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基于模糊聚类分析的幼儿图形感知教育分析。

Analysis of Graphic Perception Education for Young Children Based on Fuzzy Clustering Analysis.

机构信息

Jiangsu Vocational College of Electronics and Information, Huaian, Jiangsu 223003, China.

Department of Family Welfare, Sangmyung University, Jongno-gu, Seoul 03016, Republic of Korea.

出版信息

Comput Intell Neurosci. 2022 Jun 21;2022:8046713. doi: 10.1155/2022/8046713. eCollection 2022.

DOI:10.1155/2022/8046713
PMID:35774433
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9239772/
Abstract

Geometric ability includes elements of identification, conceptualization, combination, drawing, and reasoning, and graphic perception is an important part of it. Kindergarten science education includes geometry instruction. Children are guided to perceive the relationship between shapes and figures through direct perception, first-hand experience, and practical operation through concentrated educational activities, and form image-concrete thinking over time, enhancing their perception and experience of the relationship between shapes in the objective world, and accumulating certain mathematical perceptual experience. Clustering is a branch of unsupervised pattern recognition that is very useful. Fuzzy clustering, which establishes the uncertainty description of samples to categories and can objectively reflect the real world, has become the mainstream of cluster analysis research. The graphics perception education of children is investigated using fuzzy clustering analysis. The main topic of this paper is how to apply children's graphics to the design of children's educational institutions and open up new creative perspectives for the design of children's educational institutions. The method of graphic perception education: perception education for preschool children is proposed based on the multichannel characteristics of preschool children's aesthetic perception and with reference to the theory of perception. The experimental results show that the improved algorithm reduces segmentation time by 171.48 s when compared to the traditional FCM algorithm for both noisy and high-quality images and that it is significantly faster than the FCM algorithm in terms of segmentation speed. As a result, the model construction of a set of children's graphic perception education for the cognitive characteristics of the age group children can provide corresponding references and references for related topic research.

摘要

几何能力包括识别、概念化、组合、绘图和推理等元素,图形感知是其重要组成部分。幼儿园科学教育包括几何教学。通过集中的教育活动,引导儿童通过直接感知、第一手经验和实际操作来感知形状和图形之间的关系,随着时间的推移形成形象-具体思维,增强他们对客观世界中形状关系的感知和体验,积累一定的数学感性经验。聚类是无监督模式识别的一个分支,非常有用。模糊聚类建立了样本到类别之间的不确定性描述,可以客观地反映现实世界,已成为聚类分析研究的主流。使用模糊聚类分析对儿童的图形感知教育进行了研究。本文的主要主题是如何将儿童的图形应用于儿童教育机构的设计,为儿童教育机构的设计开辟新的创造性视角。图形感知教育的方法:提出了基于学龄前儿童审美感知多通道特征并参考感知理论的学龄前儿童感知教育。实验结果表明,与传统 FCM 算法相比,该改进算法在处理噪声和高质量图像时,分割时间分别减少了 171.48s 和 172.53s,在分割速度方面明显优于 FCM 算法。因此,为该年龄段儿童认知特点构建的一套儿童图形感知教育模型可以为相关主题研究提供相应的参考和借鉴。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/06dd/9239772/7cc7b040c1e9/CIN2022-8046713.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/06dd/9239772/8ae507536d8f/CIN2022-8046713.001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/06dd/9239772/b09707b2bde1/CIN2022-8046713.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/06dd/9239772/7cc7b040c1e9/CIN2022-8046713.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/06dd/9239772/8ae507536d8f/CIN2022-8046713.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/06dd/9239772/1413a63a0217/CIN2022-8046713.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/06dd/9239772/ece700140b19/CIN2022-8046713.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/06dd/9239772/9a05a0dafbd1/CIN2022-8046713.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/06dd/9239772/eb06cc018e5a/CIN2022-8046713.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/06dd/9239772/ccd16649a0f7/CIN2022-8046713.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/06dd/9239772/b09707b2bde1/CIN2022-8046713.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/06dd/9239772/7cc7b040c1e9/CIN2022-8046713.008.jpg

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