Digital Visual Sensing Design Teaching Using Digital Twins

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.

Using digital twins in education from an innovative perspective: Potential and application areas

“…On the contrary, scholars and expert commentaries furthered the debates on digital classrooms, distance learning, and humancomputer interaction in education. However, a few ideas for the future use of digital twins in education emerged, including integrating artificial intelligence (AI) with digital twins to create more personalized learning environments (Liam & Yan, 2022;Ahuja et al, 2021). Some scholars perceive digital twins and AI as tools to help teachers in their practice (Liam & Yan, 2022).…”

“…However, a few ideas for the future use of digital twins in education emerged, including integrating artificial intelligence (AI) with digital twins to create more personalized learning environments (Liam & Yan, 2022;Ahuja et al, 2021). Some scholars perceive digital twins and AI as tools to help teachers in their practice (Liam & Yan, 2022). The teaching aids proposed in these articles include digital sensors and teaching experiments designed to give learners a deeper understanding of core concepts.…”

“…The role of AI in such teaching models includes enhancing the operability of the systems due to their complexity. For instance, such teaching models consider the characteristics of different students, meaning massive amounts of student data require analysis and course modules tailored to each student (Liam & Yan, 2022). Without AI, accomplishing such a feat becomes increasingly challenging.…”

“…It is important to emphasize the concept of personalized learning. As mentioned earlier, scholars believe that the use of AI in digital twins of educational systems makes it possible for educators to capture and cater to the needs of individual students (Liam & Yan, 2022). Other scholars emphasize that digital twins and AI are the pillars of personalized learning (Furini et al, 2022).…”

Digital Visual Design Reengineering and Application Based on K-means Clustering Algorithm

“…Digital visual design reengineering graphic visual gene extraction methods are divided into color system extraction [8], graphic line body extraction [9], texture feature extraction [10] and other methods from the perspective of the type of gene extraction. Literature [11] proposes a color system extraction method based on K-means clustering algorithm, and constructs a color database of aesthetic artifacts; literature [12] adopts an image processing method, extracts the mural color system from the color three-channel, and puts forward a gene extraction method based on machine learning algorithms for the digital visual design recreation; literature [13] studies the color particles of the mural paintings, and through the particle swarm optimization algorithm Improved K-means clustering analysis of color features; Literature [14] uses deep learning methods to extract and learn graphic shapes, and constructs a digital reengineering model of multi-dimensional linear structure shapes; Literature [15] improves the K-means clustering method to digitally extract texture representations by using the peak density strategy, and at the same time, uses a self-coder neural network to construct the artifacts graphic texture expression model ; Literature [16] combines the gray wolf optimization algorithm, K-means algorithm and convolutional neural network method table mural texture aesthetics for feature extraction and reengineering representation; Literature [17] combines the color characteristics of aesthetic artifacts and line characteristics, to build a digital visual design reengineering evaluation system. For the analysis of the above literature, the existing graphic visual gene extraction methods have the following defects [18]: 1) digital visual design reengineering feature selection is not standard enough [19]; 2) gene extraction methods lack of generalization [20]; 3) gene extraction methods are not efficient enough.…”