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相关概念视频

Raman Spectroscopy: Overview01:20

Raman Spectroscopy: Overview

443
The underlying principle of Raman spectroscopy is based on the interaction between light and matter, specifically molecules' inelastic scattering of photons. When a monochromatic beam of light, typically from a laser source, interacts with a sample, most scattered light has the same frequency as the incident light. This is known as Rayleigh scattering.
However, a small fraction of the scattered light exhibits a frequency shift due to the exchange of energy between the incident photons and...
443
Raman Spectroscopy Instrumentation: Overview01:26

Raman Spectroscopy Instrumentation: Overview

450
A conventional Raman spectrophotometer includes a laser source, a sample holding system, a wavelength selector, and a detector.
The monochromatic laser source, typically using visible or near-infrared radiation, generates a highly focused beam of light. This light interacts with the molecules of the sample, scattering some of the light. Liquid and gaseous samples are usually tested in ordinary glass capillaries, while solids can be analyzed as powders packed in capillaries or as potassium...
450
IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations01:08

IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations

1.1K
Identical bonds within a polyatomic group can stretch symmetrically (in-phase) or asymmetrically (out-of-phase). Similar to hydrogen bonding, these vibrations also influence the shape of the IR peak. Generally, asymmetric stretching frequencies are higher than symmetric stretching frequencies. For example, primary amines exhibit two distinct IR peaks between 3300–3500 cm−1 corresponding to the symmetric and asymmetric N-H stretching, while secondary amines exhibit a single...
1.1K
State Space to Transfer Function01:21

State Space to Transfer Function

229
The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
229
¹H NMR: Interpreting Distorted and Overlapping Signals01:02

¹H NMR: Interpreting Distorted and Overlapping Signals

1.1K
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...
1.1K
Transfer Function to State Space01:23

Transfer Function to State Space

294
State-space representation is a powerful tool for simulating physical systems on digital computers, necessitating the conversion of the transfer function into state-space form. Consider an nth-order linear differential equation with constant coefficients, like those encountered in an RLC circuit. The state variables are selected as the output and its n−1 derivatives. Differentiating these variables and substituting them back into the original equation produces the state equations.
In an...
294

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相关实验视频

Updated: Jul 16, 2025

Ultrafast Time-resolved Near-IR Stimulated Raman Measurements of Functional &#960;-conjugate Systems
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Ultrafast Time-resolved Near-IR Stimulated Raman Measurements of Functional π-conjugate Systems

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基于循环-GAN的拉曼频谱模型转移方法.

Zilong Wang1, Zhe Yang2, Xiangning Song2

  • 1College of Optical and Electronic Technology, China Jiliang University, Hangzhou 310018, China.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
|September 18, 2023
PubMed
概括
此摘要是机器生成的。

拉曼光谱仪的硬件变化导致数据差异. 使用Cycle-GAN的新深度学习方法在仪器之间转换光谱数据,改进模型传输并实现99%以上的等号相似性.

关键词:
化学测量 化学测量 化学测量深度学习是一种深度学习.映射关系关系的映射关系模型转移 模型转移拉曼光谱法 拉曼光谱法

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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:

  • 拉曼光谱仪的硬件变化导致不一致的光谱数据.
  • 在一个乐器上训练的模型通常在其他乐器上表现不佳.
  • 传统的化学测量方法在精确的光谱数据转换方面存在困难.

研究的目的:

  • 开发一种新的深度学习方法,用于在不同仪器之间转换拉曼光谱数据.
  • 克服传统模型转移技术的局限性.
  • 为了实现光谱数据的直接,无参数转换.

主要方法:

  • 基于循环一致对抗网络 (Cycle-GAN) 的深度学习网络被调整为向量到向量的转换.
  • 网络直接将光谱数据从源域转换为目标域.
  • 转换不需要进行参数调整或额外操作.

主要成果:

  • 开发的深度学习网络成功地在不同领域之间转换了光谱数据.
  • 与传统的化学测量方法相比,该方法显示出更高的智能和效率.
  • 源域数据和转换的目标域数据之间的代数相似性超过了99%.

结论:

  • 基于Cycle-GAN的深度学习网络为拉曼光谱数据传输提供了有效的解决方案.
  • 这种方法提高了模型在不同拉曼光谱仪器仪表中的适用性.
  • 该方法代表了智能和高效的光谱数据处理的重大进步.