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相关概念视频

Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

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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...
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Functional Classification of Joints01:09

Functional Classification of Joints

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Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
An...
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Structural Classification of Joints01:20

Structural Classification of Joints

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Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
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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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Ogive Graph01:07

Ogive Graph

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An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
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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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相关实验视频

Updated: Jun 23, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain
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精制欧几里德模糊节点有助于:用于图形神经网络的联合空间图形学习方法.

Zhaogeng Liu, Feng Ji, Jielong Yang

    IEEE transactions on neural networks and learning systems
    |June 14, 2024
    PubMed
    概括

    本研究介绍了用于图形神经网络 (GNN) 的联合空间图形学习 (JSGL). JSGL 完善了超标空间中的图形拓,以解决与欧几里德嵌入问题的问题,并提高节点分类准确性.

    科学领域:

    • 机器学习 机器学习
    • 图形神经网络的神经网络
    • 数据挖掘 数据挖掘

    背景情况:

    • 图形神经网络 (GNN) 需要预定义的图形结构,这限制了它们的适用性.
    • 现有的方法共同学习图形结构和GNN参数,但往往假定空间曲率是恒定的 (欧几里德或过度).
    • 恒定曲率假设可以导致模糊节点,阻碍准确的节点嵌入和分类.

    研究的目的:

    • 为 GNNs 提出一种新的联合空间图形学习 (JSGL) 方法,处理非常数曲率.
    • 在图形学习中有效地识别和完善模糊节点的嵌入.

    主要方法:

    • JSGL使用欧几里德嵌入式学习初始图形结构.
    • 它确定了Euclidean空间中的模糊节点.
    • 然后,在这些模糊节点附近的图形拓学会使用超标空间嵌入来改进.

    主要成果:

    • JSGL成功地识别了嵌入不当的模糊节点.
    • 拟议的方法在实验评估中,与各种基线方法相比,显示出更高的性能.
    • 提供了模糊节点识别机制的理论理由.

    结论:

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    • 在非恒定的曲率的情况下,JSGL提供了一种有效的方法来学习图形结构.
    • 联合欧几里德-超标空间精细化解决了现有的GNN图形学习方法的局限性.
    • 这种方法提高了GNN的稳定性和准确性,当图形结构最初未知或复杂时.