.png){kind=icon}
返回
Low-Complexity Neural Network-Based Processing Algorithm for QCM-D Signals
.png){kind=icon}
PRE
AI
DOI：10.1109/JSEN.2024.3441716.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This article introduces a novel approach to estimate the oscillation frequency and decay time constant of quartz crystal microbalance with dissipation monitoring (QCM-D) signals using low-complexity neural network (NN) models. The proposed method involves a two-step process: frequency-domain preprocessing using fast Fourier transform (FFT) followed by a shallow neural network for frequency estimation, and time domain preprocessing for envelope detection followed by a second shallow neural network for time constant estimation. The networks were trained and tested on datasets from both a dedicated QCM-D testbench and a numerical simulator. The results demonstrate accurate estimation with errors below 10 Hz for the frequency and 1 mu s for the time constant, making the approach promising for various QCM-D applications. The simplicity of the neural network models facilitates the implementation on embedded platforms, reducing the system complexity and offering potential for cost-effective QCM-D sensing systems.
Keyword:
Neural network (NN)
quartz crystal microbalance with dissipation monitoring (QCM-D)
signal fitting
Neural network (NN)
quartz crystal microbalance with dissipation monitoring (QCM-D)
signal fitting
期刊
IF:
4.5
论文数:
2.2W
被引数:
7.3W
机构
引用论文
.png){kind=icon}
Separation of acidic and neutral saccharides by high-performance gel chromatography on cation-exchange resins in the H+form with acidic eluents在H+形式的阳离子交换树脂上，采用酸性洗脱液通过高效凝胶色谱法分离酸性糖和中性糖。
PREAI
分享收藏
A Bayesian Passive Islanding Detection Method for Inverter-Based Distributed Generation Using ESPRIT
PREAI
分享收藏
查看更多内容