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相关概念视频

¹³C NMR: ¹H–¹³C Decoupling01:04

¹³C NMR: ¹H–¹³C Decoupling

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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
¹H NMR Signal Multiplicity: Splitting Patterns01:13

¹H NMR Signal Multiplicity: Splitting Patterns

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When protons A and X are coupled, their nuclear spin energy levels are slightly modified. This is because the energy required to excite proton A to a spin state parallel to proton X is slightly different from the energy required for it to become anti-parallel to spin X. Consequently, there are two possible excitation frequencies for A (A1 and A2), depending on the spin state of X, and vice versa. The mutual nature of coupling implies that the difference between frequencies A1 and A2, indicated...
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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
Aliasing01:18

Aliasing

227
Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
227
¹H NMR Signal Integration: Overview00:58

¹H NMR Signal Integration: Overview

1.7K
The intensity of a signal, which can be represented by the area under the peak, depends on the number of protons contributing to that signal. The area under each peak is shown as a vertical line called an integral, with the integral value listed under it, as seen in the proton NMR spectrum of benzyl acetate. Each integral value is divided by the smallest integral value to obtain the ratio of the number of protons producing each signal. The ratio reveals the relative number of protons and not...
1.7K
Interpreting ¹H NMR Signal Splitting: The (n + 1) Rule01:10

Interpreting ¹H NMR Signal Splitting: The (n + 1) Rule

1.4K
In the AX proton spin system, proton A can sense the two spin states of a coupled proton X, resulting in a doublet NMR signal with two peaks of equal (1:1) intensity. When proton A is coupled to two equivalent protons (AX2 spin system), the spin states of each X can be aligned with or against the external field, creating three possible scenarios. This results in a 1:2:1  triplet signal, where the central peak corresponds to the chemical shift of A and is twice as large or intense as the...
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Updated: Sep 11, 2025

Characterizing Individual Protein Aggregates by Infrared Nanospectroscopy and Atomic Force Microscopy
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为NILM捕获高频波符号:构建负载分类数据集

Farid Dinar1, Sébastien Paris1, Éric Busvelle1,2

  • 1Laboratoire d'Informatique et des Systèmes (LIS), Unité Mixte de Recherche, Centre National de la Recherche Scientifique (UMR, CNRS) 7020, Université de Toulon, Aix Marseille Université, 83130 La Garde, France.

Sensors (Basel, Switzerland)
|August 14, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了一种新的,具有成本效益的高频能源监控系统,以改进非侵入性负载监控 (NILM). 使用高频数据的机器学习显著提高了设备分类的准确性.

关键词:
基于高频信号的电气测量.嵌入式系统 嵌入式系统能源分类 能源分类的分析和的分析机器学习是机器学习.非侵入性的负载监控 (NILM)智能能源系统 智能能源系统

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Last Updated: Sep 11, 2025

Characterizing Individual Protein Aggregates by Infrared Nanospectroscopy and Atomic Force Microscopy
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科学领域:

  • 电气工程 电气工程
  • 计算机科学 计算机科学
  • 数据科学数据科学数据科学

背景情况:

  • 传统的非侵入性负载监控 (NILM) 难以处理低频数据,限制了设备分类的准确性.
  • 高频电数据提供了更丰富的签名,包括律顺序,以提高NILM的性能.
  • 缺乏全面的,可访问的公共数据集阻碍了先进的NILM研究.

研究的目的:

  • 开发一个可扩展,具有成本效益的能源监测系统,用于生成详细的NILM数据集.
  • 创建一个可复制和可访问的总和单个设备测量的数据集.
  • 通过机器学习验证高频特征在提高NILM分解精度方面的有效性.

主要方法:

  • 设计并实施了一种用于高频数据采集的新能源监测系统.
  • 收集总和单个电器电气数据,形成一个新的NILM数据集.
  • 应用机器学习技术来分析高频电气特征以进行设备分类.

主要成果:

  • 开发的系统提供了详细的高频电气测量.
  • 新的数据集使得可重复的NILM验证和研究成为可能.
  • 使用高频特征的机器学习模型显示,与传统方法相比,分类精度更高.

结论:

  • 高频数据采集系统对于推进NILM研究至关重要.
  • 开发的数据集和方法解决了能源分类中的一个关键差距.
  • 这项工作为改进实时能源分类和智能能源管理应用铺平了道路.