## 3 Materials and Methods
### Dataset Preprocessing
Additionally, the continuity and periodicity of time-series data must be preserved during preprocessing. For applications with significant periodic patterns (e.g., financial or meteorological data), Fourier transforms can extract periodic components, and abnormal frequency components can be filtered. To better suit subsequent model inputs, normalization or standardization is often applied. Normalization scales the data to a range of [0, 1]:  
\[x_{i}^{\prime}=\frac{x_{i}-\min(x)}{\max(x)-\min(x)}, \tag{22}\]  
where \(\min(x)\) and \(\max(x)\) denote the minimum and maximum values of the series, respectively. Standardization, on the other hand, adjusts the data to have zero mean and unit variance:  
\[x_{i}^{\prime}=\frac{x_{i}-\mu}{\sigma}. \tag{23}\]  
Image data augmentation generates new training samples by transforming original images, aiming to enhance model generalization and robustness. This technique introduces controlled perturbations without altering data labels, exposing the model to diverse sample variations, reducing overfitting, and improving resilience to noise and unseen data. In deep learning, augmentation techniques have proven indispensable, especially when training data are limited. Cutout is a simple yet effective augmentation method that simulates local information loss by randomly masking one or more rectangular regions in an image. This forces the model to learn robust global features. Given an input image \(I\in\mathbb{R}^{H\times W\times C}\), where \(H\), \(W\), and \(C\) denote height, width, and channels, respectively, a center point \((x_{c},y_{c})\) is selected randomly, and a rectangular mask of size \(h\times w\) is applied. The augmented image \(I^{\prime}\) is defined as  
\[I^{\prime}(x,y)= \begin{cases}0,&\text{if }x_{c}-\frac{h}{2}\leq x\leq x_{c}+ \frac{h}{2}\text{ and }y_{c}-\frac{w}{2}\leq y\leq y_{c}+\frac{w}{2},\\ I(x,y),&\text{otherwise}.\end{cases}  \tag{24}\]CutMix, a more advanced augmentation method, combines two images and their labels by pasting a cropped region from one image onto another. For two images \(A\) and \(B\) with labels \(y_{A}\) and \(y_{B}\), the augmented image \(I^{\prime}\) is defined as  
\[I^{\prime}(x,y)= \begin{cases}I_{B}(x,y),&\text{if }x_{c}-\frac{y}{2}\leq x\leq x _{c}+\frac{y}{2}\text{ and }y_{c}-\frac{w}{2}\leq y\leq y_{c}+\frac{w}{2},\\ I_{A}(x,y),&\text{otherwise}.\end{cases}  \tag{25}\]  
The new label is computed as a weighted combination:  
\[y^{\prime}=\lambda y_{A}+(1-\lambda)y_{B}, \tag{26}\]  
where \(\lambda=\frac{h\cdot w}{H\cdot W}\) represents the area ratio of the cropped region. Compared to traditional methods, Cutout and CutMix offer significant advantages. Cutout enforces global feature learning by simulating information loss, while CutMix enhances inter-class variability through pixel and label blending. Both methods alleviate data imbalance issues, especially in scenarios involving small samples or uneven class distributions. The selection of augmentation techniques should align with task characteristics and data distribution. For instance, in object detection, methods preserving target regions are preferable, whereas, in classification, occlusion and blending may enhance diversity. Integrating augmentation with model architectures and optimization strategies, such as attention mechanisms, can further improve utilization of augmented data.