## 2 Materials and Methods
### The Framework of AUV Drift Track Prediction  
To help solve the problem of AUV drift in different ocean layers, an AUV drift track prediction framework based on a modified neural network is proposed. The framework consists of two different modules, as shown in Figure 3. As can be seen in Figure 3a, it is the base model predictor based on LSTM, which realizes the basic prediction of AUV drift track in real time and obtains the prediction result \(y_{t}(x,y,z)\). Figure 1: AUV drifts and floats in the deep ocean and thermocline under the combined action of 18 factors, resulting in six degrees of freedom of motion. Figure 2: Variation curves of temperature, salinity, and density with ocean depth. (**a**) The salinity variation curve with depth. (**b**) The temperature variation curve with depth. (**c**) The density variation curve with depth. Long short-term memory (LSTM) [20] is a special RNN, which is mainly used to solve the problem of gradient disappearance and gradient explosion during long sequence training. Compared with RNN, which has only one transmission state \(h_{t}\), LSTM has two transmission states, one cell state \(C_{t}\) and one hidden state \(h_{t}\). The internal structure of LSTM is to control the transmission state through the three gated states. Based on the \(\sigma\) function, the extracted effective information is filtered, and each component is rated (0-1). The higher the rating, the more memories will enter the unit state. Therefore, LSTM can perform better in longer sequences than ordinary RNN. Figure 2(b) is a modified neural network model based on the emotion modulation mechanism proposed in this paper. The model includes two different modules, which are Proactive Modulation Module and Response Modulation Module. The correction track relies on the prediction results \(y_{t}\) of the base prediction model. Since the depth of AUV prediction in the deep layer is only related to the residual buoyancy which is mainly related to density. It can be seen in Figure 1(c) that the density of the deep ocean layer changes more slowly, so the base model is more accurate for depth prediction. Therefore, the ocean stratification of AUV is judged in real time by depth. According to the depth variation rule of thermocline in study [21], the depth of the thermocline in the sea area with the data obtained in this paper is 200 meters below sea level. By judging the \(z\) variables in \(y_{t}\), the different correction modules are entered. When \(|z|\geq 200\), the prediction results of the base prediction model in the deep ocean layer is fine-tuned by the Proactive Modulation Module to obtain the prediction results \(y_{T}\). When \(|z|<200\), the Response Modulation module corrects the prediction results of the base model in real time when the marine environment changes greatly after the AUV drifts to the thermocline. The results \(y_{A}\) are obtained by real-time corrections of the prediction results of the base model. The calculation method of the framework results is as Equations (1)-(3):  
\[y_{t}=f(x_{t}\cdot W+y_{t-1}\cdot V+b) \tag{1}\]  
\[y_{T}=f_{T}\big{(}(\lambda y_{t}\cdot\mu(t))w_{q}+x_{t1}\cdot v_{q})\cdot w_{T }+\varphi \tag{2}\]  
\[y_{A}=f_{A}((y_{t},x_{t2})\cdot W_{s}+\varphi) \tag{3}\]  
Since the AUV track prediction problem is dependent on the time dimension, so the base model of the AUV drift track prediction framework is selected as the LSTM model with higher prediction accuracy for short sequences [22]. The structure of the AUV track prediction model based on LSTM is shown in Figure 4.  
Figure 3: AUV drift track prediction framework based on a modified neural network. The AUV time series data set is \(X(x_{0},x_{1},x_{2}\cdots x_{17})\). When AUV drifts in the deep ocean layer, the main influencing factor is \(x_{t}\), which is expressed as in Equation (4):  
\[