ECAPA-TDNN based online discussion activity-level evaluation.

With the continuous development and application of online interactive activities and network transmission technology, online interactive behaviors such as online discussion meetings and online teaching have become indispensable in people's studies and work. However, the effectiveness of working with online discussions and feedback from participants on their conference performance has been a major concern, and this is the issue examined in this post. Based on the above issues, this paper designs an online discussion activity-level evaluation system based on voiceprint recognition technology. The application system developed in this project is divided into two parts; the first part is to segment the online discussion audio into multiple independent audio segments by audio segmentation technology and train the voiceprint recognition model to predict the speaker's identity in each separate audio component. In the second part, we propose a linear normalized online meeting activity-level calculation model based on the modified main indexes by traversing and counting each participant's speaking frequency and total speaking time as the main indexes for activity-level evaluation. To make the evaluation results more objective, reasonable, and distinguishable, the activity score of each participant is calculated, and each participant's activity-level in the discussion meeting is derived by combining the fuzzy membership function. To test the system's performance, we designed an experiment with 25 participants in an online discussion meeting, with two assistants manually recording the discussion and a host moderating the meeting. The results of the experiment showed that the system's evaluation results matched those recorded by the two assistants. The system can fulfill the task of distinguishing the level of activity of participants in online discussions.

UAV path planning based on third-party risk modeling.

Drones play an important role in modern cities, they bring convenience but also bring corresponding risks. Falling drones can cause casualties or damage to urban facilities and serious property damage, which increases the third-party risk problem. An effective way to reduce these third-party risks is to avoid high-risk areas in path planning, but most current path planning methods are optimized to minimize flight distance, and third-party risk costs are rarely considered. In this paper, a comprehensive risk-cost UAV path planning method is proposed, which evaluates urban risk by establishing a third-party risk model that incorporates obstacle risk, death risk, and property loss risk. This paper proposes a Min-cost A* algorithm based on city risk assessment, and smooths the generated low-risk path through the improved Floyd algorithm. The results show that the path planning method can effectively reduce the risk in the flight path, improve the reliability of the UAV flight path in the urban environment, and solve the problem of planning the third-party risk path of the UAV.