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基于网格模型简化的在线智能课程教育。

Online Intelligent Course Education Based on Grid Model Simplification.

机构信息

Anhui Jianzhu University, Hefei, Anhui, China.

Anhui University of Finance and Economics, Bengbu, Anhui, China.

出版信息

Comput Intell Neurosci. 2022 Jul 15;2022:6009917. doi: 10.1155/2022/6009917. eCollection 2022.

Abstract

In order to explore the teaching efficiency of online intelligent courses and improve the quality of online teaching, this paper builds a classroom intelligent auxiliary management model based on the grid simplification method. Moreover, this paper formulates corresponding teaching strategies through the recognition of student state features, uses a target detector to detect all detection targets from the scene, and then counts the number of detection targets, identifies specific individuals, and judges the individual state. Simultaneously, this paper intercepts candidate subregions from the scene image and then inputs the subregion image to the detector to determine whether the candidate region image is a detection target and formulate corresponding countermeasures. In addition, on the basis of the existing 3D mesh model stitching and editing method, this paper proposes a grid splicing and fusion method based on the idea of partial reuse of the model to calculate the result of the target. Finally, this paper designs experiments to verify the performance of the model. The research results show that the model constructed in this paper is effective.

摘要

为了探索在线智能课程的教学效率,提高在线教学质量,本文构建了一种基于网格简化方法的课堂智能辅助管理模型。此外,本文通过识别学生状态特征制定了相应的教学策略,使用目标检测器从场景中检测到所有检测目标,然后计算检测目标的数量,识别特定个体,并判断个体状态。同时,本文从场景图像中截取候选子区域,然后将子区域图像输入到检测器中,以确定候选区域图像是否为检测目标,并制定相应的对策。此外,在现有的 3D 网格模型拼接和编辑方法的基础上,本文提出了一种基于模型部分重用思想的网格拼接和融合方法来计算目标的结果。最后,本文通过设计实验验证了模型的性能。研究结果表明,本文构建的模型是有效的。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/263a/9307335/d0f728896984/CIN2022-6009917.001.jpg

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