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The paper proposes a method of infrared spectroscopy for objective control of the adhesive composition in the production of paper. For sizing paper, in order to increase mechanical strength and reduce water absorption, water-repellent materials are used. One of the most effective and used water-repellent material is ordinary pine resin rosin which is in short supply for the Republic of Uzbekistan. The paper investigates the properties of a new adhesive based on rosin from cherry resin for use in mass sizing in order to expand the range of sizing agents and reduce the share of imported components in the structure of local paper production. Infrared spectroscopy was used to identify the rosin-based glue from the resin of the cherry tree. The built-in device with a diamond crystal of the universal and compact Thermo Scientific Nicolet iS50 IR Fourier spectrometer made it possible to register spectra in the mid and far IR ranges up to 100 cm–1. The efficiency of sizing paper samples was studied by standard methods and evaluated by the following properties: density, tensile strength at stretching on a tensile machine, surface absorbency with one-sided wetting. Infrared spectra revealed the identity of the sizing agent from the resin of the cherry tree to the glue from the pine resin, taken for comparison, because their chemical composition is the same. A comparative analysis of the mechanical and surface properties of paper samples confirmed the possibility of using the studied sizing agent, since the obtained values are close to each other, while it is advisable to add in the range from 1 to 1.5 g per 100 g of dry cellulose raw materials. The use of infrared spectroscopy for express analysis of the composition of the sizing agent is promising, since it becomes possible to control the technological process and create glued types of paper products with desired properties that ensure print quality without loss of fine image details.

The paper presents the concept of a point amplitude sensor for the registration of displacement of geotextile, a synthetic fabric that is used to reinforce geotechnical structures such as a dam. The implementation of a system for continuous monitoring of the structural condition of a building based on the concept of a “smart” geotextile has the potential to significantly enhance the safety of the structure. Such a system could provide early warning of the necessity for unscheduled repairs, the occurrence of an emergency situation, and the need for the immediate cessation of building operations, evacuation of personnel or population. The capabilities of existing technical solutions for displacement sensors have been evaluated. It is not feasible to apply existing monitoring systems utilizing fiber Bragg Grating Sensors (FBG) in the context of geotextile. This is due to the greater pliability of the soil which exhibits minimal elastic deformation. In addition, FBG sensors are much more expensive in production compared to telecommunication optical fiber. The single-mode fiber which constitutes the sensing element, forms one or more loops that are placed between movable stops that are attached to the sensor body and to the movable activator. At the point of macro bending of the reinforcing fiber, the phenomenon of total internal reflection is disrupted, which in turn gives rise to amplitude modulation of the radiation. The macro bending is proportional to the displacement of the activator attached to the geotextile. This paper presents the design, dimensions and mathematical relationships of the sensing element as well as the dimensions and characteristics of the design elements for signal processing. The sensor model is constructed from ABS plastic and fiber Corning SMF-28. An experimental setup was constructed to test the proposed concept which involved controlling the displacement of the activator, the input and output of radiation. The dependences of the output power on the fiber bending diameter, ranging from 25 to 11 mm, and the displacement, up to 14 mm, at a radiation wavelength of 1550 nm, were determined. It was demonstrated that the obtained dependences were monotonic and exhibited quasi-linear plots. The kinks at the small diameter of the fiber bend are caused by two factors: the intensive radiation output from the core to the cladding and scattering within it; and at the large diameter, they are due to small bending losses. The conducted studies have demonstrated that the sensor is capable of reliably detecting displacements up to 0.5 mm. The results exhibited good repeatability. The proposed sensor demonstrated inferior accuracy compared to FBG sensors. Conversely, at comparable accuracy of ground displacement registration, the proposed sensor was observed to be an order of magnitude more cost-effective than FBG sensors.

The task of automatic metainformation recognition from audio sources is to detect and extract data of various natures (speech, noises, acoustic scenes, acoustic events, anomalies) from a given audio input signal. This area is well developed and known to the scientific community and has various approaches with high quality. But, the vast majority of such methods are based on large neural networks with a huge number of weights to be trained. Subsequently, it is impractical to use them in environments with severely limited computing resources. The smart device industry is currently growing rapidly: smartphones, smart watches, voice assistants, TV, smart home. Such products have limitations in both processor and memory. At that moment, the State-of-the-Art way to cope with these conditions is to use so-called low-complexity models. Moreover, in recent years, the interest of the scientific community in the above-mentioned problem has been growing (DCASE Workshop). One of the most crucial subtasks in the global meta information recognition problem is the task of Automatic Scene Classification and the task of Sound Event Detection. The most important scientific questions are the development of both the optimal low-complexity neural network architecture and learning algorithms to obtain a low-resource, high-quality system for classifying acoustic scenes and detecting sound events. In this paper the datasets from DCASE Challenge “Low-Complexity Acoustic Scene Classification” and “Sound Event Detection with Weak Labels and Synthetic Soundscapes” were used. A multitask neural network architecture was proposed consisting of a common encoder and two independent decoders for each of the two tasks. The classical algorithms of multitask learning SoftMTL and HardMTL were considered, and their modifications were developed: CrossMTL, which is based on the idea of reusing data from one task when training the decoder to solve the second task, and FreezeMTL, in which the trained weights of the common encoder are frozen after training on the first task and used to optimize the second decoder. As a result of the experiments, it was shown that the use of the CrossMTL modification can significantly increase the accuracy of the classification of acoustic scenes and event detection in compare with classical approaches SoftMTL and HardMTL. The FreezeMTL algorithm made it possible to obtain a model that provides 42.44 % accuracy in scene classification and 45.86 % accuracy in event detection, which is comparable to the results of the baseline solutions of 2023. In this paper, a low-complexity neural network consisting of 633.5 K trainable parameters was proposed, requiring 43.2 M MACs to process one second audio. This approach uses 7.8 % fewer trainable parameters and 40 % fewer MACs compared to the naive application of two independent models. The developed model can be used in smart devices due to a small number of trainable parameters, as well as a small number of MACs required for its application.

Attribute Based Access Control (ABAC) is one the most efficient, scalable, and well used access control. It’s based on attributes not on users, but even when the users want to get access to some resource, they must submit their attributes for the verification process which may reveal the privacy of the users. Many research papers suggest blockchain-based ABAC which provides an immutable and transparent access control system. However, the privacy of the system may be compromised depending on the nature of the attributes. A Zero-Knowledge Proof, Ethereum-Based Access Control (ZK‑ABAC) is proposed in this paper to simplify the management of access to the devices/objects and provide an efficient and immutable platform that keeps track of all actions and access management and preserve the privacy of the attributes. Our ZK-ABAC model utilizes smart contracts to facilitate access control management, Zero-Knowledge Succinct Non- Interactive Argument of Knowledge (ZK-SNARK) protocol to add privacy to attributes, InterPlanetary File System (IPFS) network to provide distributed storage system, and Chainlink to manage communications and data between on/ off-chain systems. Comprehensive experiments and tests were conducted to evaluate the performance of our model, including the implementation of ZK-SNARK on the Ethereum blockchain. The results demonstrated the scalability challenges in the setup and proving phases, as well as the efficiency gains in the verification phase, particularly when scaled to higher numbers of users. These findings underscore the practical viability of our ZK-ABAC model for secure and privacy-preserving access control in decentralized environments.

Currently, switched reluctance motors are considered the most promising type of electromechanical energy converter without permanent magnets, especially for operations at sub-nominal speeds. To control of a switched reluctance motor to minimize torque ripple requires the regulation of phase currents based on the rotor angular position, utilizing the flux linkage as a function of both current and rotor angle. The flux linkage characteristic is essential in control systems that indirectly determine the rotor position. The paper presents an experimental methodology for deriving the flux linkage characteristic of a switched reluctance motor. The calculation of flux linkage for each rotor position angle of the electric machine is provided. The proposed methodology involves mechanically locking the rotor and periodically applying voltage to one of the motor phases using a power converter to gather data on phase current and voltage. Using the proposed experimental methodology, the relationships between flux linkage, phase current, and rotor angle were obtained. The results demonstrate that this methodology can be effectively utilized to accurately determine the flux linkage characteristic of a switched reluctance motor. The experimental methodology proposed in this paper can be employed to generate the flux linkage characteristic of a switched reluctance motor. This approach is particularly advantageous for designing model predictive control systems.

A new method for the synthesis of adaptive state observation for a class of nonlinear non-stationary systems is proposed. This task is important and fundamental in control theory and is related to both the control problem and the task of monitoring the efficiency of the system operation. The solution to the problem is based on the generalized observer parameter estimation method to obtain the regression equation necessary for estimating the state and parameters of the system. Further, the dynamic regressor expansion and blending method dynamic regressor extension and mixing method is applied to identify the unknown system parameters. The paper proposes a method for estimating the state vector for a nonlinear non-stationary system in which the unknown parameters depend on the state vector under external disturbances. The results obtained are rigorously proved using mathematical theory. Simulation in Matlab/Simulink is performed to demonstrate the effectiveness of the developed algorithm. The mathematical model of the considered objects is a nonlinear system of equations with variable parameters. Compared to previous methods, the method proposed in this paper is more general, especially in a system where the unknown parameters depend on the state vector with nonlinear functions. However, the problem is currently solved only for discrete systems. In the future, it may be possible to extend it to continuous systems.

