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• COMPARATIVE ANALYSIS OF MISSING DATA RECOVERY METHODS

  A.A. Sorokin , A. V. Dagaev , I. M. Borodyansky

  2020-11-22

  Abstract ▼

  In recent decades, the methods of system analysis have been developing qualitatively. It is
  associated with an increase in the rate of technical development, the densification of time processes,
  the rapid growth of accumulated information and new capabilities of computer technology.
  These include methods for analyzing large amounts of data, methods of data mining, methods of
  analytical modeling, methods of parallel data processing, neural network methods, forecasting
  methods, and others. The presented methods make it possible to quickly and efficiently process
  heterogeneous clusters of information, accumulate and synthesize data, generalize and classify
  information. The last of the presented methods are methods of interpolation and extrapolation of
  lost, damaged or missing information. These methods allow to structure, restore and model information
  based on statistical data, mathematical and algorithmic methods. Thus, the article deals
  with the problem of recovering missing data in graphic and complex objects. Literary sources on
  the problems under consideration are given. They provide extensive information on the topic under
  consideration: present genetic algorithms used for spatial interpolation; the solution of problems
  of heterogeneity of interpolation of seismic data is considered; it is described the use of
  spline approximation to calculate the characteristics of nonlinear electronic components; the
  method of constructing a model of three-dimensional parametric rational bodies using generalized
  Bezier interpolation is analyzed, which allows modeling the shape of a body and anisotropic
  space; methods using fuzzy linear equations are described, which are widespread in computer
  vision; the method of adaptive interpolation based on the gradient and taking into account the
  local gradient of the original image is investigated. It is made comparing several common methods
  of interpolation and data restoration, in article, such as: bilinear interpolation, Bezier surface.
  Each method and features of its application within the framework of the experiment are briefly
  described. The result of a series of experiments with the presented methods with different numbers
  of tests is presented. In conclusion, summary is drawn about the rationality of choosing one of the
  proposed methods without the use of a long field experiment in each case.

• TRANSFORMATION AND ANALYSIS OF INFORMATION WHEN CREATING A DATABASE OF PARTICIPANTS OF THE GREAT PATRIOTIC WAR 1941-1945 IN THE MEMORIAL COMPLEX «ROAD OF MEMORY» IN THE MAIN RUSSIAN ARMED FORCES CATHEDRAL ON THE BASIS OF COMPUTER METHODS OF INFORMATION PR

  S. A. Botsvin , V.A. Khvatkov

  2021-11-14

  Abstract ▼

  Preserving the historical memory of the participants of the Great Patriotic War
  1941–1945 is a world-class task that should preserve the truth about the most terrible war and the
  feat of our people. In modern conditions, attracting interest in history, traditions and finally
  recognition of one's duty to the past generations requires modern methods. One of these methods
  is the transformation of information, which allows you to present this information in such a way
  that it can be used most effectively. At the same time, the main goal in the transformation of historical
  data is to optimize their representations and formats and not change the information content.
  The presented algorithms of transformation and analysis of information when creating a database
  of participants of the Great Patriotic War were aimed at maximizing the preservation of historical
  value and reliability of information. To achieve this goal, computer methods of information processing
  for normalization and consolidation of personal data obtained from various sources are
  considered. The analysis of the content of information in archival documents with the presentation
  of statistical data on the number of documents (records) from various sources (archives, databases,
  information resources, etc.) is carried out and the procedure for translating information
  from archival documents into electronic form, which has been applied in practice, is described.
  Based on the analysis of the information, diagrams of the content of personal information in archival
  sources are constructed, the stages of systematization and bringing the generalized information
  array records to a single format are presented, as well as the procedure for combining and
  deleting duplicate records. For the possibility of using in other projects, an algorithm for consolidating
  data obtained from various sources is described in detail, and its block diagram is constructed.
  In addition, the applied fuzzy search algorithms are described, which made it possible to
  minimize errors in records, as well as image comparison algorithms for searching for duplicates
  from photographs. All of these algorithms have made it possible to bring together information
  contained on various media, having different structures and geographical location. The created
  information resource allows you to enormously reduce the resources needed to find the necessary
  information, including access to which was limited or not at all. Further improvement of algorithms
  for normalization and consolidation of information can serve as a basis for data migration
  from outdated to promising systems, as well as for the formation of information resources from
  existing heterogeneous archival funds.

