There is often a need to analyze unstructured data when assessing the risk of emergency situations. Traditional analysis methods may not take into account the ambiguity of information, which makes them insufficiently effective for risk assessment. The article proposes the use of a modified hierarchy process analysis method using fuzzy logic, which allows for more effective consideration of uncertainties and subjective assessments in the process of analyzing emergency risks. In addition, such methods allow for consideration of not only quantitative indicators, but also qualitative ones. This, in turn, can lead to more informed decisions in the field of risk management and increased preparedness for various situations. The integration of technologies for working with unstructured data in the process of assessing emergency risks not only increases the accuracy of forecasting, but also allows for adapting management strategies to changing conditions.

Keywords: artificial intelligent systems, unstructured data, risk assessment, classical hierarchy analysis method, modified hierarchy analysis method, fuzzy logical inference system

Many modern information processing and control systems for various fields are based on software and hardware for image processing and analysis. At the same time, it is often necessary to ensure the storage and transmission of large data sets, including image collections. Data compression technologies are used to reduce the amount of memory required and increase the speed of information transmission. To date, approaches based on the use of discrete wavelet transformations have been developed and applied. The advantage of these transformations is the ability to localize the points of brightness change in images. The detailing coefficients corresponding to such points make a significant contribution to the energy of the image. This contribution can be quantified in the form of weights, the analysis of which allows us to determine the method of quantization of the coefficients of the wavelet transform in the proposed lossy compression method. The approach described in the paper corresponds to the general scheme of image compression and provides for the stages of transformation, quantization and encoding. It provides good compression performance and can be used in information processing and control systems.

Keywords: image processing, image compression, redundancy in images, general image compression scheme, wavelet transform, compression based on wavelet transform, weight model, significance of detail coefficients, quantization, entropy coding

The paper considers the task of collection and preparation of data coming from several information systems on the example of automation of registrar's reporting. The languages OWL, XML, XBRL and semantic networks can be used to describe the subject area. A set of criteria for analysing and selecting the most appropriate knowledge representation language for the purpose of data collection on the example of financial statements is prepared. The results of service development are described and the application of XBRL format is shown. The multi-agent approach to modelling and design of information systems was used in the development of the service.

Keywords: data mining, subject area model, data formats, XBRL, business process, service, data integration

The work presents the review of modern log trucks under the recent sanctions imposed. The author states that the problem of renewing the existing log trucks becomes urgent for forest transporting and logging companies nowadays. There is a wide range of new basic chassis and trucks at the market to build log trucks with a wheel formula 6x4 and 6x6 produced by Russian, Belorussian and Chinese factories. A great number of trailer links is produced to build log trucks. There is an opportunity to buy used trucks of other companies. For the first stage of the technical and economic analysis and preliminary selection of the optimal type and composition of a logging truck, a comparative assessment of the effectiveness of logging trucks was carried out. The analysis shows that Russian log trucks with engine power more than 400 HP (horsepower) can compete with the best foreign models. Nevertheless, the problem of reliability of Russian, Belorussian and Chinese log trucks needs further research.

Keywords: log trucks, trailer links, productivity, effectiveness

This paper describes approaches to visualization and comparison of semantic trees reflecting the component structure of the patented device and the connections between them using graph databases. DBMS data uses graph structures to store, process, and represent data. The main elements of a graph database are nodes and edges, which, within the framework of the task, model entities of 3 types (SYSTEM, COMPONENT, ATTRIBUTE) and 5 types of connections (PART-OF, LOCATED-AT, CONNECTED-WITH, ATTRIBUTE-FOR, IN-MANNER-OF). According to the results of the study, it can be stated that Neo4j demonstrates the best possibilities for graph visualization; ArangoDB, despite correctly entered queries, performs incomplete visualization; AllegroGraph showed difficult work with code, difficult configuration of graph tree visualization. 3 algorithms for comparing graph representations of information have been tested: Graph Edit Distance, Topological Comparison, Subgraph Isomorphism. The algorithms are implemented in python, compares 2 graph trees, displays visualization and analysis of common graph structures and differences.

