1. The Flood 2.0 Project
Year of implementation: 2023. As part of this project, an intelligent system for forecasting and modeling flood situations based on its own library of machine and deep learning has been implemented.
The project was implemented jointly with students of the Financial University under the Government of the Russian Federation for the Republic of Bashkortostan.
Scientific publications on the project:
1.1. Palchevsky, E.V. Development of an intelligent system for early forecasting and modelling of flood situation on the example of the Republic of Bashkortostan using a proprietary machine and deep learning library / E.V. Palchevsky, V.V. Antonov, N.N. Filimonov, L.L. Rodionova, L.L. Kromina, T. Breikin, A.A. Kuzmichev, A.A. Pyatunin, V.V. Koryakin // Journal of Hydrology. Vol. 633. – Elsevier, 2024. – pp. 130978. (Scopus: Q1, WoS: Q1)
The world's most prestigious scientific journal on hydrology. The article was written based on the results of research work within the framework of the State Assignment.
Link to the publication.
1.2. Palchevsky, E.V. Modeling of flooding zones based on time series forecasting and GIS technologies / E.V. Palchevsky, V.V. Antonov, L.E. Rodionova, L.A. Kromina, A.R. Fakhrullina // Computer Optics. Vol. 48. No. 6 - ISO RAS, 2024. pp. 913-923. (Scopus: Q2, Web of Science: Q3)
Link to the publication.
1.3. Palchevsky, E.V. Method of Data Preprocessing on the Basis of Pulse Neural Network to Improve the Accuracy of Water Level Forecast on the Example of Ufa City of the Republic of Bashkortostan / E.V. Palchevsky, V.V. Antonov, E.A. Makarova, N.A. Kononov, Ya.S. Voyakovskaya // Programmnaya Ingeneria. Vol. 15. No 5. — New technologies, 2024. — pp. 265-272. (RSCI)
Link to the publication.
1.4. Palchevsky, E.V. Development of a method of ensuring control over deviations in the work of automated systems of the enterprise on the example of the electric power industry / E.V. Palchevsky, V.V. Antonov, V.N. Shkarov, L.E. Rodionova, O.N. Smetanina // Programmnaya Ingeneria. Vol. 15. No 6. — New technologies, 2024. — pp. 322-328. (RSCI)
Link to the publication.
2. The Energetika Project
Year of implementation: 2023. As part of this project, an intelligent system has been implemented to predict electricity consumption in the Republic of Bashkortostan based on its own library of machine and deep learning.
The project was implemented jointly with students of the Financial University under the Government of the Russian Federation for the Republic of Bashkortostan.
Scientific publications on the project:
2.1. Palchevsky, E.V. The Concept of Formation of Intelligent Control Systems of Power Supply of Urban Networks / E.V. Palchevsky, V.V. Antonov, L.A. Rodionova, L.A. Kromina, A.R. Fakhrullina, L.I. Baimurzina, E.A. Rodionov // Mekhatronika, Avtomatizatsiya, Upravlenie. Vol. 24, No 4. – New Technologies Publishing House, 2023. – pp. 190–198. (Scopus: Q3)
Link to the publication.
2.2. Palchevsky, E.V. Intelligent forecasting of electricity consumption in managing energy enterprises in order to carry out energy-saving measures / E.V. Palchevsky, V.V. Antonov, L.A. Rodionova, L.A. Kromina, A.R. Fakhrullina // Mekhatronika, Avtomatizatsiya, Upravlenie. Vol. 24, No. 6. – New Technologies Publishing House, 2023. – pp. 307-316. (Scopus: Q3)
Link to the publication.
2.3. Palchevsky, E.V. Artificial Neural Network for Forecasting Electricity Consumption in Energy Enterprises / E.V. Palchevsky, V.V. Antonov, L.E. Rodionova, A.R. Fakhrullina, L.A. Kromina // Programmnaya Ingeneria. Vol. 14. No. 1. — New technologies, 2023. — pp. 34-41. (RSCI)
Link to the publication.
2.4. Palchevsky, E.V. Method for improving the accuracy of predictive values of time series based on the imputation of historical data / E.V. Palchevsky // Programmnaya Ingeneria. Vol. 14. No. 6. — New technologies, 2023. — pp. 301-306. (RSCI)
Link to the publication.
