Факультет Будівництво, архітектура та інфраструктура ( з 2022 року до факультету увійшли наступні кафедри : КТІ; КБВ; КАПЗБМ; КГВФ; КВМ; КЕЦБ)

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ENG: Faculty Construction, architecture and infrastructure

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Efficiency of Energy Storage Control in the Electric Transport Systems
(Politechnika Warszawska; Instytut Transportu, Poland, 2022) Sablin, Oleg I.; Bosyi, Dmytro O.; Kuznetsov, Valeriy; Lewczuk, K.; Kebal, Ivan; Myamlin, Sergiy S.
ENG: The problems of storage and supplying the energy, together with reducing energy intensity for transport, are now crucial for developing sustainable and reliable transport systems. The energy network must be gradually adapted to new loads and power consumption patterns, especially in railways. The article aims to develop the simulation model to investigate the energy storage systems in its use in the electric transport infrastructure. The authors review selected technical solutions for electric energy storage in transport. The theoretical aspects of energy exchange in the energy storage systems were presented as a base for a continuous simulation model of electric transport power supply. In the non-periodic random voltage input applied to the storage unit, it is proposed to use the calculation method based on the Duamel integral to analyze its charge-discharge processes. The resistance functions were applied to analyze the traction power supply mode with variable in time and space by active loads. The simulation showed that the direct connection of the unit to the traction network significantly reduces the traction energy consumption.
Embedded AI for Audio-Based Drone Detection in Critical Railway Infrastructure
(Silesian University of Technology, Poland, 2025) Bosyi, Dmytro O.; Sablin, Oleh I.; Potapchuk, Iryna Yu.; Usenko, Andrii Yu.
ENG: Summary. With the increasing threat of unmanned aerial vehicles to critical railway infrastructure, the need for advanced detection technologies has become more urgent. This paper reviews existing railway monitoring solutions and outlines their limitations in identifying aerial threats. An acoustic analysis is conducted to extract distinctive unmanned aerial vehicle sound patterns using Mel-frequency cepstral coefficients, which serve as primary features for classification. Neural network models are applied to detect and differentiate aerial threats from environmental noise, achieving high recognition accuracy. The study also describes the development of an embedded artificial intelligence system based on STM32 microcontrollers, which combines real-time digital signal processing with efficient on-device neural inference. This solution offers a scalable and energy-efficient platform for decentralized audio-based drone detection in railway security applications.