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相关概念视频

Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

103
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...
103
Transformers in Distribution System01:27

Transformers in Distribution System

103
Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
103
Types Of Transformers01:16

Types Of Transformers

978
Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...
978
State Space Representation01:27

State Space Representation

209
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...
209
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

157
In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
157
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

406
Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
406

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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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空间时间知识嵌入式变压器用于视频场景图形生成

Tao Pu, Tianshui Chen, Hefeng Wu

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
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    概括
    此摘要是机器生成的。

    本研究介绍了用于视频场景图形生成 (VidSGG) 的时空知识嵌入式变压器 (STKET). 通过整合空间和时间的相关性,STKET增强了对象关系预测,优于现有的方法.

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    科学领域:

    • 计算机科学 计算机科学
    • 人工智能的人工智能
    • 机器学习 机器学习

    背景情况:

    • 视频场景图形生成 (VidSGG) 涉及识别视频中的对象及其关系.
    • 了解对象相互作用和时间动态对于准确的VidSGG至关重要.
    • 空间共发生和时间过渡相关性为VidSGG提供了有价值的先验知识.

    研究的目的:

    • 提出一个新的空间时间知识嵌入式变压器 (STKET) 改进 VidSGG.
    • 有效地将先前的时空知识纳入基于变压器的模型.
    • 在视频中增强代表关系表示的学习.

    主要方法:

    • 空间共发生和时间过渡相关性的统计学学习.
    • 设计空间和时间知识嵌入层,利用多头交叉注意.
    • 整合视觉表示与学习知识,用于嵌入式表示.
    • 聚合主体-对象对的表示,以最终预测.

    主要成果:

    • 与现有算法相比,拟议的STKET模型表现出优越的性能.
    • 在各种实验环境中观察到mR@50的显著改善 (例如8.1%,4.7%,2.1%).
    • 该方法有效地利用时空先验来进行更准确的关系推理.

    结论:

    • 通过嵌入时空知识,STKET提供了一个强大的视频场景图形生成框架.
    • 将先前的知识整合到注意力机制中,导致更具代表性的关系预测.
    • 拟议的方法代表了VidSGG领域的重大进步.