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相关概念视频

UV–Vis Spectrometers01:14

UV–Vis Spectrometers

1.5K
The absorbance of UV and visible (UV–visible) radiations is measured using a UV–visible spectrophotometer. Deuterium lamps, which emit UV radiation, and tungsten lamps, which produce radiation in the visible region, are used as light sources in UV–visible spectrophotometers. A monochromator or prism is used for diffraction grating, i.e., to split the incoming radiation into different wavelengths. A system of slits is used to focus the desired wavelength on the sample cell.
1.5K
IR Spectrum01:19

IR Spectrum

1.3K
When infrared (IR) radiation passes through a molecule, the bonds stretch or bend by absorbing the radiation. This absorption creates the molecule's absorption spectrum, which is the plot of its percentage transmittance versus wavenumber.
Transmittance is defined as the ratio of the radiant power passing through a sample to that from the radiation's source. Multiplying the transmittance by 100 gives the percent transmittance (%T), which varies between 100% (no absorption) and 0%...
1.3K
UV–Vis Spectrum01:30

UV–Vis Spectrum

1.3K
When light passes through a substance, a portion of the light is absorbed while the remaining light is reflected or transmitted. If the molecule absorbs light between the wavelengths of 180–400 nm range, the UV spectrum is obtained, and if it absorbs light in the 400–780 nm wavelength range, the visible spectrum is obtained.     
The UV–Vis spectrum of a molecule is the plot of its absorbance versus wavelength. The plot is drawn by taking molar...
1.3K
Molecular Spectroscopy: Absorption and Emission01:14

Molecular Spectroscopy: Absorption and Emission

3.4K
Molecules possess discrete energy levels called quantum states. Unlike atoms, which have simpler energy levels, molecules possess additional rotational and vibrational energy levels.  Each energy level is separated by an energy gap, with the gaps between adjacent electronic, vibrational, and rotational levels varying significantly. The three types of energy levels in a diatomic molecule are shown in Figure 1.
3.4K
¹³C NMR: ¹H–¹³C Decoupling01:04

¹³C NMR: ¹H–¹³C Decoupling

1.2K
The probability of having two carbon-13 atoms next to each other is negligible because of the low natural abundance of carbon-13. Consequently, peak splitting due to carbon-carbon spin-spin coupling is not observed in spectra. However, protons up to three sigma bonds away split the carbon signal according to the n+1 rule, resulting in complicated spectra.
A broadband decoupling technique is used to simplify these complex, sometimes overlapping, signals. Broadband decoupling relies on a...
1.2K
UV–Vis Spectroscopy of Conjugated Systems01:32

UV–Vis Spectroscopy of Conjugated Systems

7.3K
Organic compounds with conjugated double bonds show strong absorption features in the UV–visible region of the electromagnetic spectrum attributed to π → π* electronic excitations. Generally, a UV–vis absorption spectrum is recorded as a plot of absorbance vs wavelength. The wavelength of maximum absorbance, which manifests as a peak in the absorption spectrum, is denoted as λmax.
One of the factors influencing λmax is the extent...
7.3K

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Updated: Sep 11, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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基于端到端深度学习网络的宽带吸收频谱处理方法.

Haoyong Li, Hai Zhong, Dahua Gao

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    概括
    此摘要是机器生成的。

    这项研究引入了一种快速的神经网络方法来分析吸收光谱,显著减少处理时间. 这种新的方法增强了发动机温度测量和材料设计中的实时应用.

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    Deep Neural Networks for Image-Based Dietary Assessment
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    科学领域:

    • 频谱学是一种光谱学.
    • 人工智能的人工智能
    • 化学工程是化学工程的重要组成部分.

    背景情况:

    • 吸收频谱分析对于诸如发动机温度测量和材料设计等应用至关重要.
    • 目前处理吸收光谱的方法缓慢而复杂,阻碍了实时应用.
    • 现有的技术涉及多个步骤,包括过,无效化,基线校正和数据库比较.

    研究的目的:

    • 开发一种快速而准确的方法来处理吸收光谱.
    • 克服传统,耗时的光谱分析技术的局限性.
    • 为了实现吸收频谱数据的实时在线处理.

    主要方法:

    • 设计了一个新的神经网络架构,结合了长期短期记忆 (LSTM) 和完全卷积网络 (FCN).
    • 神经网络直接处理未处理的吸收光谱,而不需要预处理步骤.
    • 该方法在H2O和CO2吸收光谱的温度处理中进行了测试.

    主要成果:

    • 拟议的神经网络方法实现了H2O和CO2温度处理的98.4%准确率.
    • 每个吸收频谱的平均处理时间为0.00036秒,代表了显著的速度改进.
    • 该方法证明了吸收光谱的高精度和前所未有的处理速度.

    结论:

    • 开发的神经网络方法为吸收频谱分析提供了非常准确和极快的解决方案.
    • 这种方法在各种科学和工程领域具有实时在线应用的巨大潜力.
    • 结合LSTM和FCN网络,有效地解决了在光谱特征提取和处理精度方面的挑战.