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The article focuses on the problem of mechanisms’ co-design for robotic systems to perform adaptive physical interaction with an unstructured environment, including physical human robot interaction. The co-design means simultaneous optimization of mechanics and control system, ensuring optimal behavior and performance of the system. Mechanics optimization refers to the search for optimal structure, geometric parameters, mass distribution among the links and their compliance; control refers to the search for motion trajectories for mechanism’s joints. The paper presents a generalized method of structural-parametric synthesis of underactuated mechanisms with closed kinematics for robotic systems for various purposes, e. g., it was previously used for the co-design of fingers’ mechanisms for anthropomorphic gripper and legs’ mechanisms for galloping robots. The method implements the concept of morphological computation of control laws due to the features of mechanical design, minimizing the control effort from the algorithmic component of the control system, which reduces the requirements for the level of technical equipment and reduces energy consumption. In this paper, the proposed method is used to optimize the structure and geometric parameters of the passive mechanism of the back support module of an industrial exosuit. Human movements are diverse and non-deterministic when compared with the movements of autonomous robots, which complicates the design of wearable robotic devices. To reduce injuries, fatigue and increase the productivity of workers, the synthesized industrial exosuit should not only compensate for loads, but also not interfere with the natural human motions. To test the developed exosuit, kinematic datasets from motion capture of an entire human body during industrial operations were used. The proposed method of structural-parametric synthesis was used to improve the ergonomics of a wearable robotic device. Verification of the synthesized mechanism was carried out using simulation: the passive module of the back is attached to two geometric primitives that move the chest and pelvis of the exosuit operator in accordance with the motion capture data. The ergonomics of the back module is quantified by the distance between the joints connecting the upper and bottom parts of the exosuit; minimizing deviation from the average value corresponds to a lesser limitation of the operator’s movement, i. e. greater ergonomics. The article provides a detailed description of the method of structural-parametric synthesis, an example of synthesis of an exosuit module and the results of simulation.

The work is devoted to the problem of creating a model with stationary parameters using historical data under conditions of unknown disturbances. The case is considered when a representative sample of object states can be formed using historical data accumulated only over a significant period of time. It is assumed that unknown disturbances can act in a wide frequency range and may have low-frequency and trend components. In such a situation, including data from different time periods in the sample can lead to inconsistencies and greatly reduce the accuracy of the model. The paper provides an overview of approaches and methods for data harmonization. In this case, the main attention is paid to data sampling. An assessment is made of the applicability of various data sampling options as a tool for reducing the level of uncertainty. We propose a method for identifying a self-leveling object model using data accumulated over a significant period of time under conditions of unknown disturbances with a wide frequency range. The method is focused on creating a model with stationary parameters that does not require periodic reconfiguration to new conditions. The method is based on the combined use of sampling and presentation of data from individual periods of time in the form of increments relative to the initial point in time for the period. This makes it possible to reduce the number of parameters that characterize unknown disturbances with a minimum of assumptions that limit the application of the method. As a result, the dimensionality of the search problem is reduced and the computational costs associated with setting up the model are minimized. It is possible to configure both linear and, in some cases, nonlinear models. The method was used to develop a model of closed cooling of steel on a unit for continuous hot-dip galvanizing of steel strip. The model can be used for predictive control of thermal processes and for selecting strip speed. It is shown that the method makes it possible to develop a model of thermal processes from a closed cooling section under conditions of unknown disturbances, including low-frequency components.

The study of the development of π-periodic perturbations of a converging spherical shock wave leading to cumulation limitation is performed. The study is based on 3D hydrodynamic calculations with the Carnahan – Starling equation of state for hard sphere fluid. The method of solving the Euler equations on moving (compressing) grids allows one to trace the evolution of the converging shock wave front with high accuracy in a wide range of its radius. The compression rate of the computational grid is adapted to the motion of the shock wave front, while the motion of the boundaries of the computational domain satisfy the condition of its supersonic velocity relative to the medium. This leads to the fact that the solution is determined only by the initial data at the grid compression stage. The second order TVD scheme is used to reconstruct the vector of conservative variables at the boundaries of the computational cells in combination with the Rusanov scheme for calculating the numerical vector of flows. The choice is due to a strong tendency for the manifestation of carbuncle-type numerical instability in the calculations, which is known for other classes of flows. In the three-dimensional case of the observed force, the carbuncle effect was obtained for the first time, which is explained by the specific nature of the flow: the concavity of the shock wave front in the direction of motion, the unlimited (in the symmetric case) growth of the Mach number, and the stationarity of the front on the computational grid. The applied numerical method made it possible to study the detailed flow pattern on the scale of cumulation termination, which is impossible within the framework of the Whitham method of geometric shock wave dynamics, which was previously used to calculate converging shock waves. The study showed that the limitation of cumulation is associated with the transition from the Mach interaction of converging shock wave segments to a regular one due to the progressive increase in the ratio of the azimuthal velocity at the shock wave front to the radial velocity with a decrease in its radius. It was found that this ratio is represented as a product of a limited oscillating function of the radius and a power function of the radius with an exponent depending on the initial packing density in the hard sphere model. It is shown that increasing the packing density parameter in the hard sphere model leads to a significant increase in the pressures achieved in a shock wave with broken symmetry. For the first time in the calculation, it is shown that at the scale of cumulation termination, the flow is accompanied by the formation of high-energy vortices, which involve the substance that has undergone the greatest shock-wave compression. Influencing heat and mass transfer in the region of greatest compression, this circumstance is important for current practical applications of converging shock waves for the purpose of initiating reactions (detonation, phase transitions, controlled thermonuclear fusion).