The paper examines the xanthene fluorescent dyes questions available to a wide laboratories range in order to detect microplastics with an average particle length of 157 ± 59 μm in soil samples and conduct phytotests using fluorescently labeled microplastics. For the research, soils with a humus content of 1.59 ± 0.15 % (P1) and 6.74 ± 0.11 % (P2) as well as suspension polyvinyl chloride (RusVinyl LLC, 157 ± 59 μm, white) were used. In order to study the possibility of selective staining of microplastics in the presence of soil particles, polyvinyl chloride microparticles, soil P1 and P2, as well as a mixture of soil P1 and polyvinyl chloride (5 % by weight) were stained with rhodamine B, rhodamine G, fluorescein and eosin Y in isopropyl alcohol (dye concentration — 200 mg/L, temperature — 100 °C, staining time — 2 hours with constant stirring on a magnetic stirrer) and washed with distilled water on a paper filter. To study the chemical polyvinyl chloride microparticles structure before and after staining attenuated total reflectance-Fourier transform infrared spectroscopy was used (spectrometer Tensor 37 (Bruker, Germany), attenuated total internal reflection MIRacle Pike attachment with a diamond-coated ZnSe crystal). Spectrophotometry and microphotograph analysis using ImageJ software were used to determine whether dye could be leached from polyvinyl chloride microparticles after repeated washing with water. To conduct the laboratory experiment to detect microplastics in soil a mixture of P1 soil and polyvinyl chloride microparticles (0.1 % by weight) was prepared and stained with rhodamine G. In order to reduce the amount of mineral particles and concentrate polyvinyl chloride microparticles, a separation technique was used due to the difference in density using binary solution of NaCl and Ca(NO3)2. Microphotographs were obtained using an optical microscope with an additional ultraviolet source (λ = 365 nm). Image areas measurements of detected microparticles projections, that are necessary for calculating the approximate mass of the pollutant, were carried out using ImageJ software. The possibility of using fluorescently labeled polyvinyl chloride microparticles for phytotests was established using the seed germination test in contaminated soil and studying seedlings using an optical microscope with an additional ultraviolet source. It was shown that after staining with rhodamine B and rhodamine G, fluorescence is observed in polyvinyl chloride microparticles both separately and in a mixture, since soil particles P1 and P2 do not acquire similar properties. When fluorescein and eosin were used, polyvinyl chloride microparticles and soil practically did not fluoresce. It has been established that coloring does not affect the polyvinyl chloride chemical structure. Dyes are not washed out of polyvinyl chloride microparticles after repeated washing with water. The laboratory experiment showed that it is possible to detect and quantitation polyvinyl chloride microparticles in soil at the 0.1 % concentration by weight with a relative error of about 30 %. It is possible to use fluorescently labeled polyvinyl chloride microparticles with rhodamine B and rhodamine G when conducting phytotests. This research demonstrates for the first time the possibility of selective staining of polyvinyl chloride microparticles with rhodamine B and rhodamine G in a mixture with soil due to the observed fluorescence and their detection in a concentration of 0.1 % by weight. The results obtained expand knowledge in the field of monitoring microplastics in soil and, since today there are no standardized methods for detecting this pollutant, it can be used in their development. Fluorescently labeled polyvinyl chloride microparticles with rhodamine B and rhodamine G are planned to be used in phytotests as part of experiments on the hygienic justification of the maximum permissible concentration of the pollutant in the soil.

Personalization is one of the keyways to improve the performance of conversational agents. It improves the quality of user interaction with a conversational agent and increases user satisfaction by increasing the consistency and specificity of responses. The dialogue with the agent becomes more consistent, the inconsistency of responses is reduced, and the responses become more specific and interesting. Training and testing personalized conversational agents requires specific datasets containing facts about a persona and texts of persona’s dialogues where replicas use those facts. There are several datasets in English and Chinese containing an average of five facts about a persona where the dialogues are composed by crowdsourcing users who repeatedly imitate different personas. This paper proposes a methodology for collecting an original dataset containing an extended set of facts about a persona and natural dialogues between personas. The new RuPersonaChat dataset is based on three different recording scenarios: an interview, a short conversation, and a long conversation. This is the first dataset for dialogue agent personalization collected which includes both natural dialogues and extended persona’s descriptions. Additionally, in the dataset, the persona’s replicas are annotated with the facts about the persona from which they are generated. The methodology for collecting an original corpus of test data proposed in this paper allows for testing language models for various tasks within the framework of personalized dialogue agent development. The collected dataset includes 139 dialogues and 2608 replicas. This dataset was used to test answer and question generation models and the best results were obtained using the Gpt3-large model (perplexity is equal to 15.7). The dataset can be used to test the personalized dialogue agents’ ability to talk about themselves to the interlocutor, to communicate with the interlocutor utilizing phatic speech and taking into account the extended context when communicating with the user.

In this paper, we study the trajectory tracking problem of a three-wheeled omnidirectional mobile robot with full state constraints and actuator saturation. Firstly, we analyze a three-wheeled omnidirectional mobile robot and give control model with actuator saturation. By using tan-type Barrier Lyapunov Function and backstepping method, kinematic and dynamic controllers are built, which can ensure that the system full states will not violate the given constraints when the robot is performing trajectory tracking. Then, considering the differential explosion problem which occurs when solving the derivatives of the virtual control law, we use a second-order differential sliding mode surface to calculate it, so as to reduce the complexity of the operation. In addition, due to the output saturation problem of the robot drive motor, an auxiliary compensation system is adopted to compensate for the error generated by the saturation function. Finally, an experimental simulation is performed in MATLAB and the simulation results illustrate the effectiveness of the control algorithm proposed in this paper.

The paper analyzes the features of representing artificial neural networks in Simulink and SimInTech. Examples of visual schemes (models) built in these modeling environments using neural network blocks are given. The following shortcomings of such representations are the lack of mechanisms: for carrying out structural optimization of neural networks, for combining them into ensembles, for training them synchronously with the simulation of the object model. It was noted that there are difficulties in using other tools, such as specialized Python libraries (Keras, PyTorch, etc.), the NeuroGenetic Optimizer (BioCompSystems) for building neural network control models. A method is shown to implement the representation of neural networks in the formalism of the method of multilevel component circuits, according to which the construction of models of an object and a control system is carried out in a visual language from ready-made blocks (components) with directional and non-directional connections. A technique has been developed for multilevel representation of neural network control models, which allows them to be combined with other tools of the component circuit method. Two options for representing neural networks are proposed: with an encapsulated structure and with a component structure. The first version of the representation is characterized by the compactness of the representation of the control model, the possibility of automated variation and optimization of the structure of the neural network, and the possibility of changing the structure of the network during the executing of the model within a computational experiment (scenario). The second option has the ability to perform detailed debugging and research of the network learning process, and the ability to construct a network of any structural complexity. The paper describes the main developed components with their connections: a neural network, a training block, an ensemble unit (bagging), a block for reading data from a file, a sampling block, a neural network layer (input, hidden, output). A multilevel computer model of the uncontrolled flight of a body (target) and the controlled flight of a projectile is presented as an example to illustrate the operation of the developed components to solve the problem of controlling a projectile to hit the target. The developed component libraries can be used as part of the MARS modeling environment to build multilevel control systems for objects of a multiphysics nature.

The paper proposes a modified method for measuring the refractive index of a triangular prism using an autocollimation angle measuring system designed to measure angles formed by flat surfaces of objects. The method involves using a fixed mirror to reflect the refracted beam, measurement of the angles of incidence of the beam on the face of the prism corresponding to the autocollimation positions and calculating the refractive index of the prism material using the solution of a system of equations. The paper presents the experimental study results for a triangular prism made of K8 optical glass using the proposed method, and a comparison of the results with the readings obtained using the State Primary Standard of the Refractive Index Unit GET 138-2021. The proposed method makes it possible to simplify the process of measuring the refractive index since there is no need to measure the angle of deviation of the beam.

A wide class of smooth continuous dynamic nonlinear systems (control objects) with a measurable state vector is considered. The problem of finding a special function (Lyapunov function), which guarantees asymptotic stability for the presented class of nonlinear systems in the framework of the second Lyapunov method, is posed. It is known that the search for the Lyapunov function is an extremely difficult problem that has no universal solution in stability theory. The methods of selection or search of the Lyapunov function for stability analysis of closed linear stationary systems and for nonlinear objects with explicitly expressed linear dynamical and nonlinear static parts are well studied. At the same time, no universal approaches to finding the Lyapunov function for a more general class of nonlinear systems have been identified. In this paper, we propose a new approach to the search of the Lyapunov function for analyzing the stability of smooth continuous dynamic nonlinear systems with a measurable state vector. The essence of the proposed approach consists in the representation of some function through the sum of nonlinear summands representing the elements of the object state vector multiplied by unknown coefficients. The search for these coefficients is performed using a classical genetic algorithm including mutation, selection, and crossover operations. The found coefficients provide all the necessary conditions for the Lyapunov function (within the framework of the second Lyapunov method). The genetic algorithm approach does not require a training sample which imposes restrictions in the form of the structure of control objects included in it. A new method for finding the Lyapunov function represented as a nonlinear series with known functions multiplied by unknown coefficients is proposed. The effectiveness of the proposed method is demonstrated using computer simulations with a fixed number of iterations and varying population size. The dependence of the number of successfully found Lyapunov functions on the number of iterations of the genetic algorithm has been established. The convergence of the genetic algorithm using Holland’s schemes is analyzed. It is shown that the values of the sought coefficients of the potential Lyapunov function, at each algorithm iteration, approach the coefficients of the Lyapunov function which was also represented as a Taylor series. The method proposed in this paper outperforms known analogs in terms of speed, considers the decomposition of the potential Lyapunov function into a Taylor series with unknown coefficients, instead of using counterexamples or template functions.

