# Concatenated Attention: A Novel Method for Regulating Information Structure Based on Sensors
## 3 Results  
### Settings  
This section validates the effectiveness of the proposed method through two experimental parts in the image classification subtask. The first part conducts comparative experiments using the controlled variable method on five commonly applied datasets to ensure the reliability of the experimental results. The second part also adopts the controlled variable method but uses a crack dataset captured under varying lighting conditions [35]. To further demonstrate the reliability of the CA method in practical applications, the crack dataset was preprocessed to simulate real-world environments, making the task conditions closer to underwater scenarios. This enables a more accurate evaluation of the proposed method's robustness and adaptability in adverse conditions. In the experiment, the Multi-Head Self-Attention (MHSA) method was selected as the control group. MHSA is a key component in Transformer architectures. It operates by projecting input sequences into Query (Q), Key (K), and Value (V) matrices, which are then split into multiple heads. Each head computes attention scores by measuring the compatibility of Q and K, scaled by the square root of the dimension, followed by a softmax to normalize weights. These weights are applied to V, capturing different aspects of the input features in parallel. Outputs from all heads are concatenated and linearly transformed to form the final output. MHSA enhances representational power by attending to multiple features simultaneously. To maximize the credibility of the results, the same pre-trained model file was employed for every training session. This ensures consistency across experiments and minimizes the potential variability caused by differences in the initial model weights. Since neither the original method nor the proposed method modifies the network's parameters  
Figure 3: Neural network with CA method. or structure, the pre-trained file is compatible with both approaches. To ensure representativeness, the experiments were repeated multiple times, and the results were averaged to account for randomness and provide a fair comparison between the methods. The benchmark datasets applied in the experiments include CIFAR-100 [36; 37], CUB-200 [38; 39], Stanford Flower-102 [40; 41], Pet-37 [42; 43; 44], and Food-101 [45; 46]. These datasets were chosen for their diversity in terms of complexity, class distribution, and feature variations, ensuring that the proposed method is tested across a wide range of scenarios. Each dataset was pre-divided into fixed subsets for training, validation, and testing. The division ratio of the three sub-datasets is determined by the pre-division ratio of the dataset creator. For example, the creator of the Flower-102 dataset has pre-divided the samples into three subsets, so no modification will be made to these three subsets in the experiment. If the dataset is not pre-divided, the dataset will be divided into three subsets according to its size in a ratio of 7:1.5:1.5 or other ratios [47]. Meanwhile, all classes in a single dataset will be divided in the same proportion, so as to eliminate the randomness of the experimental results to the greatest extent. The final result selection strategy involved training network parameters on the training set during each epoch, followed by evaluating these parameters on the validation set. This iterative approach ensures that the network's performance on unseen data is continuously monitored and optimized. After a fixed number of training and validation epochs, the parameters achieving the highest accuracy on the validation set were employed to compute results on the test set. This experimental strategy guarantees that the final reported accuracy reflects the model's ability to generalize to unseen data, thereby ensuring the validity and reliability of the experimental conclusions. The accuracy obtained on the test set represents the final result of the experiment.