• DEVELOPMENT OF A DISTRIBUTED CONTROL SYSTEM OF THERMAL PROCESSES IN A HYDRAULIC PRESS

  A.L. Liashenko

  2021-12-24

  Abstract ▼

  The necessity of regulating the temperature of the coolant in hydraulic presses, providing
  hot gluing of plywood, regulating the pressure in the press channels and maintaining technological
  parameters at a given level, is considered. A column hydraulic press P-714-B for hot gluing of
  plywood, installed at the Ust-Izhora plywood mill, is considered as a control object. The article
  provides a description of the column hydraulic press. To monitor the parameters of the presented
  installation of plywood production, it is proposed to consider the heating press plates and plywood
  packages as an object with distributed parameters. To develop a mathematical model of the control
  object, a functional diagram of this device with the main equipment and technological flows of
  the coolant was considered. A technique has been developed for modeling objects of this class as
  objects with distributed parameters. Consideration of the processes occurring in the channels of
  the heating plates made it possible to formulate differential equations of motion that describe the
  flow of the working medium in the system of channels. The developed method of mathematical
  modeling of heat propagation in the heating plates of the press and plywood packages made it
  possible to draw up a mathematical model for the object under consideration. This mathematical
  model turned out to be quite complex, and it is not possible to solve the resulting system of partial
  differential equations analytically (to isolate the transfer function). For a numerical analysis of the
  considered control object, a discrete model of equations and a computational algorithm were
  compiled. In the process of compiling discrete models, the problems of “joining” the boundary
  conditions were solved, the stability of the computational scheme was ensured, and the steps of
  discretization with respect to spatial variables were selected. Software was specially developed for
  computer modeling. With its help, the temperature values at the control points were calculated.
  The presented mathematical model made it possible to carry out a numerical experiment, as a
  result of which the frequency characteristics of the object under study were obtained. These characteristics
  were used in the synthesis of a distributed high-precision controller.

• ASSESSMENT OF INFLUENCING FACTORS AND FORECASTING OF POWER CONSUMPTION IN THE REGIONAL POWER SYSTEM, TAKING INTO ACCOUNT ITS OPERATING MODE

  N.K. Poluyanovich, М. N. Dubyago

  2022-05-26

  Abstract ▼

  The article is devoted to the research of the assessment of influencing factors and forecasting
  of power consumption in the regional power system, taking into account its operating modes.
  The analysis of existing methods of forecasting energy consumption is carried out. The choice of a
  forecasting method using an artificial neural network is justified. An algorithm for creating a neural
  network for short-term prediction of electrical load is considered. The relevance of the work is
  due to the requirements of the current legislation for forecasting electricity consumption in order
  to solve the problem of maintaining a balance of power between the generating side and the consumption
  of electric energy. At the same time, one of the main tasks related to the generation of
  electric energy and its consumption is the task of maintaining a balance of capacities. On the one
  hand, with an increase in the planned load, interruptions in the supply of electricity may occur, on
  the other hand, a decrease in electricity consumption will also lead to a decrease in the efficiency
  of power plants, and ultimately to an increase in the cost of electricity both for the wholesale electricity
  market and for the end user. The developed neural network model reduces the task of shortterm
  forecasting of power consumption to the search for a matrix of free coefficients by training
  on available statistical data (active and re-active power, ambient temperature, date and index of
  the day). The received NS model of short-term forecasting of power consumption of a section of
  the district 10 kV electric grid takes into account the factors: – time, - meteorological conditions,
  – disconnections of individual power supply lines of cottages, – operating mode of electricity consumers.
  Predictive estimates of the power consumption of the power system have been obtained
  based on the data of the electricity consumed by the outdoor temperature, the type of day, etc. The
  model for predicting the magnitude of the consumed active and reactive power is quite workable,
  but at this stage still has a fairly high level of forecasting error. To improve the accuracy of forecasting,
  it is necessary to increase the database that makes up the training sample, because at the
  moment the available data cover a time period of only 3–4 months. The results of the analysis
  showed that forecasting reactive power consumption causes the greatest difficulties.

• METHODS AND ALGORITHMS FOR TEXT DATA CLUSTERING (REVIEW)

  V.V. Bova, Y.A. Kravchenko, S.I. Rodzin

  2022-11-01

  Abstract ▼

  The article deals with one of the important tasks of artificial intelligence – machine processing
  of natural language. The solution of this problem based on cluster analysis makes it possible
  to identify, formalize and integrate large amounts of linguistic expert information under conditions
  of information uncertainty and weak structure of the original text resources obtained from
  various subject areas. Cluster analysis is a powerful tool for exploratory analysis of text data,
  which allows for an objective classification of any objects that are characterized by a number of
  features and have hidden patterns. A review and analysis of modern modified algorithms for agglomerative
  clustering CURE, ROCK, CHAMELEON, non-hierarchical clustering PAM, CLARA
  and the affine transformation algorithm used at various stages of text data clustering, the effectiveness
  of which is verified by experimental studies, is carried out. The paper substantiates the
  requirements for choosing the most efficient clustering method for solving the problem of increasing the efficiency of intellectual processing of linguistic expert information. Also, the paper considers
  methods for visualizing clustering results for interpreting the cluster structure and dependencies
  on a set of text data elements and graphical means of their presentation in the form of
  dendograms, scatterplots, VOS similarity diagrams, and intensity maps. To compare the quality of
  the algorithms, internal and external performance metrics were used: "V-measure", "Adjusted
  Rand index", "Silhouette". Based on the experiments, it was found that it is necessary to use a
  hybrid approach, in which, for the initial selection of the number of clusters and the distribution of
  their centers, use a hierarchical approach based on sequential combining and averaging the characteristics
  of the closest data of a limited sample, when it is not possible to put forward a hypothesis
  about the initial number of clusters. Next, connect iterative clustering algorithms that provide
  high stability with respect to noise features and the presence of outliers. Hybridization increases
  the efficiency of clustering algorithms. The research results showed that in order to increase the
  computational efficiency and overcome the sensitivity when initializing the parameters of clustering
  algorithms, it is necessary to use metaheuristic approaches to optimize the parameters of the
  learning model and search for a global optimal solution.