This paper studies the trajectory tracking problem and the controller gain adjustment problem for Wheeled Mobile Robots. The controller gain has a great influence on the robot’s trajectory tracking: it can influence both the tracking accuracy and the tracking speed. Therefore, it is very important to choose a suitable control gain during the controller design process. Current neural network gain controllers have a complex structure and require a lot of calculations to find the optimal value. To solve this problem, we design a trajectory tracking controller with a simple structure with adaptive gain by combining the controller with a neural network. The input to this controller is the robot’s attitude error. The controller has no hidden layer and directly outputs the trajectory tracking control law. Firstly, the kinematic controller is designed based on Lyapunov function method to ensure that the robot moves according to the reference trajectory. Then, the online gain adjustment algorithm is designed by using neural network to realize the fast adjustment of the controller gain and ensure the reliability of the controller. Finally, the backstepping method is utilized to design the velocity tracking controller based on the error between the virtual velocity and the actual velocity. Considering the influence of the external environment, we also design a nonlinear disturbance observer to estimate the total disturbance on the robot. We perform simulation experiment in MATLAB. The result of the experiment shows that the control algorithm proposed in this paper can realize the accurate tracking of the robot on the specified trajectory. The gain adjustment algorithm we designed can find the optimal gain value quickly and efficiently, thus improving the stability and efficiency of the controller. The method can be applied to most mobile robot trajectory tracking problems and solves the problem of control gain adjustment.

The progress in the stream of the power electronic converters has led to the expansion of various protection devices for the distribution system. This also has led to an assortment of flexible transmission devices aiming to enhance the stability of the system throughout a variety of power quality issues and, furthermore, for enabling flexible uninterrupted power transmission during turbulences. This paper augments the employment of two Custom Power Devices, namely, Dynamic Voltage Restorer and Distribution Static Compensator for dealing with various power quality issues associated with distributed generation systems. This paper also involves analysis of performance of proposed Custom Power Devices with various algorithms, like gravitational search algorithm, BAT algorithm and ANT colony optimization algorithm for improving the stability of the power system. The proposed system has been tested with various distributed systems, fault conditions, and assessment has been performed among different algorithms in terms of supply voltage, supply current, active power, reactive power, and power factor. The design and analysis of entire system has been executed using MATLAB/Simulink.

The introduction of new types of heat exchangers with phase transitions and the solution of problems of optimizing the design and operational characteristics are a priority within the framework of the energy saving program. Known methods for calculating the heat-hydrodynamic parameters of the flow of refrigerants often do not take into account the specifics of boiling processes at low temperatures as well as in channels with a small flow area. This paper presents the results of modeling heat transfer during the boiling of refrigerants in the channels of evaporators of heat and cold energy complexes, taking into account the true flow parameters. The proposed mathematical model of the boiling of the working substance in channels of various shapes is based on the true flow parameters which imply knowledge of the channel cross-sectional areas occupied by each of the phases. The value of the true volumetric steam content provides the most correct modeling of two-phase flows in a wide range of regime and geometric parameters. The paper uses the equations of material and heat balance in combination with the equation of heat transfer from the environment to the boiling refrigerant. The map of flow regimes is used as an empirical component. A program has been developed for calculating the proposed system of equations which is solved iteratively at each time step using the finite volume method. Comparison of calculation results with experimental data on models of round and rectangular channels with intracanal boiling of refrigerants at positive and negative saturation temperatures is performed. It is shown that the calculation error does not exceed 10 % for a round and 20 % for a rectangular flow section. The verification results showed the possibility of using the model in the framework of engineering calculations. The proposed mathematical model can be used as the basis for the calculation programs for existing evaporators and for the creation of new types of heat exchangers with in-tube boiling of the working substance. The proposed method allows optimizing both geometric and thermal-hydrodynamic parameters.

The article discusses the experimental and theoretical results of studying the optical properties for planar quartz surfaces modified with gold nanostars using Surface-Enhanced Raman Spectroscopy (SERS). Surfaces functionalized with nanoparticles, prototypes of optical sensors, have been obtained. It is shown that the obtained experimental Raman signal enhancement from the dye rhodamine 6G (R6G) can be at least order 104. The paper presents the results of calculation, synthesis and study of the optical and morphological properties of such structures. A complex method for creating and evaluating the optical properties of planar nanostructures and evaluating their optical properties is proposed. The method includes the approaches based on mathematical modeling using finite differences in the time domain (FDTD - Finite- Difference Time-Domain) evaluating electromagnetic field strengths near the surfaces of star-shaped gold nanoparticles in colloidal solutions and on a silicon surface. During the simulation, we studied such parameters as the particle size, the wavelength of the exciting radiation, and the dependence of the effective amplification of the electromagnetic field on the morphological parameters of the star. The theoretical approach to the calculation of such structures is shown to be promising for the subsequent chemical synthesis described in the article and the study of optical properties. In this paper, a theoretical calculation of the parameters of the electric field and optical properties is carried out near star-shaped nanoparticles of varying sizes. These parameters were calculated by the finite difference method in the time domain. The task included finding the optimal sizes of nanoparticles as well as varying the thickness of the surface layers in order to obtain the maximum scattering values and further use of similar structures in SERS experiments. Also, within the framework of this work, syntheses of star-shaped nanoparticles was carried out, their optical and morphological properties were studied, quartz surfaces were functionalized with nanoparticles, and sensor prototypes were created in order to further assess the Raman signal amplification. Based on the results of mathematical modeling, the optimal sizes for the synthesis of star-shaped NPs were determined. Synthesis of NPs was carried out by a chemical method using seeds. To carry out the experimental part, SERS spectra were obtained using Centaur U spectrometer (OOO NanoScanTechnology, Russia). The spectrometer was equipped with three different laser sources: 632.8 nm He-Ne laser (17 mW), 532 nm and 473 nm DPSS lasers (45 mW). During the experiment, a He-Ne laser with λ = 632 nm and DPSS laser with λ = 532 nm were used to match the excitation of the plasmon maximum for the nanostars. The optical scheme of the spectrometer included an Olympus BX41 microscope (Olympus, Japan) with a 100× objective (NA 0.9) for positioning the beam and collecting scattered photons. The monochromator of the spectrometer had a focal length of 800 mm. A holographic diffraction grating of 300 gr/mm, and was equipped with a 1024×256 pixel thermoelectric cooled CCD detector (Andor Tech., UK). As a result of the paper, surfaces functionalized by nanoparticles were obtained prototypes of optical sensors. It is shown that the experimental gain of the Raman signal can be no less than 104 times. The presented data will serve as the basis for obtaining the required sizes of nanostars in the methods of the controlled chemical synthesis of colloidal nanoparticles. The results presented in the article can be applied to the development of biocompatible and highly sensitive optical sensors based on the effect of Raman scattering of light.

Residual amplitude modulation in LiNbO3 phase modulator is one of the key factors that limit the accuracy of highsensitive fiber-optical sensors. A fiber-optical gyroscope is an angular velocity sensor whose sensitivity is better than 0.001 °/h. Optical light intensity changes after phase modulator is a reason for wrong phase difference that introduces an error in the angular velocity signal. Most existing residual amplitude modulation suppression methods are based on reduction of back reflections between an optical fiber and integrated optical waveguide, absorbing groove to suppress or reduce reflection on the bottom face, and algorithmic compensation. In this paper, new approach to reduce residual amplitude modulation in LiNbO3 for fiber optical gyroscope application is presented. Method feature is an application of additional differential signal modulation with uniform amplitude distribution in the input signal voltage range of the phase modulator. The proposed method allows to suppress residual amplitude modulation of the multifunctional integrated optical circuit phase modulator more than 3 times using additional triangle signal modulation with the frequency f = 200.09 MHz and power P = 36 dBm. This method is suitable for improving fiber optical gyroscope accuracy. Moreover, it could be applied for any fiber-optic sensors based on LiNbO3 phase modulator. The paper will be of interest to specialists in the field of highly sensitive fiber optical sensors, fiber, and integrated optics.

Monitoring systems of automated production lines requires high accuracy and processing speed of acquired data. Nowadays digital twin technologies are rapidly developing as part of Industry 4.0. Digital twin is a virtual representation of a physical system that mimics the behaviour of a real object, and is used in real-time control, monitoring and disturbance prediction, without influence on a real object. Digital twin is being used in many fields of application such as: healthcare, manufacturing, education, city development, etc. Nonetheless, despite the rising popularity of digital twin researches, there is no basic approach to digital twin synthesis. Mostly, in state of the art articles, systems with known parameters are considered. In this paper, approach to development of digital twin, based on internal model for multi-link mechanisms with unknown motion parameters, is presented. Described approach intended to ease the task of production line monitoring and extend digital twin technology application field. In this article adaptive controller design, based on internal model, is presented, theoretical explanations of design process are provided, and application of the control algorithm in the task of multi-link mechanism motion trajectory parameters estimation is described. In this work the experimental synthesis of digital twin for Kuka youBot manipulator based on presented approach in real time is described. Adaptive motion parameters estimation was made for two links of the manipulator for the sake of confirmation of presented approach usage in the task of digital twin for systems with unknown parameters. Motion trajectory parameters of the robot links were defined manually with the use of chaotic generators to estimate the accuracy of the adaptive control system. This approach was used in order to calculate the error of reference tracking by comparing the control signal for digital twin with the reference exogenous input for manipulator links. As the results of the experiment, the graphs of the error reference tracking for both links are presented. As the goal of the experiment, the convergence of reference tracking error to the field [–0.005, 0.005] radian was set, and as can be seen from graphs this goal was achieved. There is a small disturbance of the error convergence on the graphs within the desired field which was caused by noised measurements and delays of the chosen modelling environment real-time simulations. In the conclusion of this paper, further work to reduce the influence of mentioned causes is described. The results of this paper can be used in further digital twin technology researches. Presented approach can be implemented in robots remote control tasks, production lines technical condition monitoring and in general motion trajectory parameters estimation tasks for multi-link mechanisms.

