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Digital Visual Sensing Design Teaching Using Digital Twins

Author(s): ORCID

Medium: journal article
Language(s): English
Published in: Advances in Civil Engineering, , v. 2022
Page(s): 1-11
DOI: 10.1155/2022/9311246
Abstract:

Digital twin technology can support teachers in this major to complete monitoring related topics and application research and serve teaching and scientific research through the establishment of automatic monitoring teaching model laboratory and in-depth combination with the current students’ skill training needs in this major. This exploration aims to make the visual sensor industry have a steady stream of talents. Promoting the development of visual sensor technology is to promote the development of science and technology. Based on the teaching research of visual sensing design using digital technology, a set of teaching systems of visual sensing design is designed by using the methods of literature research and investigation and analysis. Special topics are set up from the aspects of professional subjects; regarding course content, various sensor principle research courses, visual sensor design courses, experimental courses, and example courses are set up; teaching methods are divided into online and offline synchronous classes; the evaluation method should focus on the distribution, examples, and practice. The results show that traditional classroom teaching is seriously separated from extracurricular learning. Most students are in a state of passive acceptance of knowledge and have few thinking activities. The established teaching system integrates brain and cognition, photoelectric foundation, sensory imaging, visual sensing imaging, visual sensing technology courses, computer technology, virtual experiment courses, and physical experiment courses. It can carry out more than 30 experiments of optical microscopic imaging and X-ray imaging based on the principle of visual sensing. Therefore, the teaching effect and teaching mode of the proposed digital visual sensing teaching system have been greatly improved.

Copyright: © 2022 Lu Lian and Yao Yan et al.
License:

This creative work has been published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license which allows copying, and redistribution as well as adaptation of the original work provided appropriate credit is given to the original author and the conditions of the license are met.

  • About this
    data sheet
  • Reference-ID
    10657348
  • Published on:
    17/02/2022
  • Last updated on:
    01/06/2022
 
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