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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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Vector Algebra: Method of Components01:08

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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...
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Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

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Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
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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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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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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...
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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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相关实验视频

Updated: Sep 12, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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DREAM:用于无监督图域适应的双变量框架.

Nan Yin, Li Shen, Mengzhu Wang

    IEEE transactions on pattern analysis and machine intelligence
    |August 5, 2025
    PubMed
    概括

    双变义语义图表挖掘 (DREAM) 通过整合隐式和显式结构语义来解决无监督图域的适应. 这种方法提高了图形分类的准确性,克服了传统消息传递神经网络 (MPNNs) 的局限性.

    科学领域:

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

    背景情况:

    • 图形分类在机器学习中至关重要,通常使用消息传递神经网络 (MPNNs).
    • MPNN默认地学习拓语义,但在不受监督的域适应中与域移动和有限的标签作斗争.
    • 现有的方法在提取全面的图形结构语义方面面临挑战.

    研究的目的:

    • 为无监督的图域适应提出双变义语义图形挖掘 (DREAM).
    • 结合隐式和显式的图形结构语义,以改善图形表示.
    • 在域调整场景中增强图形分类模型的稳定性和准确性.

    主要方法:

    • DREAM采用双分支架构:一个隐式语义的消息传递分支,一个显式高阶结构语义的路径聚合分支.
    • 预期最大化 (EM) 风格的变化框架用于两个分支机构的联合培训.
    • E步构建了一个图的图表以捕捉域间的相关性,而M步改进了MPNNs.

    主要成果:

    • 拟议的DREAM方法与基准数据集的现有基线相比,显示出更高的性能.
    • 隐式和显式语义的联合优化有效地改善了图形分类.
    • 实验验证证证实了DREAM方法在无监督域适应中的有效性.

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    结论:

    • 通过利用互补的图形结构语义,DREAM为无监督图域适应提供了一个有效的解决方案.
    • 显式和隐式学习路径的整合增强了图形神经网络的概括能力.
    • 这项研究证实了DREAM的优势,为更强大的图形分类模型铺平了道路.