## 2 Methods
### CNN and GRU Module  
#### 2.3.1 Convolution Neural Network (CNN)  
Convolution neural network was first proposed in [17] for imageNet competition and reached the first immediately. CNN has more powerful data processing abilities than traditional full-connected neural network. CNN can preserve the neighborhood relations and spatial locality of input data at feature representation [48]. As long as the input data is enough, the CNN model can train itself automatically. The CNN structure is entirely derived from the training data, thus the network is fully adapted to the data and can obtain more representative features. The core idea of CNN is the convolution step, which can be viewed as a correlated process. Assuming that \(\omega_{1},\omega_{2},\dots,\omega_{m}\) represent the weight of 1-D convolution kernel of length \(m\), the computational process of 1-D convolution can be represented as:  
\[y_{t}=f(\sum_{k=1}^{m}\omega_{k}\cdot x_{t-k+1}), \tag{1}\]  
where \(x_{t}\) is the input sample at time \(t\), \(y_{t}\) is the superposition of the information generated at the current moment \(t\) and the information delayed at the previous moment, \(f(\cdot)\) is the activation function. The activation function we choose in this paper is Rectified Linear Unit (ReLU) [49], which can be defined as:  
\[ReLU(x)=\begin{cases}x,x>0,\\ 0,x\leq 0\end{cases} \tag{2}\]  
In this paper, we choose ReLU as our activation function after 1-D convolution processing. The typical way to increase capacity for CNN is to go deeper and wider on structure. Going deeper means stacking more layers, going wider needs more channels, both methods will increase the computational overhead. Inception [34] was proposed to solve this problem. The structure of Inception module applied in this paper is shown as Figure 3:  
Figure 2: The structure of the hybrid parallel network (**a**) The parallel feature extraction module which is the basic component of the hybrid parallel module. (**b**) The structure of the hybrid parallel network which is composed of several the hybrid parallel modules.  
The module \(1\times 1\) Kernel means that the kernel size of 1-D convolution process is \(1\times 1\). Similarly, \(1\times 3\), \(1\times 5\), \(1\times 7\) represent the convolution kernel size as \(1\times 3\), \(1\times 5\) and \(1\times 7\) respectively. After performing convolution operations, Inception module concatenates all output at channel dimension. Different scale of kernel size can help Inception to extract different features in different spatial scale. The next step of inception is Batch Normalization (\(\mathcal{BN}\)) [50]. The \(\mathcal{BN}\) layer accelerates training by normalizing features at one batch:  
\[\mu =\frac{1}{N_{batch}}\sum_{n=1}^{N_{batch}}x_{n}, \tag{3}\] \[\sigma^{2} =\frac{1}{N_{batch}}\sum_{n=1}^{N_{batch}}(x_{n}-\mu)^{2},\] \[\hat{x} =\frac{x_{n}-\mu}{\sqrt{\sigma^{2}+\varepsilon}},\] \[y_{n} =\gamma\hat{x_{n}}+\beta,\]  
where \(N_{batch}\) denotes batch size, \(x_{n}\) denotes the input data, \(\mu\) and \(\sigma^{2}\) denote the mean and variance of the batch, \(\hat{x}\) denotes the normalized data and \(\varepsilon\) is a constant to prevent zero gradient, \(\gamma\) and \(\beta\) are learnable parameter vectors for fixing data, \(y_{n}\) is the fixed output feature.