## 4 Welding Multi-Source Sensing Systems and Their Information Processing and Knowledge Modeling
### Welding Multi-Source Information Knowledge Modelling and Model Test Validation
* A neighborhood rough set-based attribute reduction algorithm is employed to approximate the attributes of the sampled dataset. Subsequently, support vector machines are trained and tested on this reduction set. Table 6 demonstrates the effect of different feature selection methods and classifiers on classification accuracy and the number of features. It can be seen that NRS is most effective in reducing the number of attributes, reducing the features to only 12. Among all classifiers, DOA-SVM performs best with an average accuracy of 98.14%, confirming its superiority. When classical rough set (RS) was used for feature selection, the accuracy of all classifiers was reduced, probably because RS is only applicable to class attributes, which need to be discretized before processing continuous data, and some useful information may be lost. While using NRS, although the number of attributes is reduced significantly, the classification accuracy of DOA-SVM is still close to the level when using the original features, indicating that NRS can effectively retain the features useful for classification while reducing the number of features; in terms of the single training time consumption, the training of the simplified data in SVM reduces about 80% of the time consumption compared to the non-simplified features with the time of 0.11 s, which indicates that data dimensionality reduction is useful to improve diagnostic efficiency and reduce model complexity. Table 7 details the performance of the Neighborhood Rough Set (NRS)-based DOA-SVM model in identifying the six types of welding defects, namely, Porosity, Incomplete penetration, Burn-through, Incompletely filled groove, Good, and weld misalignment. Significantly, the model demonstrates an extremely accurate prediction for \"burn-through\" and \"weld deviation\", with all relevant metrics reaching 1.0, while the model's precision for \"incompletely filled groove\" is 0.989, which is slightly lower than 1.0, but with a recall of 1.0, which ensures the complete detection of such defective instances. For \"Porosity\" and \" good \", although the recall is slightly lower than 1.0, which may result in a small number of samples being missed, all the samples recognized as being in these categories are correct, resulting in a precision of 1.0, while \" incomplete penetration \" is recognized with a precision of 0.937, which is the lowest of the six categories, but with a recall of 1.0 and an F1 score of 0.967, which is near 1.0. These performances show the high efficiency of the DOA-SVM model and the excellence of NRS in feature selection, which can meet the requirements of welding defect detection and identification. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **CART** & **SVM** & **DOA-SVM** & **Feature No.** & **Single Training Time** \\ \hline Raw & 95.81\% & 98.50\% & 99.22\% & 195 & 0.55 s \\ RS & 94.53\% & 95.83\% & 96.22\% & 12 & 0.11 s \\ NRS & 94.01\% & 98.43\% & 98.98\% & 12 & 0.11 s \\ Average & 94.78\% & 97.59\% & 98.14\% & 75.67 & 0.26 s \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of feature selection and classification results. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Serial Number** & **Class** & **Accurate** & **Recall Rate** & **F1 Score** \\ \hline
01 & porosity & 1.0 & 0.969 & 0.984 \\
02 & incomplete penetration & 0.937 & 1.0 & 0.967 \\
03 & burn through & 1.0 & 1.0 & 1.0 \\
04 & incompletely filled groove & 0.989 & 1.0 & 0.994 \\
05 & good & 1.0 & 0.957 & 0.978 \\
06 & weld misalignment & 1.0 & 1.0 & 1.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Statistics of NRS-based DOA-SVM experimental results.