Евгений Ивлиев

@donstu.ru

Don State Technical University



                 

https://researchid.co/ivliev123

RESEARCH, TEACHING, or OTHER INTERESTS

Electrical and Electronic Engineering, Computer Vision and Pattern Recognition, Mechanical Engineering

5

Scopus Publications

Scopus Publications

  • Investigation of the dynamic characteristics of the TDJT-101 point retarder
    Denis Medvedev, Vyacheslav Grishhenko, Evgeniy Ivliev, Aleksandr Kharchenko, and Pavel Obukhov
    AIP Publishing

  • Automatic Monitoring of Smart Greenhouse Parameters and Detection of Plant Diseases by Neural Networks
    Evgeniy Ivliev, Viktoria Demchenko, and Pavel Obukhov
    Springer Singapore

  • Improving the energy efficiency of sorting centers by identifying objects and digit-letter information with neural networks
    Evgeniy Ivliev, Pavel Obukhov, Viktor Ivliev, Denis Medvedev, and Viktor Martynov
    EDP Sciences
    The article is devoted to the development and analysis of methods of identifying dynamic objects. A neural network with the architecture of SSD MobileNetV2 has been developed to solve the problem of detecting baggage tags and barcodes. Several approaches are considered to solve the problem of identifying digital-letter information: Tesseract, SSD InceptionV2, OpenCV and a convolutional neural network. The efficiency of the methods on real images was checked. It was concluded that electricity consumption can be reduced by 49.43%.

  • Mathematical model of the pneumatic actuator follower system
    Denis Medvedev, Vyacheslav Grishhenko, Viktor Martynov, Evgeniy Ivliev, and Yurii Korol’kov
    EDP Sciences
    The article considers a method of controlling the motion of the output links of the tracking system of pneumatic actuators of technological equipment actuators. Dynamic and qualitative characteristics are improved by means of proportional-integral-differential (PID) controller. The mathematical model of actuator system, which includes power and control parts, has been developed. By calculation experiment the dynamic characteristics of the actuator have been obtained, from which it has been found possible to reduce the energy consumed by the actuator system to about 30%.

  • Comparative analysis of identification of dynamic objects by scale-invariant feature transform and deep neural networks
    E A Ivliev and P S Obukhov
    IOP Publishing
    Abstract The article is devoted to the development and analysis of methods of identifying dynamic objects. A system for identifying information from a luggage tag based on several neural networks with the SSD InceptionV2 architecture has been developed. These neural networks work with sufficiently high accuracy 82-95% and speed 7-10fps. Advantages and disadvantages of application of method of scale-invariant feature transform for identification of luggage tags are considered. The operability of the methods on real images has been tested.