Recently, there has been an increasing attempt to replace defect detection inspections in the manufacturing industry using deep learning techniques. However, obtaining substantial high-quality labeled data to enhance the performance of deep learning models entails economic and temporal constraints. As a solution for this problem, semi-supervised learning, using a limited amount of labeled data, has been gaining traction. This study assesses the effectiveness of semi-supervised learning in the defect detection process of manufacturing using the MixMatch algorithm. The MixMatch algorithm incorporates three dominant paradigms in the semi-supervised field: Consistency regularization, Entropy minimization, and Generic regularization. The performance of semi-supervised learning based on the MixMatch algorithm was compared with that of supervised learning using defect image data from the metal casting process. For the experiments, the ratio of labeled data was adjusted to 5%, 10%, 25%, and 50% of the total data. At a labeled data ratio of 5%, semi-supervised learning achieved a classification accuracy of 90.19%, outperforming supervised learning by approximately 22%p. At a 10% ratio, it surpassed supervised learning by around 8%p, achieving a 92.89% accuracy. These results demonstrate that semi-supervised learning can achieve significant outcomes even with a very limited amount of labeled data, suggesting its invaluable application in real-world research and industrial settings where labeled data is limited.

Jeong, Ye-Eun, Kim, Seung-Rae, Choi, Min-Gyeong, Shin, Eun-Jee, Kim, Dong-Wan, & Cho, Tae-Rin. (2023). “Refining Social Strata Variables in Korean Sociolinguistic Variation Research”. The Sociolinguistic Journal of Korea, 31(4), 33-69. This paper critically assesses existing studies on Korean language variation by social strata, proposing alternative approaches to address issues related to these variables. Previous researches have involved papers hierarchically categorizing social strata variables based on common perceptions or assigning weights to them arbitrarily. However, the impact of social strata variables and their weights on language variation remains unknown without thorough data collection and statistical analysis. Consequently, we emphasize the need for a more comprehensive presentation of social strata variables through an interdisciplinary approach taking into account sociological, economic, and political foundations, and stress the necessity of statistical test, observing the influence of each variable on linguistic forms through regression models