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相关概念视频

Two-Dimensional (2D) NMR: Overview01:12

Two-Dimensional (2D) NMR: Overview

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The 1D NMR spectrum of large and complex molecules like natural products has complicated splitting patterns and overlapping signals, which can be easily interpreted using 2-dimensional (2D) NMR. Unlike 1D NMR, 2D NMR has two frequency axes that provide the coupling information between the nucleus A and nucleus B in a molecule. The process from which 2D spectra are obtained has four steps.
The first step is the preparation period, during which nucleus A is excited with a radiofrequency pulse....
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¹H NMR: Interpreting Distorted and Overlapping Signals01:02

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Spin systems where the difference in chemical shifts of the coupled nuclei is greater than ten times J are called first-order spin systems. These nuclei are weakly coupled, and their chemical shifts and coupling constant can generally be estimated from the well-separated signals in the spectrum.
As Δν decreases and the signals move closer, the doublets appear increasingly distorted. The intensities of the inner lines increase at the cost of those of the outer lines as the signals are...
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A covalently bonded heteronuclear diatomic molecule can be modeled as two vibrating masses connected by a spring. The vibrational frequency of the bond can be expressed using an equation derived from Hooke's law, which describes how the force applied to stretch or compress a spring is proportional to the displacement of the spring. In this case, the atoms behave like masses, and the bond acts like a spring.
According to Hooke's law, the vibrational frequency is directly proportional to...
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1D梯度加权类激活映射,在光谱分析中可视化基于卷积神经网络模型的决策过程.

Guo-Yang Shi1,2, Hao-Ping Wu2, Si-Heng Luo2,3

  • 1Xiamen Key Lab. of Big Data Intelligent Analysis and Decision, School of Aerospace Engineering, Xiamen University, Xiamen, Fujian 361102, China.

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一个新的1D Grad-CAM算法增强了1D光谱的深度学习解释性. 这种方法准确地可视化了卷积神经网络 (CNN) 的决策,改善了光谱数据的定性和定量分析.

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科学领域:

  • 频谱学是一种光谱学.
  • 化学测量 化学测量 化学测量
  • 机器学习 机器学习

背景情况:

  • 深度学习模型在1D光谱学中提供了高精度,但由于其"黑子"性质,其解释性较低.
  • 现有的可视化方法,如CAM和Grad-CAM,是为2D数据设计的,不能准确地表示光谱数据的重要性.

研究的目的:

  • 开发一种新的可视化算法,1D Grad-CAM,用于1D光谱中的基于卷积神经网络 (CNN) 的模型.
  • 提高深度学习模型的可解释性,用于定性和定量光谱分析.

主要方法:

  • 通过修改经典的Grad-CAM开发了1D Grad-CAM,删除了梯度平均 (GAP) 和ReLU操作.
  • 引入了用于评估模型性能的"差异" (纯度/线性) 和"特征贡献"指标.
  • 使用拉曼光谱和ResNet.Net来分析植物油改的算法.

主要成果:

  • 1D Grad-CAM展示了梯度和光谱位置之间的更强的相关性,更全面地捕捉了光谱特征.
  • 该算法能够可靠地评估CNN模型的定性准确性和定量精度.
  • 对植物油分析ResNet的可视化证实了该方法在反映高准确度和精度方面的有效性.

结论:

  • 1D Grad-CAM为1D光谱数据的CNN决策过程提供了清晰的见解.
  • 开发的算法提高了光谱学中的深度学习模型的可解释性和可靠性.
  • 1D Grad-CAM可以在1D光谱领域更广泛地应用CNN.