High-precision wave height detection of triboelectric nanogenerator by using voltage waveforms and artificial neural network

As we known waves contain important information, however, to realizing high-precision quantification for ocean exploitation and utilization is challenging. In this paper, we proposed a neural network for wave height detection by training the voltage waveform of a triboelectric nanogenerator (TENG)....

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Bibliographic Details
Published in:Journal of applied physics 2023-09, Vol.134 (10)
Main Authors: Lai, Yuming, Ma, Jiahua, Wen, Honggui, Yao, Huilu, Wei, Wenjuan, Wan, Lingyu, Yang, Xiaodong
Format: Article
Language:English
Subjects:
Artificial neural networks
Electric potential
Monitoring
Nanogenerators
Surface waves
Voltage
Wave height
Waveforms
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Summary:As we known waves contain important information, however, to realizing high-precision quantification for ocean exploitation and utilization is challenging. In this paper, we proposed a neural network for wave height detection by training the voltage waveform of a triboelectric nanogenerator (TENG). First, we analyzed the voltage signal obtained using a TENG. Second, we proposed a lightweight artificial neural network model that achieves a minimal monitoring error of 0.049% at low amplitudes and yields better monitoring results than the linear model. The findings presented in this paper enable the measurement of water surface waves and eliminate the influence of external factors on sensor performance. Wave parameters can be obtained using neural networks, and this work provides a new strategy for computational and intelligent applications by using wave data.
ISSN:0021-8979
1089-7550
DOI:10.1063/5.0165984