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.
High-precision wave height detection of triboelectric nanogenerator by using voltage waveforms and artificial neural network
Yuming Lai, Jiahua Ma, Honggui Wen, Huilu Yao, Wenjuan Wei, Lingyu Wan, Xiaodong Yang; High-precision wave height detection of triboelectric nanogenerator by using voltage waveforms and artificial neural network. J. Appl. Phys. 14 September 2023; 134 (10): 104502. https://doi.org/10.1063/5.0165984
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