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

Multimachine Stability01:25

Multimachine Stability

529
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:
529
Neural Circuits01:25

Neural Circuits

2.6K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
2.6K
Network Function of a Circuit01:25

Network Function of a Circuit

589
Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
589
State Space Representation01:27

State Space Representation

496
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
496
Graphs of Equations in Two Variables01:30

Graphs of Equations in Two Variables

155
An equation with two variables, typically written in the form y = f(x) or Ax + By = C, describes a relationship between quantities represented by x and y. Each solution to such an equation is an ordered pair (x, y) that satisfies the equation when substituted. These pairs can be represented graphically to understand the variables' relationship visually.A common technique for constructing the graph of a two-variable equation is to create a value table. Begin by choosing several values for the...
155
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

466
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
466

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

A comprehensive review on master stability functions in complex network dynamics.

Reports on progress in physics. Physical Society (Great Britain)·2026
Same author

Discrete-Time Quantum Walk on Multilayer Networks.

Entropy (Basel, Switzerland)·2023
Same author

A network-based approach to identifying correlations between phylogeny, morphological traits and occurrence of fish species in US river basins.

PloS one·2023
Same author

Localization of multilayer networks by optimized single-layer rewiring.

Physical review. E·2018
Same author

Optimized evolution of networks for principal eigenvector localization.

Physical review. E·2017
Same author

Corrigendum: Understanding cancer complexome using networks, spectral graph theory and multilayer framework.

Scientific reports·2017

相关实验视频

Updated: Jan 8, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.6K

用图形神经网络预测复杂网络中的稳定状态行为.

Priodyuti Pradhan1, Amit Reza2,3

  • 1Department of Computer Science and Engineering, Indian Institute of Information Technology Raichur, Karnataka 584135, India.

Physical review. E
|December 23, 2025
PubMed
概括

这项研究使用图形神经网络在复杂系统中准确识别信息传播状态. 开发的模型有效地区分了扩散,弱局部化和强局部化状态,使用真实世界的数据.

科学领域:

  • 复杂系统科学 复杂系统科学
  • 网络科学 网络科学
  • 机器学习 机器学习

背景情况:

  • 复杂系统中的信息传播可以分为扩散,弱局部化和强局部化状态.
  • 了解这些传播动态对于分析系统行为至关重要.

研究的目的:

  • 应用图形神经网络 (GNN) 模型来学习和识别网络上的线性动态系统中的信息传播状态.
  • 开发一个GNN框架,能够准确地区分不同的本地化状态.

主要方法:

  • 开发一个图形卷积和基于注意力的神经网络框架.
  • 在网络上运行的线性动态系统上训练GNN模型.
  • 使用模拟和真实世界的网络数据评估模型的性能.
  • 为了解释性,对框架的前向和后向传播的分析推导.

主要成果:

  • 经过训练的GNN模型在区分扩散,局部化弱和局部化强的信息传播状态方面取得了很高的准确性.
  • 在对真实世界数据集进行评估时,该模型表现出了强大的性能.
  • 分析推导为模型的决策过程提供了洞察力.

结论:

更多相关视频

Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke
05:30

Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke

Published on: October 10, 2025

372
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.4K

相关实验视频

Last Updated: Jan 8, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.6K
Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke
05:30

Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke

Published on: October 10, 2025

372
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.4K
  • 图形神经网络是分析复杂系统中信息传播动态的有效工具.
  • 开发的GNN框架提供了一种强大且易于解释的状态识别方法.
  • 这种方法在涉及网络系统的各个领域都有潜在的应用.