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相关概念视频

Histogram01:05

Histogram

12.6K
The histogram is a graphical representation in the x-y form of data distribution in a data set. The horizontal x-axis is labeled with what the data represents (for instance, distance from your home to school). The vertical y-axis is labeled either frequency or relative frequency (or percent frequency or probability).
A histogram graph consists of contiguous (adjoining) boxes. The heights of the bars correspond to frequency values. The graph will have the same shape with respective labels. The...
12.6K
Relative Frequency Histogram01:14

Relative Frequency Histogram

5.4K
The relative frequency depicts the proportion of data points that have each value. The frequency tells the number of data points that have each value. Like the histogram, a relative frequency histogram also has the same shape with a horizontal scale (the x-axis), but the vertical scale (the y-axis) is marked with relative frequencies (percentages of the whole) instead of actual frequencies. A relative frequency histogram is a graphical representation of a frequency distribution where the...
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Probability Histograms01:17

Probability Histograms

11.0K
A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
11.0K
Routh-Hurwitz Criterion I01:15

Routh-Hurwitz Criterion I

134
Consider an electrical power grid, where stability is essential to prevent blackouts. The Routh-Hurwitz criterion is a valuable tool for assessing system stability under varying load conditions or faults. By analyzing the closed-loop transfer function, the Routh-Hurwitz criterion helps determine whether the system remains stable.
To apply the Routh-Hurwitz criterion, a Routh table is constructed. The table's rows are labeled with powers of the complex frequency variable s, starting from the...
134
Routh-Hurwitz Criterion II01:19

Routh-Hurwitz Criterion II

170
In the application of the Routh-Hurwitz criterion, two specific scenarios can arise that complicate stability analysis.
The first scenario occurs when a singular zero appears in the first column of the Routh table. This situation creates a division by zero issues. To resolve this, a small positive or negative number, denoted as epsilon (∈), is substituted for the zero. The stability analysis proceeds by assuming a sign for ∈. If ∈ is positive, any sign change in the first...
170
Statgraphics01:10

Statgraphics

99
Statgraphics is a comprehensive statistical software suite designed for both basic and advanced data analysis. Originating in 1980 at Princeton University under Dr. Neil W. Polhemus, it was one of the pioneering tools for statistical computing on personal computers, with its public release in 1982 marking an early milestone in data science software. Over the years, it has evolved into a robust platform for data science, offering tools for regression analysis, ANOVA, multivariate statistics,...
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相关实验视频

Updated: May 24, 2025

Generating Strictly Controlled Stimuli for Figure Recognition Experiments
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Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

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人口普查-Stub图形不变描述符.

Matt I B Oddo, Stephen Kobourov, Tamara Munzner

    IEEE transactions on visualization and computer graphics
    |March 3, 2025
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    概括
    此摘要是机器生成的。

    一种新的方法,BFS-Census,有效地描述了网络结构,克服了可视化挑战. 人口普查-Stub是BFS-Census的一个组成部分,它提供了卓越的网络辨别能力,有效地利用资源.

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    相关实验视频

    Last Updated: May 24, 2025

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    Published on: March 18, 2019

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    科学领域:

    • 图形理论是指图形的理论.
    • 网络分析 网络分析
    • 数据可视化数据可视化

    背景情况:

    • 传统的网络可视化,如节点链路图,遭受了"毛球现象",掩盖了网络结构.
    • 不变描述符通过总结网络特征提供了一个替代方案,但设计它们需要平衡数据抽象与信息保留.
    • 以前的工作包括BMatrix描述器,可视化为"网络肖像"热图.

    研究的目的:

    • 介绍BFS-Census,这是一个用于计算网络不变描述符的新算法.
    • 开发新的数据结构:人口普查节点,人口普查边缘和人口普查.
    • 评估这些新描述符的性能和可视化功能.

    主要方法:

    • 开发了BFS-人口普查算法来计算人口普查数据结构.
    • 专注于人口普查-Stub描述符,该描述符分析网络'stubs' (半边).
    • 创建了新的可视化:Hop-Census多线和人口普查-人口普查轨迹.

    主要成果:

    • 与研究中的其他描述符相比,人口普查-Stub表现出数量级更大的辨别能力.
    • 这种增强的分辨率可以在没有显著增加存储空间或计算成本的情况下实现.
    • 新的可视化有效地将图形拓变化映射到人口普查表示中的视觉变化.

    结论:

    • BFS-Census,特别是Census-Stub描述符,为网络分析提供了一种强大而有效的方法.
    • 开发的可视化提供了直观的方式来探索网络结构及其变化.
    • 这种方法有效地解决了传统网络可视化技术的局限性.