# Concatenated Attention: A Novel Method for Regulating Information Structure Based on Sensors
## 3 Results
### Comparison on Benchmark  
Table 1 lists the average results and computational resources of the network under the two methods on the CUB dataset, obtained through multiple repetitions. Here, \"MHSA\" and \"CA\" represent the original method and the proposed method, respectively. The results show that the average accuracy of the original method is 69.70, while the proposed method achieves an average accuracy of 71.40. This demonstrates that the split-and-concatenate mechanism in the CA method can improve the learning efficiency of the neural network on the dataset, and the effectiveness of intermediate variables is enhanced through weighted summation. It also confirms that the network structure extracts more critical features from the input data after undergoing the processes of splitting, weighted summation, and recombination. Moreover, the parameter count and floating-point operations per second (FLOPs) remain unchanged for both methods, confirming that the computational overhead introduced by the proposed method is negligible, making it suitable for resource-constrained application scenarios. Using the bootstrap method, the 95 percent confidence interval for the difference in accuracy between the CA method and the MHSA method is \([0.0086,0.0262]\). The mean difference in accuracy is 0.0170. Since the confidence interval does not include 0, this indicates that the CA method's improvement over the MHSA method is statistically significant. To intuitively illustrate the differences between the two methods during training, a line chart was created to show accuracy variations across epochs. The accuracy trends on the training set for both methods are depicted in Figure 4, where the curves exhibit nearly identical trends. This consistency suggests that the proposed method does not disrupt the network's ability to effectively adjust parameters during training, even when the training set size is small. This is because the training set contains significantly more data than the other subsets, allowing the network parameters to be adequately adjusted. The accuracy trends on the validation set are shown in Figure 5, revealing that the proposed method achieves noticeably higher accuracy than the original method after approximately 10 epochs. The similarity in accuracy trends between the CA and MHSA methods on the training set indicates that both methods are equally capable of fitting the training data. This suggests that the core improvements introduced by the CA method do not negatively affect the network's ability to learn from the available training data. However, the noticeable differences on the validation set, with the CA method achieving faster and higher accuracy improvements, demonstrate its enhanced generalization capabilities. This can be attributed to the CA method's ability to extract more diverse and informative features through its split-and-concatenate mechanism. By enriching the representation of intermediate variables and dynamically weighting their contributions, the CA method effectively captures more critical patterns and relationships in the data that are relevant for unseen validation samples. In contrast, the MHSA method may focus more on fitting the training data without adequately capturing the broader feature diversity, leading to a slower improvement in validation accuracy. The superior performance of the CA method on the validation set highlights its robustness and adaptability, making it better suited for handling complex and diverse real-world data distributions. From the curve trends in Figures 4 and 5, we can see that the accuracy change is always on an upward trend. Although there are small fluctuations, it does not affect the overall change. In addition, the curves in both figures are on an upward trend. At the same time, since the samples of the two subsets do not intersect, this shows that the model is not overfitting or underfitting. The average accuracies of the two methods on the CIFAR-100 and Flower-102 datasets are shown in Tables 2 and Table 3, respectively.