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• CONVOLUTIONAL NEURAL NETWORK HYBRID ARCHITECTURE DEVELOPMENT USING SPECTRAL TRANSFORMATIONS

  B. V. Kostrov , S.I. Babaev , А.I. Efimov , V. Y. Tarasova

  2026-02-27

  Abstract ▼

  The hybrid convolutional neural network architecture with combining spectral and spatial layers, as well as new methods of subsampling (WalsPooling) and convolution (ConvWals) are proposed. The developed system is used to geographical proximity assess of images pair based on their visual similarity. A pair of different sensors obtained images visual similarity determination is complicated by different scales and sensor tilt angles shooting conditions. Based on the low-altitude image fragment, a search in the database of underlying surface images is performed. The search is performed in the surrounding area of a given route based on the vector of image features, which is formed on the last layer of the convolutional neural network. The system uses the Siamese architecture, since a pair of images must be submitted to the input. The relevance of this problem stems from the need to ensure UAV navigation in the absence or unreliability of a GPS signal. The approach to data set formation and its preprocessing is also considered. The database search is performed in the surrounding area of the route, which reduces computational costs. The experiments include an analysis of the applicability of the proposed layers (WalsPooling, ConvWals) and a comparison with traditional pooling and convolution methods. The paper also presents a linear approximation method with trainable parameters for reducing the dimensionality of the convolutional layer. The main advantage of the approach is its resistance to changes in the scale and angle of shooting due to a combination of spectral and spatial features. The results demonstrate the applicability of the method for UAV navigation in conditions of loss of GPS signal is lost or unreliable. The experiment demonstrated that using images reconstructed after spectral transformation yields the best neural network convergence and mean square error. The developed architecture demonstrates robustness to geometric and brightness distortions, and its quality metrics (Precision = 0.728, Recall = 0.800, F1 = 0.872) confirm the effectiveness of the approach for visual localization tasks based on images from a surface database.

• USING PROJECT PLANNING TOOLS: GANTT CHART AND NETWORK DIAGRAM

  А.А. Bognyukov , D.Y. Zorkin , I.А. Tarasova

  102-110

  2025-10-01

  Abstract ▼

  An integrative model has been developed that combines calendar planning methods with software functionality (Excel, MS Project) for multi-level project optimization. Central focus is placed on three complementary methodologies: the Gantt chart, network diagram, and critical path analysis, which form the conceptual foundation for effective coordination of project processes. The study details the algorithm for creating a Gantt chart, which visualizes timeframes and task sequences, with emphasis on the functional capabilities of specialized software solutions, including Microsoft Project and Excel, enabling automated construction and adjustment of schedules. Further, the principles of constructing a network diagram, interpreted as a directed graph with edges (tasks) and vertices (events), are elaborated. This approach allows for identifying logical dependencies between project stages and determining the critical path – a sequence of operations with zero time reserves, defining the project’s minimum duration. Practical illustrations of critical path calculations are supported by examples demonstrating its role in optimizing time resources. A key aspect of the study is the analysis of time reserves, aimed at minimizing deadline risks through rational resource reallocation. The methodological framework is supplemented by visualization tools: resource requirement graphs and resource load diagrams, ensuring operational control over material and personnel assets across all project phases. The final element of the planning system is the calendar plan, which structures data on work titles, chronological intervals, and resource intensity. This document serves as an integrative foundation for synchronizing operational activities, ensuring adherence to established deadlines

• HIERARCHY ANALYSIS METHOD: A SYSTEMATIC APPROACH TO DECISION MAKING UNDER UNCERTAINTY

  А.А. Bognyukov, D.Y. Zorkin, I. А. Tarasova

  2025-01-30

  Abstract ▼

  This article provides a detailed examination of the application of the Analytic Hierarchy Process
  (AHP) for evaluating investment alternatives under dynamic market conditions. The AHP methodology
  enables the structuring of complex multi-criteria tasks by dividing them into hierarchical levels and then
  progressively synthesizing the results to reach an optimal decision. Special emphasis is placed on how
  AHP reduces subjectivity when assessing numerous investment-related factors, as the final conclusions
  are based on quantitative indicators and a consistency check of expert judgments. To illustrate the advantages
  of this approach, the article presents a comparative analysis of three companies: Apple Inc.,
  PAO “Segezha Group,” and PAO “Aeroflot.” The evaluation criteria include stock price dynamics, dividend
  yield, market capitalization, volatility (oscillation coefficient), and the influence of industry specifics
  on growth prospects. Apple Inc. stands out primarily due to its high market capitalization and stable dividend
  payouts, whereas PAO “Segezha Group” and PAO “Aeroflot” each have their own strengths, such
  as growth potential in specific market segments and a focus on promising industries. Nevertheless, the
  final results of the multi-criteria analysis indicate that Apple Inc. leads in most of the key metrics overall.
  It should be noted that the significance of AHP extends well beyond academic research. In practice, this
  method is widely used in the corporate sector for risk assessment, investment portfolio formation, and the
  selection of strategic priorities. Its flexibility ensures universal applicability both for large multinational
  corporations and for local enterprises that aim to objectively compare alternatives. The article also high lights the importance of careful data collection and systematization. Errors or inaccuracies at this stage can
  significantly distort the final conclusions, which is particularly critical in making investment decisions. The
  consistency check within AHP makes it possible to promptly identify conflicting evaluations and adjust the
  pairwise comparison matrices. Thus, the authors demonstrate that the Analytic Hierarchy Process is a reliable
  tool for the objective and transparent evaluation of investment projects. By considering a wide range of
  quantitative and qualitative characteristics, AHP enables the development of balanced recommendations
  regarding which assets and companies can deliver the highest returns at a reasonable level of risk.