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• 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

• DEVELOPMENT AND IMPLEMENTATION OF A CORPORATE INFORMATION SYSTEM AT THE AvtoVAZ INDUSTRIAL ENTERPRISE

  D.Y. Zorkin , А.А. Bognyukov , Т. Е. Kozhanova

  2026-02-27

  Abstract ▼

  In the context of global industrial digitalization, the development and implementation of corporate information systems (CIS) have become strategically critical for enhancing operational efficiency and competitiveness of enterprises. This study examines the integration case of the ERP system "1C: Enterprise Management" at the AvtoVAZ plant – a key player in the Russian automotive industry. The research aimed to optimize management and production processes through the automation of planning, resource accounting, and coordination of cross-functional interactions. The methodological framework combined analytical, graphical, and comparative approaches, as well as practical testing of solutions in the "1C" software environment. The focus was on designing algorithms for managing production cycles, forming resource specifications, and configuring planning scenarios. The study developed demand forecasting models, analyzed production capacities, and balanced output based on model prioritization (Lada Granta, Vesta, Largus). The system implementation reduced order processing time by 30%, minimized warehouse downtime by 18–22% through synchronized logistics schedules, and improved quality control accuracy via integrated diagnostic tools (CAN-bus, spectrophotometry). Special emphasis was placed on overcoming institutional and technological barriers, including modernizing outdated planning methods, training employees in ERP interfaces, and deploying hybrid cloud solutions to ensure system scalability. The practical significance of the research was confirmed by achieving resource allocation transparency, reducing operational costs, and forming an adaptive production strategy aligned with market dynamics. The results demonstrate that CIS implementation not only optimizes current business processes but also lays the foundation for sustainable enterprise development in the digital transformation era. The acquired experience can be extrapolated to other engineering and industrial enterprises facing challenges in management automation and data integration under competitive pressure. Future research prospects involve analyzing the long-term effects of ERP system adoption, including their impact on innovation potential and supply chain ecosystems.

• A TIME SERIES FORECASTING METHOD BASED ON COGNITIVE FUZZY MODELING AND REGRESSION ANALYSIS

  А.I. Guseva , R.М. Romanov

  157-178

  2025-12-30

  Abstract ▼

  The relevance of the study stems from the low effectiveness of traditional time series forecasting methods under conditions of high uncertainty and limited data, which are typical of weakly formalized systems. The aim of the work is to develop and substantiate a time series forecasting method based on a hybrid approach that integrates cognitive fuzzy modelling, regression analysis, and the analytic network process. Within the study, a systematic review and comparative analysis of existing forecasting methods was carried out, including approaches based on fuzzy logic, neural network and cognitive modelling, as well as ensemble and hybrid methods, and their limitations were identified when dealing with small samples, nonlinear dependencies, and uncertainty. The proposed method includes: the construction of fuzzy cognitive maps, defuzzification of linguistic assessments, clustering of factors, application of the analytic network process to determine priorities, and the formation of a weighted regression model. The model undergoes statistical validation using the , , , and  metrics, as well as diagnostic checks of the assumptions underlying regression analysis, including tests for multicollinearity and autocorrelation. Application of the method reduced  from 0.38 to 0.22,  from 0.30 to 0.18, and  from 11.65 % to 7.12 %, thereby confirming an improvement in the accuracy and robustness of forecasts under limited data compared with classical multiple regression. The novelty of the proposed method lies in the integration of cognitive modelling, regression analysis, and the analytic network process, whereby the strengths of each component compensate for their individual limitations, providing more accurate and robust forecasting under the uncertainty inherent in the system under study. The practical significance of the work consists in the possibility of applying the proposed method to support decision-making and to enhance the validity of forecasts in various subject domains and situations characterized by a limited number of observations, a substantial role of expert judgments, and a complex structure of causal relationships between indicators over time