## 2 Proposed Method
### Network Details
#### 2.2.1 Input Module  
Using transform domain analysis methods such as wavelet transform, the time domain signals of vibration and load can be analyzed in the transform domain. In particular, the  
Figure 1: The framework sketch of the proposed method. The input module takes the acceleration measurements from \(N\) different measurement points in the time domain as the input. For each acceleration measurement, a wavelet transform is applied and 9 decomposed components are input to the feature-extraction module, in which the convolutions are utilized. The prediction module would make use of the extracted features in the transform domain, and predict the 9 decomposed components of the load at current timestamp in wavelet domain. In the output module, the predicted decomposed components are transformed back to the time domain to obtain the load at current time. [PERSON] wavelet proposed by the mathematician [PERSON] is suitable for analyzing the local characteristics of the signal. At the same time, the [PERSON] wavelet can also describe the details of the signal in different frequency ranges. It is suitable for analyzing signals with different frequency contents. The basis function of the [PERSON] wavelet is given by  
\[\Psi(\omega)= \begin{cases}\frac{1}{\sqrt{2\pi}}\sin\left(\frac{\pi}{2}\
u\left( \frac{3|\omega|}{2\pi}-1\right)\right)e^{j\omega/2}&\text{if }2\pi/3<|\omega|<4\pi/3\\ \frac{1}{\sqrt{2\pi}}\cos\left(\frac{\pi}{2}\
u\left(\frac{3|\omega|}{4\pi}-1 \right)\right)e^{j\omega/2}&\text{if }4\pi/3<|\omega|<8\pi/3\\ 0&\text{otherwise}\end{cases} \, \tag{1}\]  
where \(\
u(\cdot)\) is an auxiliary function. By truncating the high-frequency or low-frequency part of the basis function \(\Psi(\omega)\) in the frequency domain, the low-pass filter and high-pass filter of the [PERSON] wavelet in the frequency domain can be obtained. After inversely transforming the two types of filters in the frequency domain to the time domain, The low-pass filter coefficient \(h(t)\) and high-pass filter coefficient \(g(t)\) of [PERSON] wavelet can be obtained. In this paper, the 8-order Meyer wavelet is utilized to perform transform domain decomposition on the time domain signals of response. Take the time domain response \(a^{i}(t)\) for measurement point \(i\) as an example, by filtering \(a^{i}(t)\) with low-pass filter \(h(t)\) and high-pass filter \(g(t)\), the low-frequency components of \(a^{i}(t)\), \(a^{i}_{a_{1}}\), and the high-frequency components \(a^{i}_{d_{1}}\) can be obtained, respectively. The scale of \(a^{i}_{a_{1}}\) and that of the \(a^{i}_{d_{1}}\) are the same as that of \(a^{i}(t)\). Then, a down-sampling is applied on the low-pass and high-pass filtered signals, i.e., \(a^{i}_{a_{1}}\) and \(a^{i}_{d_{1}}\), and low-pass and high-pass filtering is applied to obtain the low-frequency component \(a^{i}_{a_{2}}\) and high-frequency component \(a^{i}_{d_{2}}\) at a lower scale. Repeat the down-sampling and filtering, the low-frequency component \(a^{i}_{a_{1}},a^{i}_{a_{2}},\cdots,a^{i}_{a_{8}}\) and the high-frequency component \(a^{i}_{d_{1}},a^{i}_{d_{2}},\cdots,a^{i}_{d_{8}}\) at 8 different scales can be obtained. For each response measurement \(a^{i}(t),i=1,2,\cdots,N\), the low frequency components at the 8-th scale \(a^{i}_{\text{ss}}\), and all 8 high frequency components \(a^{i}_{d_{1}},a^{i}_{d_{2}},\cdots,a^{i}_{d_{8}}\) are used for feature extraction. Therefore, for \(N\) measurement, there are \(9\times N\) components are input to the feature extraction module, with \(1\times N\) for low frequency components and \(8\times N\) for high frequency components. Figure 2 gives an example of the original acceleration measurement signal and the decomposed components.