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Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

675
Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
675
Distribution Reliability and Automation01:25

Distribution Reliability and Automation

133
Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
133
Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

2.5K
The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an...
2.5K
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

238
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:
238
Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

4.3K
In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
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Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

125
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...
125

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相关实验视频

Updated: Jul 23, 2025

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

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以属性驱动的流媒体边缘分区与分布式图谱神经网络训练的和解.

Zongshen Mu1, Siliang Tang1, Yueting Zhuang1

  • 1Zhejiang University, Hangzhou, China.

Neural networks : the official journal of the International Neural Network Society
|July 19, 2023
PubMed
概括

本研究介绍了以属性驱动的流边缘分区与和解 (ASEPR) 进行高效的分布式图训练. 通过智能分区图形和调和异质模型,ASEPR显著降低了通信成本,并加快了融合速度.

科学领域:

  • 图形神经网络的神经网络
  • 分布式系统 分布式系统
  • 机器学习 机器学习

背景情况:

  • 目前的分布式图训练框架由于分离的存储和训练设计而遭受高通讯成本和缓慢的融合.
  • 通过忽视节点属性,传统的图形分区方法会产生内存开销和损坏语义结构.
  • 分布式培训中的异质局部模型通过简单的平均同步阻碍了趋同.

研究的目的:

  • 提出一种新的分布式图训练方法,属性驱动的流边缘分区与和解 (ASEPR).
  • 在分布式图训练中降低通信成本和内存开销.
  • 通过异构的本地模型,提高融合速度和全球模型性能.

主要方法:

  • ASEPR集群具有相似属性的节点,以保持语义结构和邻近本地.
  • 流式分区与属性聚类相结合,用于高效的子图赋值,减轻内存开销.
  • 使用跨层调和策略,包括知识蒸和对比学习,以从异构的本地模型中增强全球模型.

主要成果:

  • 在节点分类和链接预测任务上,ASEPR优于DistDGL等现有方法.
  • 拟议的方法需要更少的计算资源.
  • 与基线方法相比,ASEPR实现了与基线方法相比,融合速度增加了多达四倍.
关键词:
由属性驱动的流媒体边缘分区.分布式图形神经网络培训调解是为了调解.

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结论:

  • 通过优化图形分区和模型调和,ASEPR为分布式图形训练提供了有效的解决方案.
  • 该方法成功地解决了传统框架的局限性,从而提高了效率和性能.
  • 在资源利用和训练速度方面,ASEPR在大规模图形分析方面表现出显著的优势.