# Concatenated Attention: A Novel Method for Regulating Information Structure Based on Sensors
## 4 Discussion  
The experimental results validate the effectiveness and generalizability of the proposed method across multiple datasets. Compared to the original MHSA method, the proposed approach achieved higher average accuracy. The improvement is consistent across diverse datasets, ranging from general-purpose image classification benchmarks to  
Figure 10: The ROC curve of the CA method on the Crack dataset.  
Figure 9: The ROC curve of the MHSA method on the Crack dataset.  
specialized datasets captured under challenging conditions, underscoring the versatility of the proposed approach. This demonstrates that the processes of splitting, weighted summation, and recombination within the self-attention mechanism can improve network performance without increasing computational overhead. By introducing finer-grained control over intermediate variable interactions, the CA method enhances the network's ability to capture subtle relationships in the data, leading to improved feature representation and decision making. The strength of the CA method lies in its ability to enrich the representation of intermediate variables. Furthermore, despite challenging conditions such as low-light environments and surface contaminants in real-world applications, the results on the Crack dataset confirm the robustness of the CA method. The robustness highlights its potential for deployment in environments where data quality or illumination conditions may vary significantly, such as underwater monitoring or industrial inspection systems.  
The lack of statistical significance for the CA method's improvement on the Flower-102, Pet-37, and Crack datasets can be attributed to several factors. First, the small sample size or limited number of repetitions might have led to wider confidence intervals, making it difficult to detect subtle differences. Second, the intrinsic characteristics of these datasets, such as balanced class distributions, high-quality images, or low noise levels, likely reduced the potential for significant performance gains, as the MHSA method already performed effectively in capturing key features. Additionally, on the Crack dataset, the model's accuracy was near its upper limit, leaving little room for further improvement. Together, these factors suggest that the datasets themselves and the experimental setup may have constrained the measurable advantages of the CA method.  
From the perspective of prior research, the CA method aligns with the trend of optimizing neural network architectures to improve accuracy without significantly increasing computational demands. This alignment is particularly evident in its compatibility with existing attention-based architectures, enabling seamless integration without requiring major modifications to network structures or hyperparameters. The method effectively addresses data limitations and environmental challenges, making it a practical and scalable solution for applications that demand both high precision and computational efficiency. It provides a practical solution for real-world applications such as defect detection and image classification under adverse conditions. Moreover, the CA method's emphasis on maintaining computational efficiency while improving accuracy ensures its applicability in resource-constrained scenarios, paving the way for broader adoption in edge computing and real-time processing tasks.  
However, the improvements in CA methods on some datasets, while showing improvements in accuracy, are not statistically satisfactory. This suggests that performance gains may depend on dataset characteristics, such as class balance or intrinsic task difficulty. Future work could focus on understanding and mitigating these dependencies.