2.5. Palchevsky, E.V. Development of a method of ensuring control over deviations in the work of automated systems of the enterprise on the example of the electric power industry / E.V. Palchevsky, V.V. Antonov, V.N. Shkarov, L.E. Rodionova, O.N. Smetanina // Programmnaya Ingeneria. Vol. 15. No 6. — New technologies, 2024. — pp. 322-328. (RSCI)
Link to the publication.
3. The Flood Project
Year of implementation: 2021. As part of this project, an intelligent flood forecasting system based on an artificial neural network has been implemented.
The project was implemented jointly with students of Ufa State Aviation Technical University (now Ufa University of Science and Technology) for the Republic of Bashkortostan.
Scientific publications on the project:
3.1. Palchevsky, E.V. A system based on an artificial neural network of the second generation for decision support in especially significant situations / E.V. Palchevsky, V.V. Antonov, R.R. Enikeev, T. Breikin // Journal of Hydrology. Vol. 616. – Elsevier, 2023. – pp. 128844. (Scopus: Q1, WoS: Q1)
The world's most prestigious scientific journal on hydrology. The article is based on the results of the Flood project in 2021 as part of the State Assignment.
Link to the publication.
3.2. Palchevsky, E.V. Threats complex distributed systems parrying based on their development prognostication / E.V. Palchevsky, O.I. Khristodulo, S.V. Pavlov // Advances in Social Science, Education and Humanities Research. Vol. 483. – Atlantis Press, 2020. – pp. 191-194. (Web of Science)
Link to the publication.
3.3. Palchevsky, E.V. Threat prediction in complex distributed systems using artificial neural network technology / E.V. Palchevsky, O.I. Khristodulo, S.V. Pavlov // CEUR Workshop Proceedings. Vol. 2763. — CEUR Workshop, 2020. — C. 284-289. (Scopus)
href="https://palchevsky.ru/r.php?r=https://ceur-ws.org/Vol-2763/CPT2020_paper_s7-1.pdf " target="_blank" rel="noreferrer noopener">Link to the publication.
3.4. Palchevsky, E.V. Intelligent data analysis for forecasting threats in complex distributed systems / E.V. Palchevsky, O.I. Khristodulo, S.V. Pavlov, A.M. Kalimgulov // CEUR Workshop Proceedings. Vol. 2744. — CEUR Workshop, 2020. — C. 285-296. (Scopus)
Link to the publication.
3.5. Palchevsky, E.V. Decision support system based on application of the second generation neural network / E.V. Palchevsky, V.V. Antonov // Programmnaya Ingeneria. Vol. 13. No. 6. — New technologies, 2022. — pp.301-308. (RSCI)
Link to the publication.
3.6. Palchevsky, E.V. Forecasting based on an artificial neural network of the second generation to support decision-making in particularly significant situations / E.V. Palchevsky, V.V. Antonov, R.R. Enikeev // Software products and systems. Volume 35. Issue 3. – Publishing house: CJSC Scientific Research Institute "Centerprogramsystem", Tver, 2022. – pp. 488-503. (RSCI)
Link to the publication.
4. The project "Intelligent system for creating test tasks"
Year of implementation: 2024. Within the framework of this project, a chatbot (a small analogue of ChatGPT) has been implemented based on the LLM model to generate responses to various user requests.
The project was implemented jointly with students of the Financial University under the Government of the Russian Federation.
The project was implemented as part of a doctoral thesis.
Scientific publications on the project: in progress…
5. The project "Intelligent system for forecasting pressure in a gas pipeline and modeling emergency situations"
Year of implementation: 2025.
The main feature of this system is the advanced LSTM architecture, which is integrated with physical models for accurate prediction of accidents on gas pipelines. The model uses adaptive gates that are dynamically adjusted to take into account external parameters (for example, temperature and humidity), which significantly improves the accuracy of forecasts. A specialized 4D tensor has been developed for deep analysis of multidimensional data, taking into account physical limitations, and a built-in modeling algorithm visualizes the consequences of accidents (explosion radius and impact zone).
The proposed approach can be adapted to various systems in a wide variety of fields, for example, the oil and gas industry (forecasting and monitoring the condition of pipelines, accident prevention and optimization of technological processes), the electric power industry (forecasting and modeling of emergencies on power grids), hydrology (forecasting and modeling of flood situations), the chemical industry (control of chemical reactions, prevention of leaks and ensuring the safety of technological installations), water supply systems (leak detection, network optimization and forecasting possible emergencies), transport infrastructure (forecasting the wear of bridges, railways and other facilities where the dynamics of physical activity is important).
The project was implemented independently as part of a doctoral thesis.
Scientific publications on the project: in progress…