This paper discusses the effect called undesirable extrusion dynamics that occur during material extrusion printing or fused deposition modeling. This effect is revealed during high acceleration and deceleration of printing head and results in over-extrusion on corners of parts and printed layer unevenness. To minimize this effect, modern control systems use Advance algorithms for dynamics control. However, such disadvantages as inertia, reduced printing speed, need for manual calibration, as well as uncertainty of the influence of material, nozzle geometry, and printing process parameters on the algorithm performance do not allow this group of solutions to be applied on an industrial scale. The paper presents a study of the influence of printing characteristics, such as material type, extruder type, printing temperature, layer and nozzle geometry, on extrusion dynamics through a series of experiments. The experiments were carried out on a Creality Ender 3 printer. The obtained experimental data will allow us to deepen the understanding of extrusion dynamics influence in FDM printing; they are also used in the present study to modify existing dynamics control algorithms. The paper proposes a modification of Advance algorithm based on machine learning. It is proposed to implement an algorithm containing two trained neural network models. One model predicts changes in printing head motion to minimize residual defects and increase average print speed. The second model predicts the compensation parameter for specific printing conditions without the need for manual calibration. A neural network model was trained to determine the compensation parameter depending on the type of material, layer thickness, nozzle geometry and printing temperature. The model was trained based on experimental data. The developed algorithm was introduced into Linear Advance algorithm of the Marlin firmware and tested on Creality Ender 3. The experiments showed that the developed model can successfully predict the required compensation and can be applied during the printing process. The proposed algorithm helps to facilitate and automate the process of extrusion dynamics compensation which will expand the possibilities of FDM printers’ application in industrial conditions. The obtained algorithm can improve the accuracy and printing speed which will subsequently help to increase the economic independence and competitiveness of small design organizations and enterprises in Russia that use 3D printers. This research expands the possibilities of rapid prototyping and may help to ensure the rapid creation of pilot batches.

The paper deals with the problem of acoustic events classification which is actively applied to the problems of a safe city, smart home, IoT devices, and for the detection of industrial accidence. A solution to improve the accuracy of classifiers without changing their structure and collecting additional data is proposed. The main data source for the experiments was the TUT Urban Acoustic Scenes 2018, Development Dataset. The paper presents the way to increase the accuracy of audio event classification by using the FN-corrector. The FN-corrector is a linear two-stage classifier performing the transformation of the feature space into a linearly separable space and the linear separation of one class from another. If a corrector is applied, the responses of the original classifier generate four classes: positive (P), negative (N), false positive (FP), and false negative (FN). As a result, it becomes possible to train two types of correctors: the FP-corrector separating positive and false positive classifier responses, and the FN-corrector separating negative and false negative classifier responses. In the experiments, the VGGish convolutional neural network was used as the initial classifier. The audio signal is converted into a spectrogram and is fed to the input of the neural network which forms the spectrogram feature description and performs a classification. As an example, two ”confused“ classes are selected to demonstrate the increase in classification accuracy. Using the feature description of audio recordings of these classes, an FN-corrector was built, trained and connected to the original classifier. The response from the classifier, as well as the feature description, has been passed to the corrector input. Next, the corrector translated the feature space into a new basis (into a linearly separable space) and classified the classifier answer responding to the question whether the original classifier makes a mistake on such a feature vector or not. If the original classifier made a mistake, then his answer is changed by the corrector to the opposite, otherwise the answer remains the same. The results of the experiments demonstrated a decrease in the level of class confusion and, accordingly, an increase in the accuracy of the original classifier without changing its structure and without collecting an additional data set. The results obtained can be used on IoT devices that have significant limitations on the size of the models used, as well as in solving the problems of domain adaptation which is relevant in audio analytics.

In current digital climate, education sector is evolving as the computer technology advances. Education is being digitized: online classes, online examination methods are conducted, etc. During examination, students are assessed by their answers having given for the question set by a teacher. Today many tools are available to assess the performance of a student using multi choice questions tools which provide instant evaluation, but there are available very limited and operational tools where subjective type answer of students are evaluated. This paper presents a web-based application to address this challenge. It automates the process of subjective answers checking and generates results through using natural language processing methods, like keyword matching semantic, lexical analysis and cosine similarity. Experiments show that appreciated by the teacher result and the system estimation does not have much difference which signifies that the system evaluates answers with a 97 % accuracy. The presented system not only reduces manpower but also eliminates the traditional method of conducting exclusively subjective exams using paper documents. It also eliminates the delays in the paper checking, result generation process. The cases of information leak are being reduced and the objectivity of the assessment is being increased.

The paper presents a methods of image local features extraction research in relation to people authentication problem by face thermogram. As a part of the study, there were formed two datasets for methods training and testing: photographic images and face images in the long-wavelength infrared specter (LWIR) with various factors. The novelty of this study is due to the approach to collecting datasets to verify the accuracy of authentication methods. The dataset was collected with more realistic conditions that affect the quality of authentication, such as changing facial expressions, wearing glasses, medical masks, applying make-up/cosmetics, changing the lighting and temperature conditions of the environment, rotating the head. The methods core is based on the idea of constructing a vector of image features while reducing the dimension and highlighting the boundaries. Likewise, the methods of this group cope well with extracting features task on images and are widely used in the tasks of authentication by 2D face image, as well as in other computer vision tasks. In this paper, four classical methods of local feature extraction are considered: the method of locally binary templates, Gabor wavelets, scale-invariant feature transformation, and Weber’s local descriptor. The classifiers for the feature vectors comparison in this research are SVM and the simplest Perceptron — the basic methods of machine learning. As part of the study, a comparative analysis of each method was carried out in relation to the collected datasets. The methods were trained and tested on a collected face dataset of over 632,000 images of 152 people. As a result of the comparative analysis, it can be concluded that the method of local binary features demonstrates the best result among the considered methods for both types of data: for face thermograms (for SVM — 0,57, for Perceptron — 0,58), for photographic images (for SVM — 0,71, for Perceptron — 0,73). Furthermore, the SIFT method showed similar results: for face thermograms (for SVM — 0,58, for Perceptron — 0,55), for photographic images (for SVM — 0,72, for Perceptron — 0,74). Gabor filters and Weber local descriptor application demonstrate a low accuracy rate in the authentication task by both types of data. The results of the work can be used in access control and management systems to increase the fault tolerance of person authentication. The appliance of the considered methods are effective in the tasks such as processing thermograms for authentication a person by so-called “secondary” signs, for example, by the veins and vessels on face patterns, in cases of facial expressions and appearance changes.

Problems of Wireless Sensor Networks (WSN) are associated with a significant increase in the number of devices on these networks. In this regard, the requirements for the protection and the security of WSN from external influences are increasing significantly. WSN security problems are solved by solving the problem of optimal path routing, energy conservation, and so on. This paper proposes a hybrid model of an efficient packet routing and delivery system to prevent Black-hole attacks. This type of attack is considered the most common on the network due to its unique characteristics. To detect such attacks, a deep learning model using a Convolutional Neural Network (CNN) is proposed. The learning algorithm must be reliable and trustworthy so that attack analysis can be considered at different levels to study the intelligent behavior of network attacks. The paper considers the problem of finding the optimal shortest path using Deep Q-Learning and convolutional neural networks to perform efficient routing and delivery of packets in a safer way. As a result of simulation, the achieved accuracy reached 98.57 %.

This paper examines routing algorithms for networks on a chip (NoC). An analysis of existing routing algorithms is provided; their limitations and areas of application are highlighted. The algorithms were evaluated taking into account the requirements of specific applications and architecture features. The results of comparing the performance of the considered algorithms are presented. Analysis and comparison of various routing algorithms for NoC are carried out taking into account critical characteristics. The main attention is paid to such routing algorithms as the deterministic XY algorithm, the model rotation algorithm, congestion-aware routing, fault-tolerant routing, Quality of Service routing, and the ant colony algorithm. It is shown that the choice of routing algorithm should be based on the specific requirements and conditions of use of the network. The importance of adapting to a variety of conditions and tasks that NoC users and developers may encounter is shown. Based on data from existing studies, an analysis of algorithms was carried out based on several key indicators: latency, throughput, adaptability, fault tolerance and implementation complexity. The strengths and weaknesses of each algorithm are identified in various use scenarios and under different network loads. It is shown that the choice of a routing algorithm should be based on the specific requirements and conditions of use of the network, as well as on the balance between performance, adaptability, fault tolerance and implementation complexity. The study contributes to the understanding of the effectiveness of various routing algorithms in NoC, providing recommendations for their selection depending on the specific application requirements and system architecture. The study contributes to a better understanding of the impact of routing algorithms on the overall performance of NoC, suggesting directions for further improvements in this area. The results of the work can be applied in the design and development of highperformance multiprocessor systems on a chip where efficient data routing between various systems components is a key factor in ensuring high performance. The importance of developing fault-tolerant routing algorithms that can ensure the continuity of system operation in the event of failures of individual components or units is emphasized. This is especially important for mission-critical applications where service continuity and reducing the risk of data loss are top priorities.

The paper presents the results of an experimental study of the possibility of using a narrow-band semiconductor distributed feedback laser diode used as a source and detector of optical radiation to detect the spectral response from a fiber Bragg grating. The DFB laser “LDI-1550-DFB-2.5G-20/70” from the company “Laserscom”, mass-produced on the Russian market and having standard characteristics, was chosen as the laser diode under study. To sweep the central wavelength of a semiconductor distributed feedback laser diode in the range 1549.5–1552 nm, direct pulse current modulation was used with a frequency of 100 kHz, a duty cycle of 40, and a current value of 1 A per pulse. The radiation reflected from the fiber Bragg grating corresponding to the central Bragg wavelength was recorded as a change in voltage at the anode and cathode of the laser diode due to the photoelectric effect in the laser diode. An experimental assessment of the optoelectronic parameters of a laser diode in photovoltaic and short-circuit modes was carried out: dark current, bandwidth and spectral sensitivity. The evaluation was carried out at a temperature of 25 °C. A measuring circuit has been created to detect the response from a fiber Bragg grating based on direct pulse current modulation and the photovoltaic mode of a semiconductor distributed feedback laser diode. It is shown that the photovoltaic mode of the laser diode is applicable to problems of recording optical radiation. The amplitude-frequency characteristic of a laser diode in the photovoltaic mode was experimentally obtained depending on the forward bias voltage. It is experimentally found that the –3 dB bandwidth is 300 MHz and the maximum sensitivity is 0.1 A/W in short-circuit mode, and the amplitude response is linear in the wavelength range from 1540 to 1560 nm. For the laser diode under study, the reverse branch of the current-voltage characteristic was experimentally obtained and the dark current at zero bias of the laser diode is 12.5 pA. The demonstrated method of FBG interrogation can be used for miniaturization and simplification of optical devices for fiber Bragg grating interrogation. The obtained results may be useful to specialists in fiber optic sensors, system for interrogation and processing signals from fiber optic sensors.