Keywords: semantic tree, component structure, patent, graph databases, Neo4j, AllegroGraph, ArangoDB

In systems for monitoring, diagnostics and recognition of the state of various types of objects, an important aspect is the reduction of the volume of measured signal data for its transmission or accumulation in information bases with the ability to restore it without significant distortion. A special type of signals in this case are packet signals, which represent sets of harmonics with multiple frequencies and are truly periodic with a clearly distinguishable period. Signals of this type are typical for mechanical, electromechanical systems with rotating elements: reducers, gearboxes, electric motors, internal combustion engines, etc. The article considers a number of models for reducing these signals and cases of priority application of each of them. In particular, the following are highlighted: the discrete Fourier transform model with a modified formula for restoring a continuous signal, the proposed model based on decomposition by bordering functions and the discrete cosine transform model. The first two models ideally provide absolute accuracy of signal restoration after reduction, the last one refers to reduction models with information loss. The main criteria for evaluating the models are: computational complexity of the implemented transformations, the degree of implemented signal reduction, and the error in restoring the signal from the reduced data. It was found that in the case of application to packet signals, each of the listed models can be used, the choice being determined by the priority indicators of the reduction assessment. The application of the considered reduction models is possible in information and measuring systems for monitoring the state, diagnostics, and control of the above-mentioned objects.

Keywords: reduction model, measured packet signal, discrete cosine transform, decomposition into bordering functions, reduction quality assessment, information-measuring system

At present, continuous tank reactor is widely used in many different industries, and there are many control methods for this reactor. This paper presents a design method for model predictive controller (MPC) based on fuzzy model. The control object is modeled by fuzzy model (Takagi-Sugeno), the optimization problem is solved by genetic algorithm. Using fuzzy models and genetic algorithms to implement MPC controller, it achieved better quality than traditional MPC controllers.

Keywords: method of designing a model predictive controller, fuzzy model, Takagi Sugeno, genetic algorithms, multiple inputs-multiple outputs

In operational diagnostics and recognition of states of complex technical systems, an important task is to identify small time-determined changes in complex measured diagnostic signals of the controlled object. For these purposes, the signal is transformed into a small-sized image in the diagnostic feature space, moving along trajectories of different shapes, depending on the nature and magnitude of the changes. It is important to identify stable and deterministic patterns of changes in these complex-shaped diagnostic signals. Identification of such patterns largely depends on the principles of constructing a small-sized feature space. In the article, the space of decomposition coefficients of the measured signal in the adaptive orthonormal basis of canonical transformations is considered as such a space. In this case, the basis is constructed based on a representative sample of realizations of the controlled signal for various states of the system using the proposed algorithm. The identified shapes of the trajectories of the images correspond to specific types of deterministic changes in the signal. Analytical functional dependencies were discovered linking a specific type of signal change with the shape of the trajectory of the image in the feature space. The proposed approach, when used, simplifies modeling, operational diagnostics and condition monitoring during the implementation of, for example, low-frequency diagnostics and defectoscopy of structures, vibration diagnostics, monitoring of the stress state of an object by analyzing the time characteristics of response functions to impact.

Keywords: modeling, functional dependencies, state recognition, diagnostic image, image movement trajectories, small changes in diagnostic signals, canonical decomposition basis, analytical description of image trajectory

The article is devoted to describing approaches to analyzing the information space using low-code platforms in order to identify factors that form new identities of Azerbaijan and the unique features of the country’s information landscape. The article describes the steps to identify key themes and collect big data in the form of text corpora from various Internet sources and analyze the data. In terms of data analysis, the study of the sentiment of the text and the identification of opinion leaders is carried out; the article also includes monitoring of key topics, visualized for a visual presentation of the results.

Keywords: data analytics, trend monitoring, sentiment analysis, data visualization, low-code, Kribrum, Polyanalyst, big data

The problem of optimisation of selective assembly of plunger-housing precision joints of feeders of centralised lubrication systems used in mechanical engineering, metallurgy, mining, etc. is considered. The probability of formation of assembly sets of all types is used as the target function; the controlled variables are the number and volumes of parts of batches and their adjustment centres, as well as the values of group tolerances. Several variants of solving the problem at different combinations of controlled variables are considered. An example of the solution of the optimisation problem on the basis of the previously developed mathematical models with the given initial data and constraints is given, the advantages and disadvantages of each of the variants are outlined. Optimisation allows to increase the considered indicator by the value from 5% to 20%.

