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D. O. Makienko

Observed names: D.O. Makienko, Danila Makienko, Daria Makienko, Д. О. Макиенко, Д.О. МАКИЕНКО
ORCID: 0000-0001-7341-6128
Observed ORCID: 0000-0001-7341-6128
OpenAlex ID: A5090126536
An Approach for Matrix Multiplication of 32-Bit Fixed Point Numbers by Means of 16-Bit SIMD Instructions on DSP
2022 · Ilia Safonov, Anton Kornilov, et al. · Electronics · 6
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Joint Usage of Whole Core Images Obtained in Different Frequency Ranges for the Tasks of Automatic Lithotype Description and Modeling of Rocks’ Petrophysics Properties
2020 · I.A. Seleznev, V.V. Abashkin, et al. · Geomodel · 4
A new state-of-the-art technique for textural and structural microimager data analysis using deep learning algorithms
2025 · G. A. Kossov, V. V. Abashkin, et al. · Georesursy · 1
Evolvement of artificial intelligence and hybrid methods for modeling and optimization in complex systems
2025 · Bekhan Malsagov, Fardiana Ketova, et al. · ITM Web of Conferences · 1
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Quantum computing and artificial intelligence: A synergy for the future
2025 · Muslim Aleroev, Saniyat Arvanova, et al. · ITM Web of Conferences · 1
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Analysis of Open Well Datasets
2024 · D.O. Makienko, I.V. Safonov · Scientific Visualization
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Construction of Texture Feature Profiles Via Whole Core Images
2023 · Д.О. МАКИЕНКО · Optoelectronics Instrumentation and Data Processing
Construction of Texture Feature Profiles Using Whole Core Images
2023 · D. O. Makienko · Optoelectronics Instrumentation and Data Processing
Overview of Open Well Datasets
2023 · D.O. Makienko, I.V. Safonov · International Conference on Computer Graphics and Vision (GraphiCon)
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The paper describes methods of studying the texture properties of rocks from photographs of a whole core using variograms and matrices of co-occurrence. The practical goal of our research is to obtain curves based on images of rocks; for example, core photographs, microresistivity formation images (FMI), and X-ray computerized tomography (CT), which make it possible to segment images depending on the difference of textural characteristics of different areas of the image. Such texture attributes—separately or along with color information—can be used in the tasks of automatic lithotyping, as well as in modeling the petrophysical properties of rocks. Our practical experience in building predictive models from core photographs has shown that the use of versatile information improves the quality of modeling. In some cases, the classification of lithotypes and the prediction of reservoir properties is difficult and leads to erroneous results if we do not use all the available information. In practice, UV images of the core are not always available. There are also daylight core photos, when the color characteristics are not very informative. In such cases, including imaging types such as X-ray CT, texture attributes can be of great help in predictive model building
2021 · I.A. Seleznev, D.O. Makienko, et al. · Geomodel
Новая методика текстурно-структурного анализа имиджей с помощью алгоритмов глубокого обучения
· Г. А. Коссов, В. В. Абашкин, et al. · CyberLeninK (CyberLeninka)
Works count: 11
Citation count: 13
H-index: 2
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