# Research on Pressure Exertion Prediction in Coal Mine Working Faces Based on Data-Driven Approaches
## 2 Materials and Methods
### Real-Time Classification Method for Pressure Labels of Coordinate Units  
This is the second step in predicting pressure exertion in the working face, which involves classifying pressure labels for coordinate units based on predicted support resistance data. Section 2.2 discussed a classification method for pressure labels using historical data, which employs clustering algorithms to categorize labels based on differences between samples, relying on a large number of data samples. However, the predicted support resistance data samples are limited, making it difficult to achieve classification through this unsupervised algorithm. Establishing a supervised classification model using deep learning, which learns the correlation between sample input features and classification labels through model training, enables the model to automatically discern the classification label for each cycle of support resistance prediction results. This is an effective modeling approach for the second step. There are two deep-learning modeling schemes in this step, and their performances need to be compared experimentally to determine the optimal choice. Scheme 1 is as follows: each support coordinate unit is considered as a prediction sample, where the input data are the spatiotemporal feature matrix of the support resistance for that particular support cycle, and the output data are the corresponding pressure label value. This modeling approach transforms the problem into an image-level classification problem, where the feature learning content of the model aligns with the classification method for pressure labels based on all historical data, ensuring the validity of the classification. In this modeling scheme, the input image size is 5 \(\times\) 5, and the number of output classification labels is 4, with a computational complexity lower than that of handwritten digit recognition. Therefore, the AlexNet model can meet the modeling performance requirements for this problem [35]. Adjustments have been made to the AlexNet model structure to accommodate the input and output data sizes. The adjusted model structure is shown in Figure 2. The model consists of five convolutional layers and three fully connected layers. The 1 st, 2 nd, and 5 th convolutional layers are followed by activation layers and pooling layers, while the 3 rd and 4 th convolutional layers are only followed by activation layers. The model hyperparameters are as follows: the convolutional kernel size is 3 \(\times\) 3, the stride is 1, the pooling type is max pooling, the learning rate is 0.001, the optimizer is Adam, and the Dropout rate is 0.5. Scheme 2 involves constructing a feature matrix for the classification model using all predicted stent resistance values for advanced coordinate units, with each feature matrix representing a prediction sample. This modeling approach transforms the problem into a pixel-level classification issue within the field of image segmentation, effectively addressing the coupling of global and local features, although its effectiveness remains unknown. The FCN (Fully Convolutional Network) model converts the fully connected layers of an image classification network into convolutional layers, forming a fully convolutional neural network that enables end-to-end training. Through deconvolution for upsampling and the use of skip architectures to combine semantic and representational information, it produces accurate and precise segmentation. Recognized as a pioneering work in the field of semantic segmentation, the FCN model can meet the performance requirements of this modeling approach [36; 37]. Adjustments have been made to the FCN model structure to accommodate the input and output data dimensions. The adjusted model structure is shown in Figure 3. The model hyperparameters are as follows: the convolutional kernel  
Figure 2: Real-time classification model for pressure labels based on image classification.