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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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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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State Space Representation01:27

State Space Representation

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The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
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End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

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A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
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Graphical and Analytic Representation of Sinusoids01:20

Graphical and Analytic Representation of Sinusoids

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Analyzing two sinusoidal voltages with equal amplitude and period but different phases on an oscilloscope, an instrument used to display and analyze waveforms, involves a three-step process.
The first step is measuring the peak-to-peak value, which is twice the amplitude of the sinusoid. This provides information about the maximum voltage swing of the waveform.
Secondly, the period and angular frequency are determined. The period is the time taken for one complete cycle of the waveform, while...
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Introduction to Surveying, Plane Surveying and Geodetic Surveys01:27

Introduction to Surveying, Plane Surveying and Geodetic Surveys

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Surveying is the art and science of mapping the earth's surface. It involves measuring distances, angles in horizontal or vertical directions, and levels to understand the shape and size of land features. Surveying techniques are essential for various tasks, such as identifying the levels of a land area with reference to a specific point, and mapping undulations and water bodies.There are two main types of surveying: plane surveys and geodetic surveys. Plane surveys assume the earth is flat,...
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相关实验视频

Updated: Jul 1, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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关于深度图表表示学习的全面调查

Wei Ju1, Zheng Fang2, Yiyang Gu1

  • 1School of Computer Science, National Key Laboratory for Multimedia Information Processing, Peking University, Beijing, 100871, China.

Neural networks : the official journal of the International Neural Network Society
|March 5, 2024
PubMed
概括
此摘要是机器生成的。

本调查探讨了深度图表表示学习,突出了传统方法的局限性和深度学习的优势,特别是图表神经网络,用于编码复杂的图表数据.

关键词:
在图表上进行深度学习.图表神经网络的神经网络图形表示学习学习学习图形表示.调查 调查 调查 调查

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科学领域:

  • 机器学习 机器学习
  • 数据挖掘 数据挖掘
  • 人工智能的人工智能

背景情况:

  • 图表表示学习将高维图数据编码为低维向量.
  • 传统方法保留了节点的近距离,但具有有限的容量和学习范式.
  • 深度图形表示学习,特别是图形神经网络 (GNN),提供了先进的解决方案.

研究的目的:

  • 提供当前深度图表表示学习算法的全面调查.
  • 提出一种新的分类法来对最先进的文学进行分类.
  • 讨论实际应用和未来的研究方向.

主要方法:

  • 基本的图形表示学习组件的系统总结.
  • 基于GNN架构和学习范式的现有方法的分类.
  • 综述了深度图表表示学习的最新进展.

主要成果:

  • 鉴定了传统图形嵌入技术的局限性.
  • 突出了深度学习模型的潜力和优势,特别是GNN.
  • 为了更好地理解,把现有的文学整理成一个新的分类学.

结论:

  • 深度图表表示学习显著优于传统方法.
  • GNN代表了从图形结构数据中学习的强大范式.
  • 未来的研究应该探索该领域的新视角和具有挑战性的方向.