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001-es BibID:	BIBFORM132864
Első szerző:	Korwar, Ganesh
Cím:	Design and Development of an Efficiently Harvesting Buoy-Type Wave Energy Converter / Ganesh Korwar, Timotei István Erdei, Nitin Satpute, Atul P Kulkarni, Attila Szántó
Dátum:	2025
ISSN:	2076-3417
Megjegyzések:	This paper presents an innovative approach to efficiently harvesting energy from ocean waves through a buoy-type Wave Energy Converter (WEC). The proposed methodology integrates a buoy, a Mechanical Motion Rectifier (MMR), a Motion Rectifier (MR), an Energy Storage Element (ESE), and an electric generator. A MATLAB-2023 model has been employed to assess the electrical power generated under varying wave heights and frequencies. Experimental data and numerical simulations reveal that the prototype Wave Energy Harvester (WEH) achieved a peak voltage of 6.7 V, peak power of 3.6 W, and an average power output of 8.5 mW, with an overall efficiency of 47.2% for the device`s actual size. Additionally, a theoretical analysis has been conducted to investigate the impact of incorporating additional buoys on the electrical power output.
Tárgyszavak:	Műszaki tudományok Villamosmérnöki tudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk numerical simulation MMR MR ESE energy harvesting
Megjelenés:	Applied Sciences-Basel. - 15 : 20 (2025), p. 1-25. -
További szerzők:	Erdei Timotei István (1990-) (mechatronikai mérnök) Satpute, Nitin Kulkarni, Atul Szántó Attila (1993-) (gépészmérnök)
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001-es BibID:	BIBFORM118617
035-os BibID:	(WoS)001170884500001 (Scopus)85192444966
Első szerző:	Sarvajcz Kornél (villamosmérnök, mechatronikai mérnök)
Cím:	AI on the Road: NVIDIA Jetson Nano-Powered Computer Vision-Based System for Real-Time Pedestrian and Priority Sign Detection / Sarvajcz Kornel, Ari Laszlo, Menyhart Jozsef
Dátum:	2024
ISSN:	2076-3417
Megjegyzések:	Advances in information and signal processing, driven by artificial intelligence techniques and recent breakthroughs in deep learning, have significantly impacted autonomous driving by enhancing safety and reducing the dependence on human intervention. Generally, prevailing ADASs (advanced driver assistance systems) incorporate costly components, making them financially unattainable for a substantial portion of the population. This paper proposes a solution: an embedded system designed for real-time pedestrian and priority sign detection, offering affordability and universal applicability across various vehicles. The suggested system, which comprises two cameras, an NVIDIA Jetson Nano B01 low-power edge device and an LCD (liquid crystal system) display, ensures seamless integration into a vehicle without occupying substantial space and provides a cost-effective alternative. The primary focus of this research is addressing accidents caused by the failure to yield priority to other drivers or pedestrians. Our study stands out from existing research by concurrently addressing traffic sign recognition and pedestrian detection, concentrating on identifying five crucial objects: pedestrians, pedestrian crossings (signs and road paintings separately), stop signs, and give way signs. Object detection was executed using a lightweight, custom-trained CNN (convolutional neural network) known as SSD (Single Shot Detector)-MobileNet, implemented on the Jetson Nano. To tailor the model for this specific application, the pre-trained neural network underwent training on our custom dataset consisting of images captured on the road under diverse lighting and traffic conditions. The outcomes of the proposed system offer promising results, positioning it as a viable candidate for real-time implementation; its contributions are noteworthy in advancing the safety and accessibility of autonomous driving technologies.
Tárgyszavak:	Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk ADAS AI CNN computervision deeplearning embeddedsystem neuralnetworks PyTorch real-time object detection transfer learning artificial intelligence
Megjelenés:	Applied Sciences-Basel. - 14 : 4 (2024), p. 1-24. -
További szerzők:	Ari László Menyhárt József (1988-) (gépészmérnök, létesítménymérnök, lean szakmérnök)
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