Central China Normal University

Zengzhao Chen

Zhicheng Dai

Xiuling He

Zongkai Yang

Sannyuya Liu

Sannvya Liu

Shengming Wang

Baolin Yi

The present invention discloses construction method and system of a descriptive model of classroom teaching behavior events. The construction method includes steps as the followings: acquiring classroom teaching video data to be trained; dividing the classroom teaching video data to be trained into multiple events according to utterances of a teacher by using a voice activity detection technology; and performing multi-modal recognition on all events by using multiple artificial intelligence technologies to divide the events into sub-events in multiple dimensions, establishing an event descriptive model according to the sub-events, and describing various teaching behavior events of the teacher in a classroom. The present invention divides a classroom video according to voice, which can ensure the completeness of the teacher's non-verbal behavior in each event to the greatest extent. Also, a descriptive model that uniformly describes all events is established by extracting commonality between different events, which can not only complete the description of various teaching behaviors of the teacher, but also reflect the correlation between events, so that the events are no longer isolated.

Construction method and system of descriptive model of classroom teaching behavior events

The disclosure discloses a fingerprint positioning method in a smart classroom, which is specifically: firstly, performing Gaussian filtering and taking the average value on a wireless signal strength value RSSI in the fingerprint database; then finding the neighbor point closest to the signal strength of the to-be-measured point; finally, the Euclidean distance is used as the weight reference, and the weighted center of mass is obtained for the nearest neighbor points. The weight index is introduced as an index of the weight, and the coordinates of the to-be-tested node are obtained. The disclosure has a higher positioning accuracy, smaller positioning error fluctuations and greater environmental adaptability.

Fingerprint positioning method and system in smart classroom

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Hubei, China

Zengzhao Chen