We introduce the technology required todevelop a bracket process for installing and verifying FRT bumper sensors for passenger cars. Establish and demonstrate process automation through actual design and manufaturing. We conduct quality inspection of the production process using artificial intelligence and develop technology to automatically detect good and defective products and increase the reliability of the process

This study explores the ways in which sociocultural perspectives on English language education can contribute to teacher education the era of artificial intelligence (AI). Three key words that represent the relationship between sociocultural perspectives and English teacher education—context, interaction, and social practice—can each be linked to the key concepts of criticality, multimodality, and action research. Teachers of English need to be ready for the forthcoming changes in the AI era, for which they must be equipped with a critical ability to focus on issues and needs in the Korean context. This ability can be applied in teaching students various types of interactions, especially those involving the use of computers, and will create opportunities for teachers to conduct research of their own and cultivate a professional teacher identity. This study concludes by recommending substantial changes in the current pre-service and in-service English teacher education programs in accordance with these key concepts.

In order to monitor the long-term condition of structures in nuclear waste disposal system and evaluate the degree of damage, it is necessary to secure quantitative monitoring, diagnosis, and prediction technology. However, at present, only simple monitoring or deterioration evaluation of the structure is being performed. Recently, there is a trend to develop monitoring systems using artificial intelligence algorithms, such as to introduce artificial intelligence-based failure diagnosis technology in nuclear power plant facilities. An artificial intelligence algorithm was applied to distinguish the noise signal and the destructive signal collected in the field. This can minimize false alarms in the monitoring system. However, it is difficult to apply artificial intelligence to industrial sites only by learning through laboratory data. Therefore, a database of noise signals and destructive signals was constructed through laboratory data, and signals effective for quantitative soundness determination of structures were separated and learned. In addition, an adaptive artificial intelligence algorithm was developed to enable additional learning and adaptive learning using field data, and its performance was verified through experiments.