Keywords: selective assembly, lubrication feeder, precision connection, mathematical model, optimisation

Automating government processes is a top priority in the digital era. Because of historical development, many existing systems for registering and storing data about individuals coexist, requiring intervening IT infrastructures. The article considers the procedure for the development, creation and implementation of software for updating and generating data about residents of the city of Astana. It defines the functional capabilities and determines the role of the information system in automation and monitoring government activities. The authors conducted the study by observing, synthesizing, analyzing, systematizing, and classifying the data received. The authors used scientific works of local and foreign authors on the topic under study and open databases as sources of literature. At the end of the work, the authors list the literature used. The authors have, for the first time, created the structure and algorithms of the information system known as ""Population Database ""Geonomics"". Specifically, they have developed the mechanism and algorithm for the interaction of the ""Geonomics"" information system with government databases. As well as, additional opportunities for using the software have been identified by developing an algorithm for planning and placing social objects when using the information system ""Geonomics"". The authors have concluded that the algorithms developed for the use of the information system ""Population Database “Geonomics"" represent a reliable and powerful tool, which plays a critical role in the optimization and automation of processes related to population accounting and urban infrastructure management. This software contributes to the development of the city and the improvement of its residents' quality of life, based on up-to-date and reliable information. In addition, the developed algorithm allows for real-time monitoring of the current data of city residents and their density, based on which decisions can be made regarding the construction and placement of social facilities for the comfortable service and living of city residents.

Keywords: automation, updating, government activities, government agency, information system, database

A complex dynamic system is defined by a structurally invariant operator. The operator structure allows formulating problems of stabilizing program motions or equilibrium positions of a complex dynamic system with constraints on state coordinates and control. The solution of these problems allows synthesizing a structurally invariant operator of a complex dynamic system with inequality-constraints on the vector of locally admissible controls and state coordinates. Computational experiments confirming the correctness of the synthesized structurally invariant projection operator are performed.

Keywords: structurally-invariant operator, stabilization of program motions, complex nonlinear dynamic system, projection operator, SimInTech

Digital holographic microscopy (DHM), is a combination of digital holography and microscopy. It is capable of tracking transparent objects, such as organelles of living cells, without the use of fluorescent markers. The main problem of DHM is to increase an image spatial resolution while maintaining a wide field of view. The main approaches to solving this problem are: increasing of the numerical aperture of lighting and recording systems, as well as using deep learning methods. Increasing the numerical aperture of lighting systems is achieved by using oblique, structured or speckle illumination. For recording systems it is achieved by using hologram extrapolation, synthesis or super-resolution. Deep learning is usually used in conjunction with other methods to shorten the compute time. This article is dedicated to describe the basic principles and features of the above approaches.

Keywords: digital holographic microscopy, spatial resolution, field of view, numerical aperture, sample, light beam, CCD camera, diffraction, imaging system, super-resolution

Blurred frames pose a significant problem in various fields such as video surveillance, medical imaging and aerial photography, when solving the following object detection and identification, image-based disease diagnosis, as well as analyzing and processing data from drones to create maps and conduct monitoring. This article proposes a method for detecting blurred frames using a neural network model. The principle of operation of the model is to analyze images presented in the frequency domain in the Hough space. To further evaluate the effectiveness of the proposed author's solution, a comparison was made of existing methods and algorithms that can be used to solve the problem, namely the Laplacian method and the manual sampling method. The results obtained show that the proposed method has high accuracy in detecting blurred frames and can be used in systems where high accuracy and clarity of visual data are required for decision-making.

Keywords: blurred frames, motion blur, blur, Hough transform, spectral analysis

In this paper, a new intent and entity recognition model for the subject area of air passenger service, labelled as IRERAIR-TWIN, is developed using the ‘no code’ question-answer development platform ‘TWIN’. The advantages of the no-code platform were analysed in terms of the ease of developing an application question-answer system and reducing the amount of work involved in developing an application model for a narrow subject area. The results show that the ‘TWIN’ system provides an intuitive web-based user interface and a simpler approach to develop the semantic module of a question-answer system capable of solving application problems for a narrow subject area that are not overly complex. However, this approach has limitations for deep semantic analysis tasks, especially in complex contextual inference and processing of large text fragments. The paper concludes by emphasising that future research will focus on using ChatGPT-based ‘low code’ platforms and large language models to further improve the intelligence of the IRERAIR-TWIN model. This extension aims to broaden the scope of the scenarios.

