term actions of people using three-dimensional skeleton. This method can be used in the development of a decision support system by operators of video monitoring systems used to detect and handle deviant behavior of people in real time in order to prevent escalation, provide timely aid to victims and detain suspects.

In this paper, the authors propose an algorithm for automatic operational flood detection of the Amur River basin based on data from the MSI instrument installed on the Sentinel-2 satellite. To solve the problem, a U-net convolutional neural network is used, improved due to the specifics of the data by adding an additional layer that reduces sampling and normalization layers after each neuron activation. As a training set, Sentinel-2 Level-2A data was used, which underwent the atmospheric correction procedure and represents 4 channels of the MSI instrument with a spatial resolution of 10 m, as well as index images built on their basis. As reference information, vector maps of river floods were used, built in an interactive mode by decoder specialists from the Far-Eastern Center of State Research Center for Space Hydrometeorology “Planeta”. The results of the neural network algorithm after the learning process were evaluated according to the metrics that amounted to: Precision – 94.91%, Recall – 90.76%, F1-measure – 92.79%. High accuracy ratings and fast operation speed make it possible to use the developed algorithm for automatic operational flood detection of the Amur River basin floods in the tasks of integrated monitoring of flood conditions. The work is a complete technical solution and has been put into trial operation at the Far-Eastern Center of State Research Center for Space Hydrometeorology “Planeta”. In the future, the results obtained by the authors are planned to be adapted to the data of the Russian satellite of the Meteor-M series with the KMSS-2 multi-zone satellite imagery complex installed on board. The expected results will improve the quality of the thematic products provided and will make it possible to switch over to the use of domestic data when creating our own technologies for processing satellite information.

To date, in matters of processing and managing network traffic, there is no single approach applicable to a wide pool of practical and applied tasks that would allow solving traffic management issues. Published works in this area are aimed at solving highly specialized problems: when applying complex solutions, these problems require the introduction of many additional parameters that increase computational complexity or solve only narrowly focused problems. This article provides a comparative analysis of classical network traffic models and reveals the possibility of practical application of such models in real-life problems. Classical traffic models are considered in detail, namely the Poisson model, heavy-tail traffic models, models based on Markov chains, traffic models based on the fractal theory and models based on stochastic time series. A mathematical description of each traffic model is also presented. Based on the results of the comparative analysis, the applicability of mathematical models to real projects was assessed. Based on it, two main problems were identified: first, the lack of consideration of the previous results of network traffic processing; secondly, the narrowly focused applicability of each of the models, given the rigid binding to subject areas, which allows solving only a narrow range of problems. The following indicators were taken as the criteria for evaluating network traffic models: the ability to scale the analyzed traffic, the ability to consider previous traffic data, computational complexity and the absence of some random features that could affect the operation of the model. A detailed study of the problem of traffic scaling revealed the main patterns, dependencies, dimensions of the traffic packet by the time it was processed.

This article presents the results of the study of various transient modes of the pumping unit of an oil pumping station, in particular, the stator currents of the drive motor and the speed characteristic of its rotor rotation. The characteristics of the pumping unit can change quite significantly during operation. This is due to the fact that the parameters of the pumped oil change almost continuously, physical wear of the parts of the pumping unit also occurs, and the impeller is especially susceptible to wear. The input and output variables of this object are, respectively, the time and the angular velocity of rotation of its rotor. The combination of the above factors has a significant impact on the change in the speed of rotation of the rotor during operation, which can adversely affect the operation of the system as a whole. In order to eliminate the influence of external factors on the operation of the pumping unit, the relationship between the input and output variables was described by an ordinary differential equation with constant coefficients. The purpose of this work is to illustrate the possibilities and "technology" of applying the sensitivity algorithm for estimating the parameters of a given equation and to show that the proposed method and the numerical algorithm that implements it allow us to quite successfully solve the problem under consideration and they are suitable for solving many similar applied problems related to estimation of parameters of ordinary differential equations.Based on the results of the work, the proposed method and the numerical algorithm that implements it have shown their efficiency and can be further used to automate real control objects in various fields of science and technology.

In this paper, we investigate the problem of improving data quality using the Kalman filter in Matlab Simulink. Recently, this filter has become one of the most common algorithms for filtering and processing data in the implementation of control systems (including automated control systems) and the creation of software systems for digital filtering from noise and interference, for example, speech signals. It is also widely used in many fields of science and technology. Due to its simplicity and efficiency, it can be found in GPS receivers, in devices for processing sensor readings for various purposes, etc. It is known that one of the important tasks that should be solved in systems for processing sensor readings is the ability to detect and filter noise. Sensor noise leads to unstable measurement data. This, of course, ultimately leads to a decrease in the accuracy and performance of the control device. One of the methods that can be used to solve the problem of optimal filtering is the development of cybernetic algorithms based on the Kalman and Wiener filters. The filtering process can be carried out in two forms, namely: hardware and software algorithms. Hardware filtering can be built electronically. However, it is less efficient as it requires additional circuitry in the system. To overcome this obstacle, you can use filtering in the form of programming algorithms in a single method. In addition to the fact that it does not require electronic hardware circuitry, the filtering performed is even more accurate because it uses a computational process. The paper analyzes the results of applying the Kalman filter to eliminate errors when measuring the coordinates of the tracked target, to obtain a "smoothed" trajectory and shows the results of the filter development process when processing an electrocardiogram. The development of the Kalman filter algorithm is based on the procedure of recursive assessment of the measured state of the research object.