At present, the global COVID-19 epidemic has not slowed down. To reduce the contact between people during the epidemic and prevent the epidemic from expanding, we have developed a robot to assist medical staff in patient guidance and communication services. The robot can provide an emergency contact so that users can immediately contact the counter for help. The user does not have face-face contact with the medical staff. When the robot encounters obstacles in the path of travel, the detected event and the time of occurrence are sent back to the back-end system. It also provides security personnel with real-time images and robot control rights to understand the situation and deal with it in real-time.

In recent years, bilingual education has become a critical index, and with its growth, internationalization has been significantly improved in the Republic of China (Taiwan). Therefore, countries worldwide are promoting all-English teaching, taking English as a medium of instruction and indicator of university education while moving toward an internationalized curriculum and teaching excellence. Finding the required number of bilingual teachers is also one of the keys to increasing the index. If information technology can be adopted to reduce the time and cost of bilingual teacher training, it will significantly improve the effectiveness of bilingual education promotion. However, the current traditional online training system only focuses on the delivery of one-way training content, and it cannot assist in judging the training effectiveness of the trainees. In this paper, we integrate the pose estimation technology into the online teacher training system to analyze the interactive content of the trainees during the training. We can assist in recording and interpreting the teaching demonstration process in classroom observation. We also verify the result by comparing our method’s efficiency and judgment accuracy with the traditional way. The results show that our approach is more convenient and cost-efficient.

Many countries have set up ecological coastal preserves for sustainable oceans and ecological conservation and prohibiting people from angling along the shoreline. Although there are regular patrols by law enforcement officers, there are still many anglers who do not comply with the regulations, and intrude into the ecological coastal preserve. This requires a lot of human resources and time for law enforcement officers. Therefore, we developed a fishing detection system by collecting images of anglers’ fishing behaviors. We used a deep learning model with object detection and posture recognition. When anglers engaged in fishing behaviors, the system immediately transmitted the anglers’ geographic locations to the back-end database and notified the law enforcement officers. The results showed that the system’s accuracy rate in determining angling postures was as high as 90%.