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相关概念视频

Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

12.0K
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...
12.0K
Associative Learning01:27

Associative Learning

333
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
333
Vector Algebra: Method of Components01:08

Vector Algebra: Method of Components

13.9K
It is cumbersome to find the magnitudes of vectors using the parallelogram rule or using the graphical method to perform mathematical operations like addition, subtraction, and multiplication. There are two ways to circumvent this algebraic complexity. One way is to draw the vectors to scale, as in navigation, and read approximate vector lengths and angles (directions) from the graphs. The other way is to use the method of components.
In many applications, the magnitudes and directions of...
13.9K
Multiple Bar Graph01:07

Multiple Bar Graph

5.1K
As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
5.1K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

105
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
105
pV-Diagrams01:18

pV-Diagrams

4.1K
The pV diagram, which is a graph of pressure versus volume of the gas under study, is helpful in describing certain aspects of the substance. When the substance behaves like an ideal gas, the ideal gas equation describes the relationship between its pressure and volume. On a pV diagram, it is common to plot an isotherm, which is a curve showing p as a function of V with the number of molecules and the temperature fixed. Then, for an ideal gas, the product of the pressure of the gas and its...
4.1K

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相关实验视频

Updated: Jun 21, 2025

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

16.8K

多变量网络的视觉分析与表示学习和复合变量构建.

Hsiao-Ying Lu, Takanori Fujiwara, Ming-Yi Chang

    IEEE transactions on visualization and computer graphics
    |July 5, 2024
    PubMed
    概括

    本研究介绍了一种视觉分析工作流程,以了解复杂的多变量网络. 它使用神经网络和交互式可视化来揭示网络属性之间的关联,帮助数据解释.

    科学领域:

    • 数据科学数据科学数据科学
    • 网络分析 网络分析
    • 信息可视化 信息可视化

    背景情况:

    • 多变量网络在现实世界的数据应用中很普遍.
    • 了解这些网络中的关系是具有挑战性的.
    • 现有的方法缺乏对复杂网络特征的直观解释.

    研究的目的:

    • 为研究多变量网络提供视觉分析工作流.
    • 提取结构和语义网络特征之间的关联.
    • 为了简化复杂的网络数据,以便用户解释.

    主要方法:

    • 一个基于神经网络的数据分类学习阶段.
    • 一个维度减小和优化阶段,以简化结果.
    • 一个交互式可视化界面用于用户解释.
    • 复合变量构造以线性化非线性特征.

    主要成果:

    • 在社交媒体网络数据上展示了工作流的能力.
    • 成功地提取了网络属性之间的关联.
    • 通过可视化实现复杂网络特征的直观解释.
    • 通过专家的定性反来验证.

    更多相关视频

    Cross-Modal Multivariate Pattern Analysis
    13:51

    Cross-Modal Multivariate Pattern Analysis

    Published on: November 9, 2011

    19.9K
    Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
    10:58

    Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

    Published on: January 2, 2011

    10.1K

    相关实验视频

    Last Updated: Jun 21, 2025

    Basics of Multivariate Analysis in Neuroimaging Data
    06:35

    Basics of Multivariate Analysis in Neuroimaging Data

    Published on: July 24, 2010

    16.8K
    Cross-Modal Multivariate Pattern Analysis
    13:51

    Cross-Modal Multivariate Pattern Analysis

    Published on: November 9, 2011

    19.9K
    Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
    10:58

    Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

    Published on: January 2, 2011

    10.1K

    结论:

    • 拟议的视觉分析工作流有效地帮助理解多变量网络.
    • 工作流有助于发现网络属性之间的关联.
    • 复合变量构造提高了神经网络输出的可解释性,用于网络分析.