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相关概念视频

Degree of Curvature and Radius of Curvature01:19

Degree of Curvature and Radius of Curvature

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The degree of curvature and the radius of curvature are fundamental concepts in determining the sharpness or smoothness of a curve. The degree of curvature is a measure of how steeply a curve bends and can be determined using the chord basis or the arc basis. In the chord basis method, the degree of curvature is defined as the central angle subtended by a chord of 30.48 meters, helping in the calculation of the radius of the curve. The arc basis method defines the degree of...
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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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Divergence and Curl01:15

Divergence and Curl

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The divergence of a vector field at a point is the net outward flow of the flux out of a small volume through a closed surface enclosing the volume, as the volume tends to zero. More practically, divergence measures how much a vector field spreads out or diverges from a given point. For an outgoing flux, conventionally, the divergence is positive. The diverging point is often called the "source" of the field. Meanwhile, the negative divergence of a vector field at a point means that the...
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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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Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

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The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
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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: Jul 18, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

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Published on: June 13, 2025

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基于里奇曲线的图形散散化用于学习连续图形表示.

Xikun Zhang, Dongjin Song, Dacheng Tao

    IEEE transactions on neural networks and learning systems
    |August 21, 2023
    PubMed
    概括

    这项研究介绍了子图形插曲记忆 (SEM) 对于持续的图形学习,保存关键的拓数据. 在具有挑战性的增量设置中,SEM显著提高了图表表示学习性能.

    科学领域:

    • 图形神经网络的神经网络
    • 机器学习 机器学习
    • 人工智能的人工智能

    背景情况:

    • 持续学习的目的是学习新的任务,而不忘记以前的任务.
    • 记忆重复对于欧几里得数据是有效的,但与图形数据的拓性质斗争.
    • 现有的图形学习方法在内存重播过程中经常忽略边缘信息.

    研究的目的:

    • 开发一种新的记忆重复技术,用于持续的图形表示学习.
    • 解决现有方法在图表中捕获拓信息方面的局限性.
    • 在持续学习场景中提高图形神经网络的性能和效率.

    主要方法:

    • 建议使用Subgraph Episodic Memory (SEM) 来存储拓信息作为计算子图.
    • 使用Ricci曲率进行图形散射,识别信息节点和边缘.
    • 开发了一个Ricci曲率的计算效率替代品,用于大规模的图形应用.

    主要成果:

    • 在四个公共数据集上,SEM显著超过了最先进的方法.
    • 在具有挑战性的班级增量学习 (class-IL) 环境中取得了强的表现.
    • 证明了与IL类联合培训相当的表现,这是一个显著的进步.

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    结论:

    • SEM有效地保存和重置关键的拓信息,以实现持续的图形学习.
    • 基于里奇曲率的散射增强了记忆效率和学习性能.
    • 对于任务IL和类IL设置,SEM提供了一个强大的解决方案,推进基于图形的持续学习.