Jove
Visualize
联系我们

相关概念视频

Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

12.2K
Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
12.2K
SFG Algebra01:16

SFG Algebra

118
In Signal Flow Graph (SFG) algebra, the value a node represents is determined by the sum of all signals entering that node. This summed value is then transmitted through every branch leaving the node, making the SFG a powerful tool for visualizing and analyzing control systems.
Each node in an SFG corresponds to a variable, and the interactions between nodes are represented by branches with associated gains. When multiple branches lead into a node, the value at that node is the sum of the...
118
Relation between Mathematical Equations and Block Diagrams01:20

Relation between Mathematical Equations and Block Diagrams

361
In a spring-mass-damper system, the second-order differential equation describes the dynamic behavior of the system. When transformed into the Laplace domain under zero initial conditions, this equation can be effectively analyzed and manipulated. The transformation into the Laplace domain converts differential equations into algebraic equations, simplifying the process of isolating the output.
361
Linear Circuits01:17

Linear Circuits

406
A linear circuit is characterized by its output having a direct proportionality to its input, adhering to the linearity property, which encompasses the principles of homogeneity (scaling) and additivity. Homogeneity dictates that when the input, also referred to as the excitation, is multiplied by a constant factor, the output, known as the response, is correspondingly scaled by the same constant factor. For instance, if the current is multiplied by a constant 'k,' the voltage likewise...
406
Signal Flow Graphs01:18

Signal Flow Graphs

225
Signal-flow graphs offer a streamlined and intuitive approach to representing control systems, providing an alternative to traditional block diagrams. These graphs use branches to symbolize systems and nodes to represent signals, effectively illustrating the relationships and interactions within the system.
In a signal-flow graph, branches denote the system's transfer functions, while nodes represent the signals. The direction of signal flow is indicated by arrows, with the corresponding...
225
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

631
A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of...
631

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

SiCRNN: A Siamese Approach for Sleep Apnea Identification via Tracheal Microphone Signals.

Sensors (Basel, Switzerland)·2024
Same author

Few-Shot Emergency Siren Detection.

Sensors (Basel, Switzerland)·2022
Same author

A Combined One-Class SVM and Template-Matching Approach for User-Aided Human Fall Detection by Means of Floor Acoustic Features.

Computational intelligence and neuroscience·2017
Same author

Deep Recurrent Neural Network-Based Autoencoders for Acoustic Novelty Detection.

Computational intelligence and neuroscience·2017
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

相关实验视频

Updated: Jul 5, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.3K

一个基于图的神经方法对线性和分配问题.

Carlo Aironi1, Samuele Cornell1, Stefano Squartini1

  • 1Department of Information Engineering, Università Politecnica delle Marche, Italy.

International journal of neural systems
|January 17, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了一个图形神经网络 (GNN) 来解决线性赋值问题,实现比现有方法更高的准确性和效率. 这种方法显示出强大的可扩展性,用于智能电网能源管理等实际应用.

关键词:
线性和的分配线性和的分配深度神经网络是一个神经网络.图形神经网络的神经网络智能电网优化的优化智能电表调度时间表

更多相关视频

Brain Mapping Using a Graphene Electrode Array
10:32

Brain Mapping Using a Graphene Electrode Array

Published on: October 20, 2023

1.8K
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

1.1K

相关实验视频

Last Updated: Jul 5, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.3K
Brain Mapping Using a Graphene Electrode Array
10:32

Brain Mapping Using a Graphene Electrode Array

Published on: October 20, 2023

1.8K
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

1.1K

科学领域:

  • 组合优化的优化.
  • 机器学习 机器学习
  • 图形神经网络的神经网络

背景情况:

  • 线性赋值问题在计算上具有挑战性,通常需要启发式解决方案.
  • 现有的深度神经网络 (DNN) 方法在效率和准确性方面存在局限性.
  • 智能电网需要高效的解决方案来完成诸如智能电表调度等任务.

研究的目的:

  • 使用图形神经网络 (GNN) 开发用于线性赋值问题的通用学习策略.
  • 与现有的DNN方法相比,提高分类准确性和计算效率.
  • 应用GNN方法来优化智能电网中的智能电表调度.

主要方法:

  • 使用双边图来建模问题的结构.
  • 采用传递信息的图形神经网络 (GNN) 来学习最佳的任务.
  • 通过模拟,将GNN方法与两个现有的DNN解决方案进行了比较.

主要成果:

  • 拟议的GNN模型显著提高了对现有DNN的分类准确性.
  • 由于参数共享,GNN方法在处理时间和内存使用方面表现出卓越的效率.
  • 基于图形的解决方案显示了智能电网应用的高可扩展性,优于其他启发式方法.

结论:

  • 基于GNN的策略为线性赋值问题提供了有效和高效的解决方案.
  • 该方法具有高度可扩展性,适用于复杂的现实应用,如智能电网管理.
  • 该研究为组合优化挑战提供了可重现的GNN解决方案.