One of the problems solved by developers of autonomously controlled unmanned aerial vehicles is the task of determining by the drone its exact position over the terrain without the help of global satellite navigation systems. The existing mass-dimensional and energy limitations for small-sized drones lead to the necessity of using relatively simple algorithms in drone computing devices. The paper considers methods of navigation of unmanned aerial vehicles using computer vision implemented by on-board optical and computing devices. Machine vision implemented by on-board computing devices provides autonomy of small-sized aircraft in the absence or unstable communication channel with the control center and/or satellite navigation system. The proposed algorithm solves the problem of identifying an area of terrain observed from a drone with a terrain image stored in the memory of the drone control system. The drone location is determined by the minimum (maximum) value of the discrepancy between the observed current image and the image of the terrain area stored in the drone memory device. The solution of the identification problem is based on the concept of immunocomputing using singular value decomposition of the feature matrix of the identified objects. This approach allows providing high quality indicators of identification due to decomposition of the feature matrix into three simple transformations for transition to a new feature space which is not identifiable, but whose components are statistically significant. The quality indicators of the developed algorithm were evaluated in comparison with the known method of image identification by calculating the correlation function between two arrays of features. A series of tests were carried out in which the probability of correct location determination and the speed of the algorithms were evaluated for the same initial data. It is shown that when pre-preparing a “reference” image stored in the drone memory device, the speed of the developed method exceeds the speed of the method based on the calculation of the correlation function of the compared images by an order of magnitude. The mean absolute error of correct positioning using the proposed method ranges from 0.109 to 0.153. The proposed algorithm can be used by developers of navigation systems for small-sized unmanned aerial vehicles due to its low resource requirements while maintaining a level of accuracy sufficient in the context of solving problems of orientation on the terrain. Devices realizing the proposed orientation algorithm have better energy and mass-size characteristics.

As neural networks become more complex, the number of parameters and required computations increases, which complicates the installation and operation of artificial intelligence systems on edge devices. Structural distillation can significantly reduce the resource intensity of using any neural networks. The paper presents a method for optimizing neural networks that combines the advantages of structural distillation and a genetic algorithm. Unlike evolutionary approaches used to search for the optimal architecture or distillation of neural networks, when forming distillation options, it is proposed to encode not only the parameters of the neural network, but also the connections between neurons. The experimental study was conducted on the VGG16 and ResNet18 models using the CIFAR-10 dataset. It is shown that structural distillation allows optimizing the size of neural networks while maintaining their generalizing ability, and the genetic algorithm is used to effectively search for optimal distillation options for neural networks, taking into account their structural complexity and performance. The obtained results demonstrated the effectiveness of the proposed method in reducing the size and improving the performance of networks with an acceptable loss of quality.

The main problem in building intelligent systems is the lack of data for machine learning, which is especially important for sign language recognition for the deaf and hard of hearing. One of the ways to increase the amount of data for training is synthesis. Unlike speech synthesis, it is impossible to create a sequence of gestures in Vietnamese and some other languages that exactly repeat the text. This is due to the significant limitations of the gesture dictionary and the different word order in sentences. The aim of the work is to enrich the educational corpus of video data for use in creating recognition systems for the Vietnamese Sign Language (ViSL). Since it is impossible to translate the words of the source text into gestures one to one, the problem of translating from a regular language into a sign language arises. The paper proposes to use a two-phase process for this. The first phase involves pre-processing the text with standardization of the text format, segmentation of words and sentences, and then encoding the words using the sign language dictionary. At this stage, it should be noted that there is no need to remove punctuation marks and stop words, since they are related to the accuracy of the N-gram model. Next, instead of using syntactic analysis, a statistical method for forming a sequence of gestures is used, and the Markov model on the transition graph between words is taken as a basis in which the probability of the next word depends only on the two previous words. Transition probabilities are calculated on the existing marked corpus of the ViSL. The Breadth-first Search method is used to compile a list of all sentences generated based on a given grammatical rule and a matrix of semantic interactions between words. The inverse of the logarithm of the product of the probabilities of co-occurrence of consecutive 3-word phrases in a sentence is used to estimate the frequency of occurrence of that sentence in a given data set. Based on the ViSL data of 3,234 words, we calculated probability matrices representing the relationships between words based on Vietnamese natural language data with 50 million sentences collected from Vietnamese newspapers and magazines. For different grammar rules, we compare the number of generated sentences and evaluate the accuracy of the 50 most frequent sentences. The average accuracy is 88 %. The accuracy of the generated sentences is estimated by manual statistical methods. The number of generated sentences depends on the number of word parts that are labeled according to the grammar rules. The semantic accuracy of the generated sentences will be very high if the search words are labeled with the correct part-of-speech tagging. Compared with machine learning methods, our proposed method gives very good results for languages without inflections and word order that follow certain rules, such as Vietnamese, and does not require large computational resources. The disadvantage of this method is that its accuracy largely depends on the type of word, sentence, and word segmentation. The relationship of words depends on the observed dataset. Future research direction is to generate paragraphs in sign language. The obtained data can be used in machine learning models for sign language processing tasks.

Detection and handling of anomalous behavior in the network systems are peremptory efforts to ensure security for vulnerable infrastructures amidst the dynamic context of cybersecurity. In this paper, we propose an ensemble machine learning model architecture that leverages the strengths of XGBoost, Gradient Boosting, Random Forest, and Support Vector Machine models to identify anomalies in the dataset. This method utilizes an ensemble of these models with weighted voting based on accuracy to enhance anomaly detection for robust and adaptive real-world network security. The proposed ensemble learning model is evaluated on standard metrics and demonstrates exceptional efficacy, achieving an impressive accuracy of 99.68 % on NSL KDD dataset. This remarkable performance extends the model prowess in discerning anomalies within network traffic showcasing its potential as a robust tool for enhancing cybersecurity measures against evolving threats.

Multiple Sclerosis (MS) is a progressive autoimmune disease affecting the central nervous system, causing communication disruptions between the brain and the body. Early and accurate detection of MS lesions in brain Magnetic Resonance Imaging (MRI) scans is crucial for effective treatment. This paper proposes MSNet, a deep learning-based approach for automatic detection and diagnosis of MS lesions from MRI images, leveraging Convolutional Neural Networks (CNNs) for precise lesion identification and classification. Our methodology involves a comprehensive analysis of MRI datasets, including preprocessing steps such as normalization and lesion segmentation. We propose a novel CNN architecture tailored for MS lesion detection, achieving an accuracy rate of 98.2 % on the test dataset. By incorporating advanced image recognition techniques, our system classifies MS lesions from diverse brain pathologies present in MRI images. The model also highlights MS lesions within the MRI images, aiding neuroradiologists in accurate diagnosis and treatment planning. This study contributes significantly to improving MS diagnosis by providing a reliable and automated tool for lesion detection and classification.

Intelligent systems demand interaction with a variety of complex environments. For example, a robot might need to interact with complicated geometric structures in an environment. Accurate geometric reasoning is required to define the objects navigating the scene properly. 3D reconstruction is a complex problem that requires massive amounts of images. The paper proposes producing intelligent systems for 3D reconstruction from single 2D images. Propose a learnable reconstruction context that uses features to realize the synthesis. Proposed methods produce encoding feature lable input to classification, pulling out that information to make better decisions. Binary Classifier Neural Network (BCNN) classifies whether a point is inside or outside the object. The reconstruction system models an object 3D structure and learns feature filter parameters. The geometry and the corresponding features are implicitly updated based on the loss function. The training doesn’t require compressed supervision to visualize the task of reconstructed shapes and texture transfer. A point-set network flow results in BCNN having a comparable low memory footprint and is not restricted to specific classes for which templates are available. Accuracy measurements show that the model can extend the occupancy encoder by the generative model, which doesn’t request an image condition but can be trained unconditionally. The time required to train the model will have more neurons and weight parameters overfitting.

Malware analysis is the process of dissecting malicious software to understand its functionality, behavior, and potential risks. Artificial Intelligence (AI) and deep learning are ushering in a new era of automated, intelligent, and adaptive malware analysis. This convergence of AI and deep learning promises to revolutionize the way cybersecurity professionals detect, analyze and respond to malware threats. This paper proposed a Deep Neural Network (DNN) model built from features selected by ANalysis Of Variance (ANOVA) F-test (DNN-ANOVA) to increase accuracy by identifying informative features. ANOVA is a feature selection method used for numerical input data when the target variable is categorical. The top k most relevant features are those whose score values are greater than a certain threshold equal to the ratio between the sum of all features scores and the total number of features. Experiments are conducted on CIC-MalMem-2022 dataset. Malware Analysis is performed using binary classification to detect the presence or absence of malware and multiclass classification to detect not only the malware but also its type. According to the test results, DNN-ANOVA model achieves best values of 100 %, 99.99 %, 99.99 %, and 99.98 % in terms of precision, accuracy, F1-score and recall respectively for binary classification. In addition, DNN-ANOVA outperforms the current works with an overall accuracy rate of 85.83 %, and 73.98 % for family attacks and individual attacks respectively in the case of multiclass classification.

