## 2 Methodologies
### Convolutional Neural Network  
CNN has achieved considerable success in the image processing industry and is one of the most representative deep learning network designs [38]. The advantages of CNN over other classification algorithms are that CNN has a strong generalization ability and  
Figure 2: The illustration of MSE procedures.  
learns the best features to represent the objects in the photos. Convolutional, Pooling, ReLU and Fully Connected layer make up CNN's four layers [39].  
1. A convolutional layer is made up of several filters that are applied to the input data in layers. The width, height and weights of each filter are used to extract features from the input data. The weights in the filter start out with random values during the training phase and are learnt in the training set. In the convolutional layer, each filter stands in for a feature and finds a match. Therefore, a huge number is generated by the convolutional operation, activating the filter to that feature. CNN uses this procedure to find out the best filters to describe the object [40].
2. The ReLU (Rectified Linear Unit) layer is an activation layer connected after a convolutional layer and causes the network to become non-linear [41]. The ReLU aids the network in learning more difficult decision-making functions and lessens overfitting.
3. The pooling layer is used to minimize the dimensionality of the feature maps while retaining the crucial information [42]. In the pooling layer, a filter applies the pooling operation to the input data by sliding over it in the pooling layer.
4. Input, hidden and output layers make up the MultiLayer Perception (MLP) that constitutes the fully connected layer [43]. The features produced by the CNN are sent to the input layer. A MLP is made up of one or more hidden layers, each hidden layer a series of neurons with weights that will be learned during the training step. There are also a series of neurons in the output layer.  
The convolutional and the dense steps make up the bulk of the typical structure of a CNN model. The former learns which features are most useful to extract from the images and the latter learns how to categorize the features. The structure of the CNN model is displayed in Figure 3.  
The Proposed Intelligent Timber Damage Monitoring Approach Using PZT-Enabled Active Sensing and Intrinsic Multiscale Entropy Analysis  
Aiming at diagnosing timber damage, a novel approach based on multiscale entropy analysis and CNN is proposed in this paper. VMD decomposes PZT generated response signals into multiple sets of BLIMFs. Then MSE values of certain orders of BLIMFs are calculated and adopted as quantitative features denoting timber health conditions. The main steps of the proposed approach are presented as follows:  
1. VMD is used to process the PZT generated response signals and the multiple sets of BLIMFs containing a large number of nonlinear and nonstationary damage characteristics are obtained.
2. Then, MSE values of specific orders of BLIMFs are calculated and used as indicators of the state of the timber health.
3. Two-dimensional feature matrices are constructed and randomly divided into training set, validation set and test set. The data of the training set and validation set are input into CNN for training and the parameters are adjusted during the training process to obtain a neural network model with good recognition.  
Figure 3: The structure of the CNN model.  
(4) The trained CNN model is utilized to justify the data of the test set to realize the monitoring of timber damage.