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IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations01:08

IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations

1.7K
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...
1.7K
Energy and Power Signals01:17

Energy and Power Signals

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In an electrical system with a resistor, voltage and current signals facilitate the measurement of power and energy across the resistor. For a continuous-time signal, the total energy over a time interval is defined as the integral of the square of the signal's magnitude over that interval. Mathematically, this is expressed as:
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Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

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The Synchronous Machine Model is a fundamental tool in analyzing and ensuring the transient stability of power systems. This model simplifies the representation of a synchronous machine under balanced three-phase positive-sequence conditions, assuming constant excitation and ignoring losses and saturation. The model is pivotal for understanding the behavior of synchronous generators connected to a power grid, particularly during transient events.
In this model, each generator is connected to a...
747
Power Factor Correction01:20

Power Factor Correction

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The power transmission to a factory involves the transfer of apparent power, a combination of active and reactive power. The power factor measures how effectively electrical power is converted into useful work output. The ratio of the real power (KW) that does the work to the apparent power (KVA) supplied to the circuit.
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¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)01:20

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)

1.6K
When proton-coupled carbon-13 spectra are simplified by a broadband proton decoupling technique, structural information about the coupled protons is lost. Distortionless enhancement by polarization transfer (DEPT) is a technique that provides information on the number of hydrogens attached to each carbon in a molecule. While the DEPT experiment utilizes complex pulse sequences, the pulse delay and flip angle are specifically manipulated. The resulting signals have different phases depending on...
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Multimachine Stability01:25

Multimachine Stability

545
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
545

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相关实验视频

Updated: Jan 16, 2026

Investigating the Potential of Singly Curved Thin Piezoelectric Transducers for Energy Harvesting and Structural Health Monitoring
07:02

Investigating the Potential of Singly Curved Thin Piezoelectric Transducers for Energy Harvesting and Structural Health Monitoring

Published on: November 14, 2025

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对混合动力电源的 entropy-Fused增强的交错几何模式分解质量干扰识别.

Chencheng He1, Wenbo Wang1, Xuezhuang E1

  • 1College of Science, Wuhan University of Science and Technology, Wuhan 430065, China.

Entropy (Basel, Switzerland)
|September 27, 2025
PubMed
概括

这项研究引入了一种用于检测电源质量干扰 (PQD) 的新框架,即使在噪音条件下也能达到高精度. 该方法结合了先进的分解和技术,用于强大的特征提取和分类.

关键词:
在PQD中,PQD是PQD.双层深度极端学习机器改进了简单的几何模式分解.精炼的通用化的多尺度量子.精炼的通用化的多尺度反向分散的.

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科学领域:

  • 电气工程 电气工程
  • 信号处理 信号处理
  • 机器学习 机器学习

背景情况:

  • 电力质量干扰 (PQD) 在电网中构成重大运营挑战.
  • 对PQD的准确评估在很大程度上依赖于对分类模型的有效特征选择.
  • 高维特征向量可以增加模型的复杂性并降低识别速度.

研究的目的:

  • 为准确和高效的电力质量干扰识别提出一个强大的特征提取框架.
  • 开发一个低维特征向量,以提高分类器的性能.
  • 提高电源质量干扰识别模型的准确性和稳定性.

主要方法:

  • 改进了对三频段信号分解的综合几何模式分解 (ISGMD).
  • 精细的泛化多尺度量子和反向分散的组合用于特征提取.
  • 一种用于双层复合分类模型的深度极端学习机器 (ELM) 算法.

主要成果:

  • 提出的方法在各种噪音环境中实现了97.3%的平均识别精度.
  • 在复杂的混合扰动下,准确度保持在96%以上.
  • 与CNN + LSTM相比,在识别准确度上有3.7%的改进,在小数据集上表现优越.

结论:

  • 开发的特征提取和分类策略准确地识别了功率质量干扰.
  • 与传统方法相比,该方法具有更高的分类准确性和稳定性.
  • 在模拟和测量数据上验证的有效性,显示高精度 (99.10%) 和耐噪.