Keywords: question-answering systems, No-code, Low-code, Intent recognition, Named entity recognition, Data annotation, Feature engineering, Pre-trained model, software development,End-user development

Image super-resolution is a popular task that aims to translate images from low resolution to high resolution. For this task, convolutional networks are often used. Convolutional neural networks, have a great advantage in image processing. But despite this, often information can be lost during processing and increasing the depth and width of the network can make further work difficult. To solve this problem, data transformation into frequency domain is used. In this paper, the image is divided into high frequency and low frequency regions, where higher priority is given to the former. Then with the help of quality check, and visual evaluation, the method is analyzed and the conclusion regarding the performance of the algorithm is drawn.trial enterprise.

Keywords: super-resolution (SR), low-resolution (LR), high-resolution (HR), discrete-cosine transform, convolution-neural networks

The article describes the prerequisites for creating an electronic notification system for students in an educational institution. A use case diagram is provided that describes interaction with the system from the point of view of the user-employee of the educational department and the user-student. A diagram of the physical database model is presented and a description of the purpose of the tables is given. The system uses two types of client applications: an administrative client for organizing the work of educational department employees and a Telegram bot for working on the students’ side. A scheme for working with user data when processing chatbot commands is defined in the IDEF0 notation. The choice of the interlocutor program as a communication tool was made based on the popularity of this technology. The administrative client is implemented in C# using Windows Forms technology, the chatbot is implemented in Python using the “schedule” time planning library, “time” working with time and “threading” multi-threading support.

Keywords: chat bot, Telegram bot, messenger, message, mobile device, information system, database, computer program, application

The paper is dedicated to the modeling of opportunistic behavior in electroenergetics. We considered two setups: an optimal control problem from the point of view of a separate agent and a Stackelberg game of the controller with several agents. It is assumed that the agents may collude with the controller and to diminish the data about electroenergy consumption proportionaaly to the amount of bribe. The principal attention is paid to the numerical investigation of these problems basing on the method of qualitatively representative scenarios in simulation modeling. It is shown that using of a small number of the correctly chosen scenarios provides an acceptable qualitative precision of the forecast of systems dynamics. The numerical results are analyzed, and the recommendations on the struggle with corruption are formulated. An increase of the penalty coefficient in the case of catching of the controller taking "kickbacks" or an increase of her official reward makes the kickbacks not profitable.

Keywords: opportunistic behavior, optimal control problem, simulation modeling, Stackelberg games

The paper proposes a solution to geological problems using probabilistic and statistical methods. It presents the results of using spectral correlation data analysis, which involves the processing of digital geoinformation organized into three-dimensional regular networks. The possibilities of applying methods of statistical, spectral, and correlation analysis, as well as linear optimal filtering, anomaly detection, classification, and pattern recognition, are explored. Spectral correlation and statistical analysis of geodata were conducted, including the calculation of Fourier spectra, various correlation functions, and gradient characteristics of geofields.

Keywords: interprofile correlation, self-adjusting filtering, weak signal detection, geological zoning and mapping, spatially distributed information

The article discusses the development of data normalization and standardization tools using Python libraries. A description of the theoretical foundations and formulas used to normalize and standardize data is considered. For internal calculations of the developed software, the Pandas and NumPy libraries were used. The external interface was built on the basis of the Streamlit library, which allows you to deploy web applications without any additional resources. Code fragments are provided and implementation mechanisms are explained. A description of the developed tool is provided: a detailed explanation of the functionality of the tool, user interface and examples of use. The importance of data preprocessing, selection of an appropriate method, and final remarks on the usefulness of interactive data processing tools are discussed.

Keywords: data processing, statistics, information systems, Python web systems.

