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• OPTIMIZATION OF THE COMPUTATIONAL SCHEME FOR THE INTERPOLATION OF DECADAL METEOROLOGICAL DATA BY INVERSE DISTANCE WEIGHTING WITH PARALLEL PROCESSING OF MULTIPLE TIME SLICES

  О.М. Golozubov , А.V. Kozlovskiy , E.V. Melnik , Y.E. Melnik , А.N. Samoylov

  22-32

  2025-12-30

  Abstract ▼

  The present study is devoted to solving the problem of computational inefficiency in spatial interpolation of large arrays of decadal meteorological data using the inverse distance weighting method. Traditional approaches involving sequential and independent processing of each time slice demonstrate a linear increase in execution time and significant RAM consumption, which becomes a critical barrier to the rapid construction of detailed and geographically linked raster fields in GeoTIFF format. This significantly limits the use of the method in tasks requiring rapid processing of long-term data archives. The purpose of this work is to develop and validate an optimized computational scheme that can radically reduce time costs while maintaining the completeness and accuracy of the results. The key scientific novelty of the proposed approach lies in the fundamental rethinking of the computational process. Instead of repeating identical operations many times, a scheme is proposed based on a single calculation of the full vector of geodetic distances from each grid cell to all weather stations. This most resource-intensive operation is performed only once. Subsequently, the resulting distance vector is applied to all time slices (decades) to calculate the interpolated values, which eliminates the main computational redundancy and ensures a sublinear dependence of processing time on the number of decades. To further improve performance, a parallel processing mechanism is used at the CPU level, implemented by dynamically dividing the raster into independent computing units (batches). The size of the batches is adaptively adjusted taking into account the available RAM, which guarantees the stability and scalability of the solution on systems of various capacities. The testing of the method on real meteorological data for the period 2015-2024 demonstrated a radical reduction in the execution time. In particular, processing ten decade time slices on a standard laptop takes less than 3.5 minutes, and on a server platform it takes about 3 minutes, which represents a multiple acceleration compared to traditional implementations. Thus, the developed solution makes the operational processing of large spatial and temporal meteorological arrays a reality for a wide range of researchers, opening up new opportunities for climate monitoring, agrometeorology and geoinformation analysis without the need for specialized expensive equipment

• MOBILE-CLOUD SYSTEM FOR SOLVING PHOTOGRAMMETRY TASKS IN INDUSTRY

  A.N. Samoylov, Y. M. Borodyansky

  2021-11-14

  Abstract ▼

  With the development of the capabilities of mobile devices and the increase in the availability
  of wireless communication, the possibilities of building industrial automation systems have
  significantly expanded. The quality of digital photography obtained with a smartphone camera
  makes it possible to build mobile systems based on computer vision: for example, photogrammetry
  systems. There are several factors to consider. The first factor is that the tasks of processing digital
  photography for industrial purposes remain resource-intensive and cannot be fully implemented
  only on the basis of a mobile device. Therefore, it is required to transfer the execution environment
  for resource-intensive tasks to third-party computing power available on demand. The second
  factor is the stability and bandwidth of the communication channel - mobile devices are usually
  needed in remote locations where the deployment of desktop computers is not possible. Therefore,
  using a smartphone only as a camera is not always justified, since the transfer of an unprocessed
  image may take a long time or even be impossible. The third factor hindering the widespread
  use of mobile devices in solving photogrammetric problems is the variability and constant
  emergence of new methods of image processing and analysis. It is necessary to centrally create
  and replenish libraries of such modules. Thus, the creation of mobile photogrammetric measuringsystems requires combining the computing power of cloud services and the mobility of smartphones. The article proposes a method for constructing photogrammetric measuring systems
  based on mobile cloud computing, which provides a dynamic balance of the computational load on
  the nodes of the system, as well as the variability of functionality on mobile devices of users

• AN APPROACH TO BUILDING ADAPTIVE OBJECT ACCOUNTING SYSTEMS USING ARTIFICIAL INTELLIGENCE METHODS

  V.I. Voloshchuk, Ali Garyagdiyev, М.А. Kozlovskaya, Y.E. Melnik, А.N. Samoylov

  2024-11-10

  Abstract ▼

  The use of artificial intelligence methods for object accounting is associated with a number of difficulties,
  such as the variability of objects, the influence of shooting conditions, the overlap of objects in
  complex scenes, the need to work with different scales and high accuracy, as well as the presence of noise
  distortions in the data. The paper proposes an approach based on dynamic learning and adaptation to
  input data to organize the setup and operation of adaptive object accounting systems based on artificial
  intelligence methods, which includes several consecutive stages. The first stage is the semantic analysis of
  the user's request, which is based on the use of vector-graph data structure, which provides the allocation
  of semantically important elements of the request, allowing the system to understand the context of the
  task and adapt the strategy of search and classification of objects. Then follows the stage of automatic collection and preprocessing of data from open sources, which provides the expansion of the training
  sample and increases the stability of the model. The next important step is the generation of the training
  sample. This process includes image retrieval based on query semantics, manual validation and data partitioning,
  and initial training of the system for automatic partitioning. The above steps are repeated until
  the desired system performance is achieved. The iterative process of pre-training based on alternation of
  automatic markup and manual correction allows to reduce time expenditures on formation of training
  samples. The advantage of using vector-graph structure is the formation of more accurate semantic representation
  of information. Data augmentation including rotation, reflection, scaling, changing brightness
  and contrast, and adding noise is applied to enhance the generalization ability of the model. The proposed
  approach is designed to improve the efficiency (as the ratio of system operation time to its setup time) of
  object registration systems, ensuring their adaptability to different tasks and survey conditions

• LARGE LANGUAGE MODELS APPLICATION IN ORGANIZATION OF REPLENISHMENT OF THE KNOWLEDGE BASE ON METHODS OF INFORMATION PROCESSING IN SYSTEMS OF APPLIED PHOTOGRAMMETRY

  А.V. Kozlovskiy, E.V. Melnik, А.N. Samoylov

  2024-08-12

  Abstract ▼

  The article deals with the issues related to the automation of the procedure of synthesis of applied
  photogrammetry systems. Such systems serve to measure and account for objects from images and are
  now widely utilized in various fields of activity, such as mapping, archaeology and aerial photography.
  Increasing availability and mobility of imaging devices has also contributed to the widespread application.
  All this has led to active research aimed at developing methods and algorithms for applied photogrammetry
  systems. Manual tracking of new methods and algorithms of photogrammetric information
  processing for a wide range of application areas is quite difficult, which makes the automation of this
  procedure urgent. The solution proposed in the article is based on the use of a knowledge base of information
  processing methods in applied photogrammetry systems, the main elements of which are a fuzzy
  ontology of the subject area and a database, which is logical, since the information about the subject area
  can be structured quite easily. As a basis for the ontology, an existing solution was taken, which was supplemented
  based on the results of analyzing the current state of the subject area. The resulting ontology
  was further used to search and classify information processing methods in applied photogrammetry systems
  and to populate the knowledge base. Due to the intensification of the development of new methods of information processing in the systems of applied photogrammetry, there is a need to modify the ontology
  and to replenish the database, i.e. to replenish the knowledge base. The Internet is an important source of
  information for this purpose. To automate the search for data on information processing methods and
  ontology modification, it is reasonable to use large language models. To automate data mining of information
  processing methods and to populate the knowledge base, it is useful to use large language models
  that simplify several natural language processing tasks, which include clustering and formation of new
  entities for classification. The corresponding method is described in the paper. For the method the results
  of testing its performance are given. As part of problem solving, a comparative analysis of large language
  models has been carried out, resulting in the RoBERTa model.