The paper proposes a mathematical model of the epidemic process, taking into account the dependence of the rates of cure and loss of immunity on time. Today, mathematical models of epidemics based on the basic Kermack–McKendrick model have become widespread. The most famous models are Susceptible-Infected-Recovered (SIR) and Susceptible-Exposed-Infected-Recovered (SEIR). These models are based on dividing the population into separate groups that are in different epidemic conditions. The description of the models is based on differential equations similar to the equations of birth and death in the process of radioactive transformations of elements in a radioactive chain. However, this approach does not take into account the dependence of the probabilities of the transition of the population from group to group on time spent in the treatment process and in the process of loss of acquired immunity. The known models do analyze the nature of the course of the epidemic for long periods of time, when the process can enter a stationary state. The paper proposes a mathematical model based on dividing the population into separate groups. The first group consists of healthy people susceptible to infection due to contact with members of the second group, which includes the infected population. Members of the third group are being treated, the fourth group includes members of the society who have recovered with antibodies and are vaccinated. The fifth group consists of deceased members of society. In contrast to the SIR and SEIR models, the proposed approach takes into account that immunity is lost over time, and people who survived again move to the group susceptible to infection. The dependences of the probabilities of transition from group to group on time spent both during the treatment process and in the loss of acquired immunity have been taken into account. Thus, the proposed mathematical model is based on five integro-differential equations, two of which are partial differential equations. A new mathematical model has been formulated that makes it possible to take into account the dependence of the cure rate and the probability of transition from the vaccinated state to the initial state on time spent in the corresponding state. It is shown that the proposed model is autocatalytic. With increasing time, a state of bistability is observed, when, under the same boundary conditions, two stable states coexist. Switching between states is determined by the epidemic spread control parameter found in the work. One of the stable states is stationary and leads to the end of the epidemic, the second one leads to the population’s extinction. It has been shown that, for the stationary regime, the form of the distribution function in terms of treatment time and exposure time in the vaccinated state does not affect the final result in any way. The conditions for suppressing the epidemic for managing the process of its development are formulated. Numerical experiments were carried out to simulate the epidemic spreading process, taking into account the constancy of all transition probabilities. Integration of the original system of equations was carried out using the Radaus algorithm for stiff differential equations. The results of numerical simulations have confirmed that the experimental data agree with the theory of the control parameter. The results of the work can be used to organize the management of the epidemic spreading process in order to suppress it as soon as possible by changing the value of the control parameter.

The paper considers the optical systems of virtual reality, which use an optical wedge to eliminate or reduce the conflict of convergence and accommodation (vergence- ccommodation conflict, VAC). The inclined surfaces of the optical wedge introduce specific aberrations into the optical system that can critically affect image quality. The authors analyzed wedge aberrations using relationships derived from the third-order aberration theory. In contrast to existing approaches, the relationships were obtained without the assumption of a small refractive angle or wedge thickness. The results demonstrate that in the scheme with an optical wedge, with characteristics typical for virtual reality systems, the dependence of astigmatism on the angular field is linear and significantly exceeds the other types of aberrations. It is shown that to obtain a high-quality image, the most successful compensation for such aberration can be achieved by using diffractive optical elements (kinoforms) or a Fresnel lens. The paper gives examples of optical systems with a wedge, developed taking into account the requirements for the overall dimensions of virtual reality systems and image quality. The obtained ratios for the aberrations of the optical wedge and the inclined surface make it possible to evaluate the correction capabilities of schemes where such elements are used with arbitrary characteristics. The proposed solutions can be used in virtual reality systems with a reduced value of vergence-accommodation conflict.

The paper considers the problem of controlling the movement of a mobile robot along a given smooth trajectory without measuring its position coordinates. To solve the problem, an adaptive observer of the local coordinates of a moving object is used by measuring the linear speed, yaw angle, and range to a beacon with known coordinates. Then the minimum distance from the robot to the given smooth trajectory is determined. Based on the estimates for the coordinates of the robot and the distance to the curve, we synthesized the control law of the movement along the trajectory with the desired speed under the conditions of uncertainty of the mathematical model. The motion control algorithm is based on the robust sequential compensator method, which ensures that the deviations of the robot from a given trajectory are limited. The proposed coordinate observer ensures asymptotic convergence of the estimation errors to zero. In this paper, we propose two algorithms for determining the minimum distance from the robot to the trajectory: an exact analytical calculation and a nonlinear observer that guarantees the convergence of the estimate to the true value in an arbitrarily short time. The trajectory regulator ensures the movement of the robot along a given trajectory with a limited error. The application of the proposed approach allows one to solve the issues of controlling the movement of a mobile robot without measuring the position coordinates. The approach can be widely applied for controlling self-driving vehicles when they run in tunnels or under a bridge, where it is not possible to measure their coordinates using the satellite navigation systems (GLONASS or GPS).

The development of the medical imaging field is becoming a significant challenge due to the growing need for automated, fast, and efficient diagnostics. Traditionally, blood cells are counted by using a hemocytometer along with other laboratory equipment and chemicals, which is a laborious task. The paper investigates the application of machine learning methods to the identification and classification of blood cells, which allow increasing the recognition rate without deteriorating quality. A comparative analysis of methods for solving the problem of recognizing blood cells based on artificial intelligence approaches is carried out. The paper uses support vector machine, k-nearest neighbors’ algorithm, deep learning (convolutional neural network), and forward propagation neural network. A set of images with cell samples was selected as the initial data for comparison. A comparative analysis of the quality of the considered algorithms was performed on a set of training data with more than 3000 images. It is shown that a program that implements artificial intelligence methods provides a cell recognition time within 4-6 seconds when using an office personal computer, which is significantly less than the time spent by medical workers on one study of a biomaterial. The implementation of the presented results makes it possible to automate the process of studying a biomaterial, reduce the time for conducting and obtaining the result of the analysis of whole blood cells (identification and counting), lessen the influence of operator errors on the result, unload computing resources, thereby increases the efficiency of digital medicine.

A heuristic approach to optimization of complex physicochemical processes in the form of a genetic algorithm for solving problems is presented. In comparison with other evolutionary methods, the genetic algorithm allows working with large search spaces and complex evaluation functions, which is especially important in the study of multifactor physicochemical systems. Due to the relatively high need for computing resources when working with large and complex search spaces, optimization of existing calculation organization schemes has a positive effect on the accuracy of the calculated results. The paper presents a modified genetic algorithm that minimizes the number of iterations to achieve a given accuracy when solving the problem of finding the optimal composition of the initial reaction mixture. For a complex physicochemical process, an optimization problem is formulated which consists in finding the composition of the initial reaction mixture that promotes maximization (or minimization) of a given target parameter. The optimality criterion is determined by the type of the problem being solved and, when organizing calculations, is focused on the maximum yield of the target product. The main steps of implementing the genetic algorithm include creating an initial set of solutions and subsequent iterative evaluation of their quality for subsequent combination and modification until optimal values are achieved using mechanisms similar to biological evolution. To improve the efficiency of the method and reduce the number of iterations, a modification of the genetic algorithm is proposed which boils down to a dynamic estimate of the “mutation” parameter, depending on the diversity of individuals in the formed population of solutions. In a series of computational experiments, an analysis was made of the influence of the genetic algorithm parameters on the accuracy and efficiency of solving the problem using the example of studying the kinetics of the Michaelis-Menten enzymatic reaction. The results of calculations to determine the optimal composition of the reaction mixture showed that the dynamic determination of the “mutation” parameter contributes to an increase in the accuracy of the problem solution and a multiple decrease in the relative error value reaching 0.77 % when performing 200 iterations and 0.21 % when performing 400 iterations. The presented modified approach to solving the optimization problem is not limited by the type and content of the studied physicochemical process. The calculations performed showed a high degree of influence of the “mutation” parameter on the accuracy and efficiency of the problem solution, and dynamic control of the value of this parameter allowed increasing the speed of the genetic algorithm and reduce the number of iterations to achieve an optimal solution of a given accuracy. This is especially relevant in the study of multifactorial systems when the influence of parameters is non-trivial.

This paper considers the problem restoring a non-stationary heat flow from a coolant to a heat-storing substance of a separate element of a thermal accumulator. Solving the problem allows avoiding errors associated with averaging the heat flow over all battery cells, and provides the opportunity to find the optimal sizes and composition of the filler for each battery cell. The problem is especially relevant for cascade batteries where cells with different fillers are simultaneously used. A comparison is made of two methods for solving the problem. The first method is based on numerical simulation of the thermal energy storage discharge process using the Computational Fluid Dynamics software package. The second approach proposed by the authors is based on the parametric identification of a differential-difference model of heat transfer with the solution of the inverse problem of heat conduction together with coefficient smoothing calculation. The proposed method makes it possible to smooth out abruptly changing thermophysical characteristics and take into account the moving phase boundary of a substance. The method for solving the inverse heat conduction problem can significantly reduce the recovery time of non-stationary boundary conditions of heat transfer for the entire battery and, thus, reduce the requirements for computing resources when designing and optimizing the battery by facilitating experimental search. For the first time, the use of the method of parametric identification and calculation of smoothing coefficients for solving the Stefan problem was considered and proposed. The results obtained can be used to calculate the heat flow from an individual element of a thermal energy accumulator.

This paper addresses the problem of optimizing the use of temporal resources of a radar station (RS) under limited time resources. Special attention is given to the necessity of considering a multitude of compensatory optimality criteria when allocating the RS operating time. The proposed approach is based on the use of fuzzy set theory which represents an innovative solution in the context of this task. The task of managing the RS temporal resources is formulated as the search for an optimal work schedule among all potentially possible options. This schedule should minimize the values of all partial optimality criteria. Fuzzy set theory is applied to solve this problem, allowing for the consideration of uncertainty and variability in task execution conditions. An algorithm for managing the RS temporal resources was presented. The review results confirm the probable increase in efficiency, especially in conditions of acute shortage of temporal resources, ensuring their optimal distribution among current tasks. Furthermore, the algorithm enables decisions to be made about the possibility of performing special or additional tasks without compromising the main monitoring functions. The review of the proposed algorithm provides a basis for hypothesizing its advantages over traditional methods of managing the RS temporal resources. In particular, the use of fuzzy set theory allows for more flexible responses to changes in task execution conditions and enhances the overall adaptability of the system. In the future, this approach could be adapted and applied in other areas where there is a need for resource optimization under conditions of limitation and uncertainty of external factors.

