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相关概念视频

IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

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IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
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A Silicon-tipped Fiber-optic Sensing Platform with High Resolution and Fast Response
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使用可见光和机器学习的智能光纤宽度检测.

Naikui Ren, Longxiang Wang, Nan Huo

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    此摘要是机器生成的。

    这项研究介绍了一种使用光纤传感和机器学习的智能宽度测量系统. 开发的组合模型准确地检测宽度变化,显示出对智能制造应用的巨大希望.

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    科学领域:

    • 工程 工程师 工程师 工程师
    • 计算机科学 计算机科学
    • 材料科学 材料科学 材料科学

    背景情况:

    • 准确的宽度测量对于制造中的质量控制至关重要.
    • 传统的方法可能耗时且缺乏精度.
    • 开发自动化,智能化测量系统是研究的一个关键领域.

    研究的目的:

    • 开发一个智能系统,用于精确的宽度测量.
    • 为此目的利用光纤传感,可见光检测和机器学习.
    • 为了评估整体机器学习模型对宽度变化检测的性能.

    主要方法:

    • 组合机器学习模型结合了随机森林回归,k-最近邻居回归和决策树回归.
    • 输入特征是通过光纤传输的光点图像的统计分析 (第一阶统计,GLCM,DLS) 来得出的.
    • 使用准蒙特卡洛和树结构的Parzen估计算法进行了特征维度减少和超参数优化.

    主要成果:

    • 最佳组合模型在五次交叉验证后实现了0.2084mm2的平均平方误差和0.9997的确定系数.
    • 特性重要性分析指导了模型输入特征的减少.
    • 开发的系统在检测宽度变化方面表现出高准确性和可靠性.

    结论:

    • 拟议的智能宽度测量系统非常准确和可靠.
    • 整体机器学习方法有效地检测使用光纤传感的宽度变化.
    • 这种方法在推进智能制造工艺方面具有重大潜力.