This article is devoted to the development of a collision detection technique using a polygonal mesh and neural networks. Collisions are an important aspect of realistically simulating physical interactions. Traditional collision detection methods have certain limitations related to computational accuracy and computational complexity. A new approach based on the use of neural networks for collision detection with polygonal meshes is proposed. Neural networks have shown excellent results in various computer vision and image processing tasks, and in this context they can be effectively applied to polygon pattern analysis and collision detection. The main idea of ​​the technique is to train a neural network on a large data set containing information about the geometry of objects and their movement for automatic collision detection. To train the network, it is necessary to create a special module responsible for storing and preparing the dataset. This module will provide collection, structuring and storage of data about polygonal models, their movements and collisions. The work includes the development and testing of a neural network training algorithm on the created dataset, as well as assessing the quality of network predictions in a controlled environment with various collision conditions.

Keywords: modeling, collision detection techniques using polygonal meshes and neural networks, dataset, assessing the quality of network predictions

The article discusses the concept of software implementation of complex tools on the platform "1C:Enterprise" for automating the accounting of the activities of shelters for homeless animals. The architecture of the solution is described, highlighting aspects of the functioning of the system’s integration modules with the social network “VKontakte” and the Telegram messenger. Diagrams of the sequence and activity of processes regarding the interaction of citizens with the key functionality of the system are presented.

Keywords: animal shelter, homeless animals, 1C:Enterprise, automation, activity accounting, animals, software package, information system, Telegram bot, integration with VKontakte, pet search

Orthogonal Frequency Division Multiplexing –OFDM) multiplexing technology is quite promising in wireless communication systems. Simultaneous use of multiple subcarriers allows for a relatively high information transfer rate. The use of mathematical models of discrete wavelet transformations instead of the fast Fourier transform (hereinafter FFT), allows you to increase the speed of signal processing by using modular codes of residue classes (hereinafter MKV). At the same time, these codes can be used to increase the noise immunity of systems with OFDM. It is known that block turbo codes (hereinafter referred to as TC) are widely used to combat packets of errors that occur when transmitting signals over a communication channel. The article presents a developed method for constructing modular turbocodes based on a system of residual classes (hereinafter MTKSOC). Obviously, the use of MTCS entails changes in the structure of the system with OFDM. Therefore, the development of a method for constructing a modular turbo code of SOC and a structural model of an interference-resistant system with OFDM using MTXOC is an urgent task. The purpose of the article is to increase the level of noise immunity of systems with OFDM, using wavelet transformations implemented in MKV instead of FFT, through the use of modular turbo code SOC.

Keywords: modular codes of residue classes, residual class system, modular turbo code of residual class system, error correction algorithm, structural model, multiplexing, orthogonal frequency division of channels

Currently, Internet of Things technologies are actively used in manufacturing enterprises for remote monitoring and preventive control of technological processes. The article is devoted to the development of an original mathematical model of the process of transmitting information packets and confirmations in the Industrial Internet of Things system, the use of which allows us to assess the probability of duplication of messages sent to the production process control center. To develop the model, the mathematical apparatus of probabilistic graphs was used, which makes it possible to take into account all possible states of the simulated process and the probabilities of transitions from one state to another. The results of computational experiments showed that the use of the developed model makes it possible to justify the choice of the maximum number of retransmissions, in which the probability of message duplication does not exceed the specified permissible values ​​at the current level of bit errors.

Keywords: industrial Internet of things, telemetry data, production process control, message duplication, retransmissions, bit error rate, sensor devices, server, probabilistic graph

Detecting aggressive and abnormal driver behavior, which depends on a multitude of external and internal factors, is critically important for enhancing road safety. This article provides a comprehensive review of machine learning methods applied for driver behavior classification. An extensive analysis is conducted to assess the pros and cons of existing machine learning algorithms. Various approaches to problem formulation and solution are discussed, including supervised and unsupervised learning techniques. Furthermore, the review examines the diverse range of data sources utilized in driver behavior classification and the corresponding technical tools employed for data collection and processing. Special emphasis is placed on the analysis of Microelectromechanical Systems sensors and their significant contribution to the accuracy and effectiveness of driver behavior classification models. By synthesizing existing research, this review not only presents the current state of the field but also identifies potential directions for future research, aiming to advance the development of more robust and accurate driver behavior classification systems.

Keywords: machine learning, driver classification, driver behavior, data source, microelectromechanical system, driver monitoring, driving style, behavior analysis