More recently, permanent magnet synchronous motors (PMSM) have been widely applied to power source for different applications, such as high precision tracking systems, standalone electric and unmanned aerial vehicles, industrial robotics, marine propulsions, etc., due to their advantages to conventional competitors for example induction motor with squirrel-cage rotor. In order to reduce the cost of AC drive, the removal of the mechanical sensors is required. Hence, the sensorless motor control is more preferable and is based on electrical measurements, namely, three-phase currents and voltages. The paper is devoted to sensorless field-oriented control design procedure for a non-salient PMSM. The proposed control strategy is employed on full order sliding-mode observer which provides the output insensitivity to parameter changes and disturbances. In order to reduce th e high-frequency chattering and enhance the rotor position estimation quality and accuracy, the band-pass filter with tracking of the central frequency to the speed reference is applied. To obtain actual information about the unmeasurable rotor position and speed, the phase-locked loop with cascade connection of adaptive proportional-integral controller is employed. A simulation of the dynamic starting mode of a PMSM under zero initial conditions has been performed by MATLAB/Simulink environment. As can be concluded from the simulation results, proposed sensorless field-oriented control strategy provides quick response as well as low rotor position estimation error both transient and steady-state behavior. The research significance of proposed PMSM sensorless field-oriented control strategy is to provide the wide range of motor speed operation, strong robustness of estimated rotor position to parameter perturbations as well as quality suppression of high frequency chattering effect owing to the switching attribute and the internal control discontinuity of sliding mode, whereby the practical application of a sensorless variable-frequency synchronous electric drive is expanding.

The primary idea of this paper is the implementation of the Optimal Key-Tuned Rivest Shamir Adelman technique, a dual authentication approach for effective data sharing in the cloud within hospital data management. The system begins with user registration with the Trusted Center where user details are provided. An authentication scheme utilizing the Caesar cipher and the Secure Hashing Algorithm 512 ensures integrity. The encryption process employs the Optimal Key-Tuned Rivest Shamir Adelman scheme for secure file transmission. To enhance key creation procedures in the Rivest Shamir Adelman model, the Improved Butterfly Optimization Algorithm technique is utilized to maximize throughput. Finally, dual authentication is conducted on the receiver side for file access and downloads from the cloud server. This additional layer of authentication fortifies the system resilience against unauthorized access, ensuring that only legitimate users can interact with the healthcare data stored in the cloud. The results indicate that the system outperforms other state-of-the-art systems enabling secure sharing and downloading of health data in cloud environments.

Artificial intelligence has become widespread in image processing tasks. At the same time, the number of vulnerabilities is increasing in systems implementing these artificial intelligence technologies (the attack surface is increasing). The main threats to information security can be implemented by introducing malicious perturbations into the input data, regardless of their type. To detect such attacks, approaches and methods have been developed based, in particular, on the use of an auto-encoder or the analysis of layers of the target neural network. The disadvantage of existing methods, which significantly reduce the scope of their application, is binding to the dataset or model architecture. This paper discusses the issues of expanding the scope (increasing scalability) of methods for detecting L0-optimized perturbations introduced by unconventional pixel attacks. An approach to detecting these attacks using statistical analysis of input data, regardless of the model and dataset, is proposed. It is assumed that the pixels of the perturbation embedded in the image, as a result of the L0-optimized attack, will be considered both local and global outliers. Outlier detection is performed using statistical metrics such as deviation from nearest neighbors and Mahalanobis distance. The evaluation of each pixel (anomaly score) is performed as a product of the specified metrics. A threshold clipping algorithm is used to detect an attack. When a pixel is detected for which the received score exceeds a certain threshold, the image is recognized as distorted. The approach was tested on the CIFAR-10 and MNIST datasets. The developed method has demonstrated high accuracy in detecting attacks. On the CIFAR-10 dataset, the accuracy of detecting onepixel attack (accuracy) was 94.3 %, and when detecting a Jacobian based Saliency Map Attack (JSMA) — 98.3 %. The proposed approach is also applicable in the detection of modified pixels. The proposed approach is applicable for detecting one-pixel attacks and JSMA, but can potentially be used for any L0-optimized distortions. The approach is applicable for color and grayscale images regardless of the dataset. The proposed approach is potentially universal for the architecture of a neural network, since it uses only input data to detect attacks. The approach can be used to detect images modified by unconventional adversarial attacks in the training sample before the model is formed.

It is known that when optical radiation passes through the phase modulator of a multifunctional integrated-optical chip (MIOC), along with the modulation of the phase of the light wave, there is a change in the power of optical radiation at the output of the coupled waveguide. This modulation is parasitic, and its magnitude depends on the control voltage at the modulator electrodes. Amplitude modulation leads to an error in the output signal of highly sensitive phase sensors, in particular, in a fiber optic gyroscope. This paper presents an experimental study of the change in the spatial intensity distribution (mode field) at the end of channel waveguides of a multifunctional integrated-optical chip under the action of an applied voltage. The experimental setup was assembled with a radiation source in the form of a singlefrequency laser RIO ORION with a central emission wavelength of 1550 nm. The optical receiver was an infrared camera SP503U-1550 with radiation registration in the wavelength range 1440–1605 nm, pixel size 9.9 × 9.9 μm and matrix size 640 × 480 pixels. The multifunctional integrated-optical chip was fabricated by titanium diffusion technology in Ti:LiNbO3 X-cut lithium niobate crystal substrate. A constant control voltage in the range from –10 V to +10 V was applied to the electrodes of the MIOC phase modulator. The distribution of optical radiation intensity in MIOC waveguides and in a single-mode optical fiber with an elliptical ESC-4 straining sheath was analyzed by calculating the overlap integral. The effect of electric field on optical radiation in MIOC waveguides is experimentally demonstrated. It is demonstrated that at constant voltage at the control electrodes of the phase modulator change in the radiation intensity distribution at the output of channel waveguides is observed. The observed changes correlate with parasitic amplitude modulation. The occurrence of parasitic amplitude modulation is due to the propagation of parasitic optical radiation along the waveguide. This phenomenon is caused by the escape of lithium oxide from the surface layer of lithium niobate into the gas phase during the technological process of titanium diffusion. The studies have allowed us to better understand the mechanisms of parasitic amplitude modulation in the phase modulator of MIOC and to develop practical recommendations for their elimination. These results can be useful for specialists working on research in the field of highly sensitive phase sensors using integrated optic circuits.

For continuous wavelet transformation, wavelets based on derivatives of the Gauss function are used, and for multiscale analysis, Daubechies wavelets are used. The development of algorithms for forward and inverse continuous wavelet transform in the frequency domain made it possible in this work to synthesize digital filters with a finite impulse response (FIR) different from existing methods. The quality of the synthesized filters was checked by decomposition and subsequent reconstruction of the signals. To do this, several filters were synthesized that completely cover the frequency range of the signal. Since wavelets are bandpass filters, the authors called the filters wavelets. The more precisely the reconstructed signal repeats the shape of the original signal, the better the wavelet constructed by one method or another. A comparison of the accuracy of signal reconstruction shows that the best conversion result is obtained by using synthesized wavelets. The impulse responses of the FIR filters are synthesized so that their frequency response are similar to the frequency responses of wavelets based on derivatives of the Gaussian function of a large order. The greater the filter order, the closer the frequency response is to a square-wave shape. Algorithms for forward and inverse wavelet transformation of a signal in the frequency domain using wavelets based on derivatives of the Gauss function are proposed. Profiling of the program shows that the time of the wavelet transforms using the fast Fourier transform is 15,000 times less than with the direct numerical integration for sampling the signal of 32,768 samples. These algorithms can be used for wavelets with a square-wave frequency response. At the same time, the numerical calculation time is halved. The accuracy of the reconstruction was compared for wavelets based on second-order derivatives, Daubechies wavelets, and wavelets with a square-wave frequency response. The reconstruction accuracy is highest for the latest wavelets. The use of the wavelet construction method is preferable since this method is relatively simple and it is easy to synthesize multiband filters with any form of frequency response. If, when synthesizing using existing methods, a short transition band can be obtained only for long impulse responses, while the transition band using the method of constructing wavelets is absent even for filters of very small orders. The paper presents the impulse responses of twoband, three-band digital filters and their frequency response. FIR digital filters with a square-wave frequency response have a higher delay-band attenuation coefficient compared to existing filters, do not have transition band, and can be used to process one-dimensional and two-dimensional signals.

A method for stabilizing structural anomaly detection under additive noise conditions as well as an algorithm for formal selection of the parameters of the solver rule in the structural anomaly detector based on the Robust Random Cut Forest (RRCF) method are proposed. In the framework of the developed approach, in order to stabilize the process of structural anomaly detection under the influence of additive noise, it is proposed to feed to the input of the RRCF-detector a data stream which is pre-processed by one of the digital filtering methods. In this case, the decision rule for anomaly detection is strictly formalized and transparently interpreted. The selection of parameters of the RRCF-based anomaly detector stabilized by pre-filtering methods of the input data stream is formalized. The RRCF-detector parameters choice within the proposed scheme guarantees a predetermined upper bound for the false alarm probability when deciding to detect a structural anomaly. This property is rigorously proved and formalized as a theorem. The performance of the stabilized RRCF-detector is investigated numerically. The achieved results confirm the performance of the proposed approach provided that the detection threshold is selected in the way proposed in this paper. An example of practical application of the proposed method is presented. The developed approach is promising for the detection of structural anomalies in conditions of observation additive noise, in a situation where it is important to guarantee an upper bound for the probability of false alarm. In particular, the approach can find application in monitoring technological regimes of liquid pumping in pipeline systems or in systems for detecting pre-failure states of technological equipment.

