Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Dielectric Polarization in a Capacitor01:31

Dielectric Polarization in a Capacitor

4.7K
The presence of a dielectric medium in a capacitor not only changes the voltage and capacitance but also affects the electric field. In general, dielectrics can be of two types: polar and nonpolar. In a polar dielectric, the positive and negative charges in the molecules are separated by a distance and hence have a permanent dipole moment. In contrast, no such charge separation exists in a nonpolar dielectric, however the nonpolar molecules get polarized in the presence of an external electric...
4.7K
Electrostatic Boundary Conditions in Dielectrics01:27

Electrostatic Boundary Conditions in Dielectrics

1.2K
When an electric field passes from one homogeneous medium to another, crossing the boundary between the two mediums imparts a discontinuity in the electric field. This results in electrostatic boundary conditions that depend on the type of mediums the field propagates through.
Consider a case where both the mediums across a boundary are two different dielectric materials. Recall that the electric field and electric displacement are proportional and related through the material's...
1.2K
Classification of Signals01:30

Classification of Signals

461
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
461
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

192
The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
192
Induced Electric Fields: Applications01:27

Induced Electric Fields: Applications

1.6K
An important distinction exists between the electric field induced by a changing magnetic field and the electrostatic field produced by a fixed charge distribution. Specifically, the induced electric field is nonconservative because it does not work in moving a charge over a closed path. In contrast, the electrostatic field is conservative and does no net work over a closed path. Hence, electric potential can be associated with the electrostatic field but not the induced field. The following...
1.6K
Classification of Systems-I01:26

Classification of Systems-I

186
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
186

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Diffusion-augmented YOLO26-Swin cascaded framework with hybrid SHAP-CAM for autonomous power grid inspection.

Autonomous intelligent systems·2026
Same author

Deep Learning Approach for Automatic Heartbeat Classification.

Sensors (Basel, Switzerland)·2025
Same author

Audio-Based Engine Fault Diagnosis with Wavelet, Markov Blanket, ROCKET, and Optimized Machine Learning Classifiers.

Sensors (Basel, Switzerland)·2024
Same author

Decoding Electroencephalography Signal Response by Stacking Ensemble Learning and Adaptive Differential Evolution.

Sensors (Basel, Switzerland)·2023
Same author

Video-Based Human Activity Recognition Using Deep Learning Approaches.

Sensors (Basel, Switzerland)·2023
Same author

Group Method of Data Handling Using Christiano-Fitzgerald Random Walk Filter for Insulator Fault Prediction.

Sensors (Basel, Switzerland)·2023

相关实验视频

Updated: Jul 2, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.6K

随机卷积内核转换与实证模式分解用于电网绝缘体的分类.

Anne Carolina Rodrigues Klaar1, Laio Oriel Seman2, Viviana Cocco Mariani3,4

  • 1Graduate Program in Education, University of Planalto Catarinense, Lages 88509-900, Brazil.

Sensors (Basel, Switzerland)
|February 24, 2024
PubMed
概括

这项研究通过将实证模式分解 (EMD) 方法与MiniRocket算法相结合,增强了绝缘体故障诊断. 这种方法显著提高了检测故障电绝缘体的准确性,提高了电力系统的可靠性.

关键词:
电力系统的电力系统.经验模式分解分解火箭算法火箭算法是一种算法.时间序列分类时间序列分类

更多相关视频

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.1K
A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy
08:23

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

Published on: November 13, 2016

11.2K

相关实验视频

Last Updated: Jul 2, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.6K
Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.1K
A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy
08:23

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

Published on: November 13, 2016

11.2K

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:

  • 可靠的电能供应取决于功能绝缘体.
  • 来自绝缘体的超声波时间序列数据可以识别故障.
  • 现有的方法需要改进,以准确地分类绝缘体故障.

研究的目的:

  • 提出一种用于绝缘体故障诊断的新策略.
  • 为了评估结合实证模式分解 (EMD) 方法与火箭算法的有效性.
  • 为了提高电绝缘体故障检测的准确性和可靠性.

主要方法:

  • 利用随机卷积内核转换 (火箭) 算法从时间序列超声数据中提取特征.
  • 整合了三种EMD方法:用适应噪声 (CEEMDAN) 完成集体实证模式分解,实证波形转换 (EWT) 和变化模式分解 (VMD).
  • 应用后勤回归分类器来使用提取的特征诊断绝缘体故障.

主要成果:

  • 结合EMD方法和MiniRocket,显著提高了后勤回归的准确性.
  • 实现了高诊断准确度:CEEMDAN的0.992,EWT的0.995和VDM的0.980.
  • 与没有EMD的基线方法相比,证明了更高的性能.

结论:

  • 经验模式分解方法,当与MiniRocket集成时,为绝缘体故障诊断提供了一种强大的方法.
  • 拟议的战略提高了电力系统监控的准确性和可靠性.
  • 这项研究突出了提高电气基础设施安全性和可靠性的潜力.