This work focuses on the application of a method of construction and conversion of three-dimensional computer models for optimization of geometric parameters of simulated devices. The method is used in design of complex technical devices for control system components of an exhaust gas recirculation vehicle – electric EGR valve with magnetic and electric motor. Three-dimensional geometric computer models were created in KOMPAS-3D environment and converted to Maxwell-2D. In Maxwell-2D environment transient electromagnetic processes for further optimization of parameters of therecirculation system devicewere calculated using a criterion of reducing power loss of the automobile engine.

The article focuses on a nonlinear mathematical model that describes the interaction of the different age groups of the employed population. The interactions are treated by analogy with population relationship (competition, discrimination, assistance, oppression, etc). Under interaction of peoples we mean the generalized social and economic mechanisms that cause related changes in the number of employees of different age groups. Three age groups of the employed population are considered. It is young specialists (15–29 years), workers with experience (30–49 years), the employees of pre-retirement and retirement age (50 and older). The estimation of model’s parameters for the southern regions of the Far Eastern Federal District (FEFD) is executed by statistical data. Analysis of model scenarios allows us to conclude the observed number fluctuations of the different ages employees on the background of a stable total employed population may be a consequence of complex interactions between these groups of peoples. Computational experiments with the obtained values of the parameters allowed us to calculate the rate of decline and the aging of the working population and to determine the nature of the interaction between the age groups of employees that are not directly as reflected in the statistics. It was found that in FEFD the employed of 50 years and older are discriminated against by the young workers under 29, employed up to 29 and 30–49 years are in a partnership. It is shown in most developed regions (Primorsky and Khabarovsk Krai) there is “uniform” competition among different age groups of the employed population. For Primorsky Krai we were able to identify the mixing effect dynamics. It is a typical situation for systems in a state of structural adjustment. This effect is reflected in the fact the long cycles of employed population form with a significant decrease in migration inflows of employees 30–49 years. Besides, the change of migration is accompanied by a change of interaction type — from employment discrimination by the oldest of middle generation to discrimination by the middle of older generation. In less developed regions (Amur, Magadan and Jewish Autonomous Regions) there are lower values of migration balance of almost all age groups and discrimination by young workers up 29 years of other age groups and employment discrimination 30–49 years of the older generation.

A method for studying the dispersion characteristics of photonic crystals — media with a dielectric constant that varies periodically in space — is considered. The method is based on the representation of the wave functions and permittivity of a periodic medium in the form of Fourier series and their subsequent substitution into the wave equation, which leads to the formulation of the dispersion equation. Using the latter, for each value of the wave vector it is possible determined a set of eigen frequencies. Each of eigen frequency forms a separate dispersion curve as a continuous function of the wave number. The Fourier expansion coefficients of the permittivity, which depend on the vectors of the reciprocal lattice of the photonic crystal, are determined on the basis of data on the geometric characteristics of the elements that form the crystal, their electrophysical properties and the density of the crystal. The solution of the dispersion equation found makes it possible to obtain complete information about the number of modes propagating in a periodic structure at different frequencies, and about the possibility of forming band gaps, i.e. frequency ranges within which wave propagation through a photonic crystal is impossible. The focus of this work is on the application of this method to the analysis of the dispersion properties of metallic photonic crystals. The difficulties that arise in this case due to the presence of intrinsic dispersion properties of the metals that form the elements of the crystal are overcome by an analytical description of their permittivity based on the model of free electrons. As a result, a dispersion equation is formulated, the numerical solution of which is easily algorithmized. That makes possible to determine the dispersion characteristics of metallic photonic crystals with arbitrary parameters. Obtained by this method the results of calculation of dispersion diagrams, which characterize two-dimensional metal photonic crystals, are compared with experimental data and numerical results obtained using the method of self-consistent equations. Their good agreement is demonstrated.

