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Ieee Sensors Journal ; 21(19):22047-22057, 2021.
Article in English | Web of Science | ID: covidwho-1467503


The highly infectious and serious nature of coronavirus disease 2019 (COVID-19) has highlighted the need for hospital space disinfection technology and the prevention of human exposure to pathogenic environments. This research developed novel chlorine dioxide (ClO2) sterilization technology to reduce bacteria and viruses in the air and on surfaces. A smart sterilization robot system was also developed to spray disinfectants in operating theaters or patients' rooms, designed according to the results of controlled experiments and the requirements for hospital disinfection. The system was built incorporated a semi-automatic remote-controlled module and an automatic intelligent disinfection function;that is, it could operate independently according to specific epidemic prevention strategies, which were implemented using a combination of Internet of Things (IoT) applications and a gesture recognition function. The elimination of Escherichia coli (E. coli) bacteria on sample plates was 99.8 % effective. This paper reviews the evolution of various disinfection technologies and describes a disinfection robot system in detail.

Proceedings of 2020 Ieee International Conference on Teaching, Assessment, and Learning for Engineering ; : 31-38, 2020.
Article in English | Web of Science | ID: covidwho-1312298


Many universities around the world arc now providing online courses on platforms of MOOC (Massive Open Online Course) related to the COVID-19 outbreak. Therefore, it is important for universities to quantify student-learning effectiveness based on MOOC. However, its operation faces many challenges to educational administrators and teachers. The most important thing is that the completion rate of learners is usually low, and therefore it is not comprehensive and objective to directly use online data to assess and predict the student-learning effectiveness. In order to assess multi-stage learning effectiveness of students in MOOC comprehensively, we proposed MOLEAS, a multi-stage online learning effectiveness assessment scheme. First, MOLEAS uses matrix completion to predict missing learning data of students. We take two open courses offered in the icourse MOOC platform as examples to analyze online data, and then we study the student-learning effectiveness using the matrix completion. The prediction results prove the effectiveness and reliability of our model. Then, combined with the prediction, MOLEAS utilizes the influencing factors of student-learning effectiveness, and a series of measures to improve the entire learning effectiveness of students in MOOC. What is more, we design a simulation practice platform which presents strong support to practice online teaching.