Статьи и монографии:

1. Анализ прочностных характеристик различных вариантов исполнения основных несущих элементов демонстратора вертикального взлета и посадки / Д.А. Малых //Вестник Московского авиационного института.–2024.–Том 31 № 1.– C.67-74
2. Малых, Д.А. АНАЛИЗ ПРОЧНОСТНЫХ ХАРАКТЕРИСТИК РАЗЛИЧНЫХ ВАРИАНТОВ ИСПОЛНЕНИЯ ОСНОВНЫХ НЕСУЩИХ ЭЛЕМЕНТОВ ДЕМОНСТРАТОРА ВЕРТИКАЛЬНОГО ВЗЛЕТА И ПОСАДКИ / Д.А. Малых, Д.В. Куплевацкий, В.В. Варкентин //Молодёжь и будущее авиации и космонавтики : Сборник аннотаций конкурсных работ XV Всероссийского межотраслевого молодёжного конкурса научно-технических работ и проектов в области авиационной и ракетно-космической техники и технологий, Москва, 20–24 ноября 2023 года.–2023.– C.100-100
3. Forecasting the Passage Time of the Queue of Highly Automated Vehicles Based on Neural Networks in the Services of Cooperative Intelligent Transport Systems / Shepelev, V. //Mathematics.–2022.–Vol. 10 No. 2
4. Predicting the Destruction of Composite Materials Using Machine Learning Methods / Shabley, A. //Transportation Research Procedia.–2022.–Vol. 68.– P.191-196
5. Study of Strength Properties of the Designed Tank for Marine Transportation of Liquefied Natural Gas in the Arctic Conditions / Peshkov, R. //Transportation Research Procedia.–2022.–Vol. 68.– P.819-824
6. Варкентин, В.В. Проектирование конструкции активной части электрической машины тягового двигателя для беспилотных летательных аппаратов / В.В. Варкентин, А.И. Согрин, В.Б. Фёдоров //Вестник Южно-Уральского государственного университета. Серия: Машиностроение.–2022.–Том 22 № 2.– C.5-19
7. Myakotin, D. Classification of Network Traffic Using Generative Adversarial Networks / D.. Myakotin, V.. Varkentin //Proceedings of the 2021 IEEE International Conference "Quality Management, Transport and Information Security, Information Technologies", T and QM and IS 2021.–2021.– P.519-525
8. Panyushkin, G. Network Traffic and Ensemble Models in Machine Learning / G.. Panyushkin, V.. Varkentin //Proceedings of the 2021 IEEE International Conference "Quality Management, Transport and Information Security, Information Technologies", T and QM and IS 2021.–2021.– P.526-532
9. Starodubtsev, D. Classifications of Network Traffic Using Long Short-term Memory / D.. Starodubtsev, V.. Varkentin, A.. Minbaleev //Proceedings of the 2021 IEEE International Conference "Quality Management, Transport and Information Security, Information Technologies", T and QM and IS 2021.–2021.– P.544-550
10. Alexander, G. Determining vehicle speed based on video using convolutional neural network / G.. Alexander, V.. Varkentin, N.. Goryaev //Transportation Research Procedia.–2020.–Vol. 50.– P.192-200
11. Boyko, A. Development Application for Traffic Classification Using the Neural Network Approach / A.. Boyko, V.. Varkentin, A.. Minbaleev //2020 International Multi-Conference on Industrial Engineering and Modern Technologies, FarEastCon 2020.–2020
12. Cheskidov, P. A Model for Classifying Network Traffic Using Reinforcment Learning / P.. Cheskidov, V.. Varkentin, A.. Shults //2020 International Multi-Conference on Industrial Engineering and Modern Technologies, FarEastCon 2020.–2020
13. Grents, A. Application for Recognition of Road Transport Using TensorFlow / A.. Grents, V.. Varkentin, A.. Fedorov //Proceedings of the 2020 IEEE International Conference "Quality Management, Transport and Information Security, Information Technologies", IT and QM and IS 2020.–2020.– P.214-218
14. Kataev, G. Method to estimate pedestrian traffic using convolutional neural network / G.. Kataev, V.. Varkentin, K.. Nikolskaia //Transportation Research Procedia.–2020.–Vol. 50.– P.234-241
15. Nazarenko, E. Application for Traffic Classification Using Machine Learning Algorithms / E.. Nazarenko, V.. Varkentin, A.. Minbaleev //Proceedings of the 2020 IEEE International Conference "Quality Management, Transport and Information Security, Information Technologies", IT and QM and IS 2020.–2020.– P.269-273
16. Perkova, A. Traffic Sign Recognition Using YOLOv3 / A.. Perkova, V.. Varkentin, A.. Fedorov //Proceedings of the 2020 IEEE International Conference "Quality Management, Transport and Information Security, Information Technologies", IT and QM and IS 2020.–2020.– P.277-282
17. Boyko, A. Advantages and Disadvantages of the Data Collection's Method Using SNMP / A.. Boyko, V.. Varkentin, T.. Polyakova //2019 International Multi-Conference on Industrial Engineering and Modern Technologies, FarEastCon 2019.–2019
18. Nazarenko, E. Features of Application of Machine Learning Methods for Classification of Network Traffic (Features, Advantages, Disadvantages) / E.. Nazarenko, V.. Varkentin, T.. Polyakova //2019 International Multi-Conference on Industrial Engineering and Modern Technologies, FarEastCon 2019.–2019
19. Varkentin, V. Development of an Application for Car License Plates Recognition Using Neural Network Technologies / V.. Varkentin, M.. Schukin //Proceedings of the 2019 IEEE International Conference "Quality Management, Transport and Information Security, Information Technologies" IT and QM and IS 2019.–2019.– P.203-208
20. Varkentin, V. Development of an Application for Localization and Recognition of Road Signs Using Neural Network Technology / V.. Varkentin, V.. Shadehin //Proceedings of the 2019 IEEE International Conference "Quality Management, Transport and Information Security, Information Technologies" IT and QM and IS 2019.–2019.– P.209-214
21. Varkentin, V. Development of an Application for Vehicle Classification Using Neural Networks Technologies / V.. Varkentin, D.. Kolobanov //Proceedings of the 2019 IEEE International Conference "Quality Management, Transport and Information Security, Information Technologies" IT and QM and IS 2019.–2019.– P.215-219
22. Варкентин, В.В. РАЗРАБОТКА ПРОГРАММНОГО ОБЕСПЕЧЕНИЯ АВТОМАТИЗИРОВАННЫХ СИСТЕМ УПРАВЛЕНИЯ ТЕХНОЛОГИЧЕСКИМИ ПРОЦЕССАМИ С ИСПОЛЬЗОВАНИЕМ VISUAL STUDIO И БАЗЫ ДАННЫХ ORACLE / В.В. Варкентин, Т.А. Барбасова //СОВРЕМЕННЫЕ НАУКОЕМКИЕ ТЕХНОЛОГИИ.–2013 № 8-1.– C.78-80