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相关概念视频

Reducing Line Loss01:18

Reducing Line Loss

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In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
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Neural Circuits01:25

Neural Circuits

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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...
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Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

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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...
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Design Example: Maintaining Level of an Embankment01:19

Design Example: Maintaining Level of an Embankment

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Constructing a roadway embankment over uneven terrain requires precise leveling to ensure stability and proper drainage. Surveyors use a leveling instrument and staff to calculate ground elevations and determine the required fill material at each point along the embankment alignment.The process begins by positioning a leveling instrument near a benchmark with a known elevation. A backsight reading establishes the instrument height, which serves as a reference for subsequent measurements. A...
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Survival Tree01:19

Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
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Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

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To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
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Updated: Jul 21, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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结构意识的下降边向深度图形的卷积网络.

Jiaqi Han, Wenbing Huang, Yu Rong

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    概括
    此摘要是机器生成的。

    深图卷积网络 (GCNs) 遭受过度平滑的影响. DropEdge++通过使用层依赖和特征依赖的边缘采样来增强GCN,提高节点分类任务的性能.

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    科学领域:

    • 图形神经网络 图形神经网络
    • 机器学习 机器学习
    • 深度学习 (Deep Learning) 是一种深度学习.

    背景情况:

    • 深度图形卷积网络 (GCNs) 由于过度平滑而面临性能下降.
    • 超平滑将网络输出与输入隔离,随着深度的增加而减少表达力和可训练性.

    研究的目的:

    • 通过解决过度平滑问题来提高深层GCN的性能.
    • 介绍DropEdge++,这是一个基于DropEdge.Edge的增强边缘采样技术.

    主要方法:

    • 推出了DropEdge++,其中包括两个结构意识样本:层依赖 (LD) 和功能依赖 (FD).
    • 调查了LD采样器,发现从上层底层增加了边缘采样.
    • 使用平均边缘数 (MEN) 度量理论分析现象.
    • FD采样器将边缘采样概率与节点特征相似性联系起来,将输出和输入特征空间关联起来.

    主要成果:

    • 与DropEdge和no-drop基线相比,DropEdge++实现了更高的性能.
    • 从底层取样增加边缘采样的LD采样器表现优于减少边缘采样器.
    • 通过将收子空间与输入特征对齐,FD采样器进一步提高了性能.
    • 在完全和半监督节点分类基准中证明了有效性.

    结论:

    • 在深层GCN中,DropEdge++有效地减轻了过度平滑.
    • 拟议的LD和FD采样器在性能和可训练性方面提供了显著的改进.
    • DropEdge++与各种GCN骨干兼容,展示了它的多功能性.