Modern artificial intelligence (AI) technologies are being used in a variety of fields, from science to everyday life. However, the widespread use of AI-based systems has highlighted a problem with their vulnerability to adversarial attacks. These attacks include methods of fooling or misleading an artificial neural network, disrupting its operations, and causing it to make incorrect predictions. This study focuses on protecting image recognition models against adversarial evasion attacks which have been recognized as the most challenging and dangerous. In these attacks, adversaries create adversarial data that contains minor perturbations compared to the original image, and then send it to a trained model in an attempt to change its response to the desired outcome. These distortions can involve adding noise or even changing a few pixels. In this paper, we consider the most relevant methods for generating adversarial data: the Fast Gradient Sign Method (FGSM), the Square Method (SQ), the predicted gradient descent method (PGD), the Basic Iterative Method (BIM), the Carlini-Wagner method (CW) and Jacobian Saliency Map Attack (JSMA). We also study modern techniques for defending against evasion attacks through model modification, such as adversarial training and pre-processing of incoming data, including spatial smoothing, feature squeezing, jpeg compression, minimizing total variance, and defensive distillation. While these methods are effective against certain types of attacks, to date, there is no single method that can be used as a universal defense. Instead, we propose a new method that combines adversarial learning with image pre-processing. We suggest that adversarial training should be performed on adversarial samples generated from common attack methods which can then be effectively defended against. The image preprocessing aims to counter attacks that were not considered during adversarial training. This allows to protect the system from new types of attacks. It is proposed to use jpeg compression and feature squeezing on the pre-processing stage. This reduces the impact of adversarial perturbations and effectively counteracts all types of considered attacks. The evaluation of image recognition model (based on convolutional neural network) performance metrics based was conducted. The experimental data included original images and adversarial images created using attack FGSM, PGD, BIM, SQ, CW, and JSMA methods. At the same time, adversarial training of the model was performed in experiments on data containing only adversarial examples for the FGSM, PGD, and BIM attack methods. Dataset used in experiments was balanced. The average accuracy of image recognition was estimated with crafted adversarial imaged datasets. It was concluded that adversarial training is effective only in countering attacks that were used during model training, while methods of pre-processing incoming data are effective only against more simple attacks. The average recognition accuracy using the developed method was 0.94, significantly higher than those considered methods for countering attacks. It has been shown that the accuracy without using any counteraction methods is approximately 0.19, while with adversarial learning it is 0.79. Spatial smoothing provides an accuracy of 0.58, and feature squeezing results in an accuracy of 0.88. Jpeg compression provides an accuracy of 0.37, total variance minimization — 0.58 and defensive distillation — 0.44. At the same time, image recognition accuracy provided by developed method for FGSM, PGD, BIM, SQ, CW, and JSMA attacks is 0.99, 0.99, 0.98, 0.98, 0.99 and 0.73, respectively. The developed method is a more universal solution for countering all types of attacks and works quite effectively against complex adversarial attacks such as CW and JSMA. The developed method makes it possible to increase accuracy of image recognition model for adversarial images. Unlike adversarial learning, it also increases recognition accuracy on adversarial data generated using attacks not used on training stage. The results are useful for researchers and practitioners in the field of machine learning.

The paper examines the issues of increasing the efficiency and reliability of stereo image identification through computational prediction of the position and size of the uncertainty zone in which the desired correspondence point is known to be located. A control point is selected on one of the stereo images, for which it is necessary to find a correspondence point on the second stereo image. Based on the known parameters of the stereoscopic television system and the coordinates of the control point, using the mathematical apparatus proposed in the work, the coordinates of the boundaries of the uncertainty zone on the second stereo image are calculated. The second point of correspondence is found by the search procedure by comparing identical small areas with centers in the control point on the first stereo image and in the points of the uncertainty zone on the second; the comparison is made according to the criterion of minimum quadratic mismatch of intensities. The necessary a priori information for implementing the method is the maximum heights of the relief displayed on stereo images. The ratios of linear dimensions on a flat relief and on an image formed according to the principle of central projection were obtained. Relationships have been obtained that make it possible to obtain, by calculation, the coordinates of the correspondence points and the stereoscopic mismatch for stereo images of a flat relief. For stereo images of a volumetric relief, calculation formulas are obtained for determining the boundaries of the zone of uncertainty in the second stereo image within which the search for the point of correspondence is carried out. The correctness and performance of the obtained relationships are confirmed by computer modeling. Limiting the size of the search area by means of calculated prediction of the uncertainty zone makes it possible to reduce the computational and time costs of the search procedure. Due to this, the efficiency of identifying stereo image points increases and the likelihood of false identification decreases.

The paper presents a study of the effect of laser irradiation of crystalline chalcogenide films of lead selenide (PbSe) on their electrical characteristics caused by irreversible modification of the structure due to valence reconfiguration of lead as a result of its oxidation. The study of the modification features of the electrical properties of the films was carried out because of laser exposure to nanosecond pulses with a wavelength of 1064 nm. Measurements of the electrical characteristics of PbSe films were carried out using the four-probe method. It was shown that when the current was directed parallel to the laser tracks recorded in the darkening mode, the resistance of the modified film decreased by 44 % compared to the original sample, and with the perpendicular direction of the current, the resistance increased by 153 %. The resistance of the film increased more than 27 times after laser irradiation in the bleaching mode, regardless of the direction of the current relative to the laser tracks. The experimentally measured temperature and its gradient along the laser spot on the film in the darkening and bleaching modes turned out to be in good agreement with the proposed mathematical model of the thermal effect of laser pulses. It has been shown that the processes of laser modification of films occur at lower temperatures than during standard heat treatment in a furnace. The obtained results can be applied in the development of photodetectors in the middle IR range of the spectrum based on PbSe film.

Coriolis vibration gyroscopes are a class of promising inertial primary information sensors that respond to the rotation of the resonator base through Coriolis inertial forces arising in the vibrating shell. Currently, two directions for the production of resonators for such gyroscopes have been developed: from quartz glass, a material with extremely low internal friction, and based on the processing of a metal alloy. When using the first direction, thanks to the high quality factor of quartz, it is possible to create navigation-class integrating gyroscopes. Existing samples of Coriolis vibration gyroscopes with metal resonators, as a rule, are angular velocity sensors. The problem of creating an integrating mode of a gyroscope with a metal resonator is associated with the low quality factor of metal alloys which usually does not exceed 35,000. With this value of quality factor, the duration of operation of the gyroscope in the angular deviation sensor mode will be several seconds. The paper presents methods for ensuring the functioning of Coriolis vibration gyroscopes, including the integrating gyroscope mode. A mathematical description of Coriolis vibration gyroscopes with a cylindrical cavity resonator is given based on the dynamic model of Dr. D. Lynch using the method of envelope amplitudes of oscillations. The mathematical model is supplemented with corrections that provide compensation for the dissipation of the resonator oscillations energy to implement the integrating mode of the gyroscope. The conditions for complete compensation of vibration energy dissipation are shown. A description of methods for exciting a standing wave in a resonator using periodic forcing and by creating self-oscillations is presented. It is shown that the duration of the transient excitation process is determined by the time constant of the resonator. The results of experimental studies of Coriolis vibration gyroscopes with a low-Q metal resonator are presented confirming the possibility of implementing an integrating mode of operation of the gyroscope. The initial excitation of the resonator oscillations is carried out by a self-oscillating circuit. According to the results of experimental studies, the quality factor of the metal resonator was increased by a factor of 17. The operating time of Coriolis vibrating gyroscopes has been equally increased. The possibility of constructing Coriolis vibration gyroscopes in the integrating gyroscope mode based on a low-Q metal resonator has been shown theoretically and experimentally. The solution to this problem was based on a circuitry method for increasing the quality factor. In principle, the quality factor of the resonator can be significantly increased compared to the figure achieved in the experiment. This will ensure a longer operating time of Coriolis vibration gyroscopes in the integrating mode.

It is widely known that among sound signals generated by natural and anthropogenic phenomena, the most long-lived are waves of frequency less than 20 Hz, called infrasound. This property allows tracking at a distance by infrasound monitoring the occurrence of high-energy events on regional scales (up to 200–300 km). At the same time, the separation of useful infrasound signals from background noise is a non-trivial task in real-time and post-facto signal processing. In this paper we propose a new method for classification of specific signals in infrasound monitoring data using Shannon permutation entropy and vectors of frequency distribution of occurrence frequencies of permutations of consecutive sample values of rank 3 (number of permutation elements). To evaluate the validity of the proposed entropy-based classification method, two machine learning methods — random forest method and classical neural network approach — implemented in Python language using Scikit-lean, TensorFlow and Keras libraries were used. The classification quality was evaluated against the traditional frequency-based method of class extraction based on Fourier transform. Recognition was performed on the prepared infrasound monitoring data in the Altai Republic. The results of computational experiment on the separation of 5 classes of signals showed that classification by the proposed method gives the same results of recognition by neural network with in comparison with frequency classification of the original data; the recognition accuracy was 51–58 %. For the random forests method, the recognition accuracy of frequency classes was slightly higher: 51 % vs. 45 % for classes using the permutation entropy method. The analysis of the results of the computational experiment shows sufficient competitiveness of the method of classification by permutation entropy in the recognition of infrasound signals. In addition, the proposed method is much easier to implement for inline signal processing in lowconsumption microcontroller systems. The next step is to test the method at infrasound signal registration points and as part of the infrasound monitoring data processing system for real-time event detection.