• IDENTIFICATION OF KEY TECHNOLOGIES BASED ON COLLECTION AND ANALYSIS OF DATA FROM OPEN RUSSIAN-LANGUAGE SOURCES

  А.G. Bondarenko , А.G. Kravets

  144-159

  2025-07-24

  Abstract ▼

  This article is devoted to the development and approbation of a new approach to the collection, processing and analysis of open data in the Russian language for identification of key technological trends. To solve the problem of formation and subsequent analysis of structured datasets methods of web scraping, natural language processing and analysis of time-series have been developed and implemented via programming. The approach described in the article has been applied for the first time in order to extract and structure information from scientific articles, news resources and patent documentation in the Russian language for the first time. As a result of analyzing the obtained dataset of scientific publications, 30 most frequently mentioned bigrams and the same number of trigrams of technological terms have been identified. Based on the frequency analysis of bigrams and trigrams, key technological terms were   identified which then were used for complex filtration on key technologies. Complex filtration enabled to fulfill the search of patents in Russian and their collection for further analysis. As a result of preprocessing of the obtained patent data time series of patent activity have been formed. The programme system of key technological identification has been implemented in JavaScript and Python using Selenium and BeautifulSoup libraries for web scraping, NLTK and Scikit-learn for text data processing and analysis. The study focused on the dynamics of the development of key technologies over time has allowed to identify periods of intensive patent activity and declining interest in this or that kind of technology. The results presented in the article provide a basis for further development of machine learning methods for the purpose of predicting technological development and identifying promising areas of applied research.

• MODERN APPROACHES TO NATURAL FIRE MONITORING AND FORECASTING: REVIEW AND CONCEPT OF AUTONOMOUS UAV-BASED SYSTEM

  N.D. Boldyrev , V. V. Gilka , А.S. Kuznetsova , D.А. Morozov

  58-80

  2025-12-30

  Abstract ▼

  Natural fires cause serious damage to ecosystems, the economy, and public safety every year, and timely detection of fires and prediction of their development increases the speed of response to threats and allows for optimal allocation of resources during emergency response. Existing monitoring methods are limited by the speed of detecting fire outbreaks and the speed of their further spread, which reduces the effectiveness of rescue services. To solve this problem, heterogeneous data sources can be used, including unmanned aerial vehicles (UAVs), distributed sensor networks, mobile field observation systems, ground-based thermal imaging stations, etc., which can contribute to a more accurate analysis of the current situation and improve the reliability of predictive models of fire spread. The aim of the study was to develop a concept for an automated approach to monitoring and predicting wildfires based on unmanned aerial vehicles. We believe that this approach will improve the speed of detecting fire outbreaks and the accuracy of predicting their spread. The tasks include analyzing existing monitoring methods, developing a concept for a system that integrates multispectral imaging, optimized data transmission, automatic segmentation, and forecasting based on machine learning, as well as ensuring interaction between the operator and alert specialists. The work used methods of collecting, analyzing, and transmitting data from UAVs, processing multispectral images, machine learning and neural networks for fire detection, image segmentation algorithms and simulation modeling for fire spread prediction, data visualization to support decision-making by operators and administrators, logging and analysis of results for model training, software engineering, and human-computer interaction technologies. The system will reduce the time required to detect and predict fires, enable operators to launch multiple drones simultaneously, and automate the processing of data received from them. Process automation will reduce emergency response times and staffing levels, improve resource allocation, increase forecast accuracy, and improve the timeliness of emergency service notifications. This will help reduce damage from wildfires and improve the safety of people and ecosystems. Despite the progress made in addressing this challenge, the comprehensive system described in this article does not yet exist in its entirety in Russia, the CIS countries, or in Western and Asian countries. Although individual components, such as UAVs for monitoring and artificial intelligence (AI) for data analysis, are already in active use, there is currently no integrated solution that combines all elements (drone control, near real-time fire spread prediction, data transmission, and interaction with emergency services). does not currently exist. This concept represents a new approach that could become a breakthrough technology for combating natural disasters.