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Your search keyword '"Wu, Yuanting"' showing total 244 results
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244 results on '"Wu, Yuanting"'

1. Deep Learning Based Solar Cell Recognition for Optical Wireless Power Transfer

Author

Huang, Sida, Wu, Yuanting, and Nguyen, Dinh Hoa

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Electrical Engineering and Systems Science - Systems and Control
Abstract

Optical wireless power transfer (OWPT) is a technology that wirelessly transmit light energy from an optical transmitter to an optical receiver, usually a solar cell. In order to achieve the highest transmission efficiency, the solar cell receiver should be accurately aligned with the optical transmitter. Hitherto, only a few works have been existed for solar cell recognition in presence of complex backgrounds. In this paper, we employ a deep learning approach based on Yolov5-Lite for the solar cell recognition purpose, due to its lightweight, fast and easy to deploy on hardware characteristics. Our tests show a high accuracy of the employed deep learning model with the highest F1 score of 91% and mAP of 94.8%. Therefore, this deep learning model is highly promising for use in OWPT systems to precisely align optical transmitters and solar cell receivers., Comment: In Proceedings of The International Council on Electrical Engineering (ICEE) Conference 2024

Published
2024

32. Design of Visualized Ratiometric Mechanoluminescent Materials for High‐Resolution Pressure Sensing.

Author

Zhang, Pengfei, Wu, Yuanting, Guo, Ziyi, Song, Yunmei, Hu, Jingyue, Liu, Haojin, and Zhao, Lei

Subjects
*RARE earth ions, *TRANSITION metal ions, *PALETTE (Color range)
Abstract

Optical sensing technology based on mechanoluminescent (ML) materials offers advantages such as rapid response, real‐time monitoring, visual signal output, and multi‐functional sensing, making it promising for various applications. However, traditional sensing techniques based on the absolute intensity of ML suffer from issues of low accuracy and large errors, which have a significant impact on the reliability and visual effectiveness of the sensor. To address these problems, this study employs a ratiometric ML pressure sensing method. By utilizing the primary color recognition of human cone cells, transition metal ion Mn2+ and rare earth ion Tb3+ are selected as luminescent centers. The rich intermediate colors ensure a wide range of colors that are easily distinguishable by the human eye. Through studies involving mechanical stimulation and similar high‐energy radiation, it is confirmed that the differences in sensitivity between rare earth ions and transition metal ions, as well as the contributions of shallow trap states, are the reasons for the dynamic changes in the ratiometric ML intensity. Finally, a conceptual application of pressure sensing is realized by combining homogeneous gas compression with pipeline monitoring for dynamic visualization, self‐referencing, and high‐precision mechanical sensing. [ABSTRACT FROM AUTHOR]

Published
2024
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