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在基于物联网技术的改进卷积神经网络下对艺术设计的分析
1Shandong Institute of Petroleum and Chemical Technology, Dongying, 257000, China. liubo15153141697@163.com.
Scientific reports
|September 10, 2024
概括
本研究介绍了一种改进的卷积神经网络 (CNN),与物联网 (IoT) 集成,用于艺术教育. 这种人工智能驱动的方法可以增强学生在艺术设计教学中的反和创造性表达.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 教育技术的教育技术
背景情况:
- 物联网 (IoT) 技术为艺术设计教育提供了新的途径.
- 深度学习和卷积神经网络 (CNN) 为创意领域的图像分析提供了先进的工具.
- 现有的方法可能缺乏全面艺术教育所需的综合方法.
研究的目的:
- 探索改进的CNN与物联网技术相结合在艺术设计教育中的应用.
- 提高艺术设计教学的有效性,促进学生的创造性表达.
- 开发一个系统,为艺术学生提供准确和及时的反.
主要方法:
- 一个改进的CNN模型被构建,增加了卷积层,神经元,批量正常化和脱落层.
- 使用物联网技术建立了一个实验环境,以捕获艺术图像样本和环境数据.
- 图像样本和传感器数据进行了预处理,使用CNN提取特征,并连接起来进行全面的艺术品分析.
主要成果:
- 改进的CNN模型有效地获取了艺术样本数据和学生的创造性表达数据.
- 综合系统为艺术设计教育提供了准确和及时的反和指导.
- 该模型在提高艺术教育和教学方法方面展示了有前途的应用.
结论:
- 改进的CNN和物联网技术的结合为艺术设计教育提供了一种新且有效的方法.
- 这种方法提供了有价值的见解和工具,以促进艺术教育和培养学生的创造力.
- 开发的系统在艺术教育环境中具有实践应用的巨大潜力.
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