The article investigates the possibility of detecting traces of hazardous substances (explosives and narcotics) based on the detection of their vapors in the air. The relevance of the study stems from the need to counter terrorist threats and drug trafficking, where identifying even trace amounts of substances is critical. The focus is on mathematical modeling of the evaporation of a thin substance layer from a surface, based on molecular kinetic theory. A universal model is proposed, accounting for the physicochemical properties of substances, ambient temperature, adhesion to the surface, and the initial mass of the layer. Using the Hertz – Knudsen – Langmuir and Clausius – Clapeyron equations, analytical expressions are derived for the complete evaporation time, maximum vapor mass, and process dynamics. A dimensionless parameter, $\gamma$, is identified, determining the limiting conditions for evaporation. It is shown that substance adhesion (coefficient $\alpha$) affects the evaporation rate but not the final vapor mass. Calculations were performed for six model substances (TNT, RDX, PETN, amphetamine, cocaine, heroin) with a wide range of properties. At room temperature and a surface concentration of 100 ng/cm2, most substances evaporate completely, except for RDX, which remains on the surface at 84%. Evaporation times range from fractions of a second (amphetamine) to several hours (heroin). For low-volatility substances, the maximum mass capable of evaporating under given conditions is determined. The novelty of the work lies in the development of a universal model applicable to a broad class of hazardous substances and in identifying key parameters governing the evaporation process. The results enable the estimation of detection limits for trace substances using vapor-based methods and can be applied in the design of security systems.

Image denoising is one of the fundamental problems in digital image processing. This problem usually refers to the reconstruction of an image from an observed image degraded by noise. There are many factors that cause this degradation such as transceiver equipment, or environmental influences, etc. In order to obtain higher quality images, many methods have been proposed for image denoising problem. Most image denoising method are based on total variation (TV) regularization to develop efficient algorithms for solving the related optimization problem. TV-based models have become a standard technique in image restoration with the ability to preserve image sharpness.

In this paper, we focus on Poisson noise usually appearing in photon-counting devices. We propose an effective regularization model based on combination of first-order and fractional-order total variation for image reconstruction corrupted by Poisson noise. The proposed model allows us to eliminate noise while edge preserving. An efficient alternating minimization algorithm is employed to solve the optimization problem. Finally, provided numerical results show that our proposed model can preserve more details and get higher image visual quality than recent state-of-the-art methods.

We present a novel model for the dynamical trap of the stimulus – response type that mimics human control over dynamic systems when the bounded capacity of human cognition is a crucial factor. Our focus lies on scenarios where the subject modulates a control variable in response to a certain stimulus. In this context, the bounded capacity of human cognition manifests in the uncertainty of stimulus perception and the subsequent actions of the subject. The model suggests that when the stimulus intensity falls below the (blurred) threshold of stimulus perception, the subject suspends the control and maintains the control variable near zero with accuracy determined by the control uncertainty. As the stimulus intensity grows above the perception uncertainty and becomes accessible to human cognition, the subject activates control. Consequently, the system dynamics can be conceptualized as an alternating sequence of passive and active modes of control with probabilistic transitions between them. Moreover, these transitions are expected to display hysteresis due to decision-making inertia.

Generally, the passive and active modes of human control are governed by different mechanisms, posing challenges in developing efficient algorithms for their description and numerical simulation. The proposed model overcomes this problem by introducing the dynamical trap of the stimulus-response type, which has a complex structure. The dynamical trap region includes two subregions: the stagnation region and the hysteresis region. The model is based on the formalism of stochastic differential equations, capturing both probabilistic transitions between control suspension and activation as well as the internal dynamics of these modes within a unified framework. It reproduces the expected properties in control suspension and activation, probabilistic transitions between them, and hysteresis near the perception threshold. Additionally, in a limiting case, the model demonstrates the capability of mimicking a similar subject’s behavior when (1) the active mode represents an open-loop implementation of locally planned actions and (2) the control activation occurs only when the stimulus intensity grows substantially and the risk of the subject losing the control over the system dynamics becomes essential.

In today’s technology-driven world, lite devices like Internet of Things (IoT) devices and microcontrollers (MCUs) are becoming increasingly common. These devices are more energyefficient and affordable, often with reduced features compared to the standard versions such as very limited memory and processing power for typical machine learning models. However, modern machine learning models can have millions of parameters, resulting in a large memory footprint. This complexity not only makes it difficult to deploy these large models on resource constrained devices but also increases the risk of latency and inefficiency in processing, which is crucial in some cases where real-time responses are required such as autonomous driving and medical diagnostics. In recent years, neural networks have seen significant advancements in model optimization techniques that help deployment and inference on these small devices. This narrative review offers a thorough examination of the progression and latest developments in neural network optimization, focusing on key areas such as quantization, pruning, knowledge distillation, and neural architecture search. It examines how these algorithmic solutions have progressed and how new approaches have improved upon the existing techniques making neural networks more efficient. This review is designed for machine learning researchers, practitioners, and engineers who may be unfamiliar with these methods but wish to explore the available techniques. It highlights ongoing research in optimizing networks for achieving better performance, lowering energy consumption, and enabling faster training times, all of which play an important role in the continued scalability of neural networks. Additionally, it identifies gaps in current research and provides a foundation for future studies, aiming to enhance the applicability and effectiveness of existing optimization strategies.