## 5 Conclusions  
In this study, an ultrasonic GW technique was applied to detect artificial defects in 304 SS plate specimens used in an LHSV. The ultrasonic GWs were generated and received by 2.25 MHz ultrasonic transducers with wedges. Five kinds of defects were artificially fabricated on the surfaces of plate specimens. The angles of transmission and reception of ultrasonic GWs through the defects were \(0^{\circ}\), \(45^{\circ}\), and \(90^{\circ}\). As a parameter used to classify the defects, the differences in the number of pixels obtained by the STFT of the received ultrasonic GW signals between no- and with-defect specimens were used. A linear discriminant analysis was performed to classify the defects using the differences in S0 and A0 modes. The experimental data were divided into two groups: one was a calibration set and the other a validation set. An LDA model for the classification of defects was developed and validated. The classification rate for the validation set was about 97%. As a result, the classification accuracy of the proposed method, as shown by the final verification result, is very high compared with those of methods designed in other studies [40; 41; 42; 43]. This result demonstrated that the defects of a specimen to be used for an LHSV can be classified with high accuracy using the newly developed LDA model. Additionally, as it shows high accuracy, we suggest that LDA, which has not been studied much for detecting defects by classifying signals until now, has the potential to be widely used together with other techniques [40; 43], if conditions are met.  
In the future, non-destructive evaluation studies are planned to detect internal defects in the same environmental conditions as the temperature and pressure of the LHSV being operated. In addition, based on this study, pixel-based ultrasound signal detection technology is expected to expand to research that increases accuracy by applying machine learning and deep learning models.  
Conceptualization, H.G.O., G.K. and K.-B.K.; data curation, M.-K.S. and G.K.; formal analysis, H.G.O. and G.K.; funding acquisition, K.-B.K.; investigation, Y.-I.H., M.-K.S., H.G.O., N.C. and G.K.; methodology, G.K.; project administration, K.-B.K.; resources, Y.-I.H., M.-K.S., H.G.O., N.C., G.K. and K.-B.K.; software, H.G.O.; supervision, G.K. and K.-B.K.; validation, Y.-I.H. and G.K.; visualization, Y.-I.H. and M.-K.S.; writing--original draft, Y.-I.H. and [PERSON]-K.S.; writing--review and editing, Y.-I.H., G.K. and K.-B.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by a Development of Safety Measurement Technology for Infrastructure Industry grant funded by the Korea Research Institute of Standards and Science (KRISS-2022-GP2022-0010); and by the Korea Institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry (IPET) through Agriculture, Food and Rural Affairs Convergence Technologies Program for Educating Creative Global Leader, funded by Ministry of Agriculture, Food and Rural Affairs (MAFRA) (Project No. 320001-4), Republic of Korea.  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Step I in S0 Mode** & **Misclassified Data** & **Step II in S0 Mode** & **Misclassified Data** \\ \hline No defects (50) & 4 & No defects (4) & 3 \\ With defects (50) & 7 & With defects (7) & 0 \\ Total (100) & \(11\) & Total (11) & 3 \\ \hline \end{tabular}
\end{table}
Table 4: Classification results of the newly developed LDA model for classifying the defects by validation set.