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相关概念视频

Transformers in Distribution System01:27

Transformers in Distribution System

127
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...
127
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

181
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...
181
Types Of Transformers01:16

Types Of Transformers

1.0K
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...
1.0K
Energy Losses in Transformers01:21

Energy Losses in Transformers

904
In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality,  the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
There are four main reasons for energy losses in transformers.
The first cause can be  the high resistance of the...
904
Transformers01:26

Transformers

1.1K
A device that transforms voltages from one value to another using induction is called a transformer. A transformer consists of two separate coils, or windings, wrapped around the same soft iron core. However, they are electrically insulated from each other.
The iron core has a substantial relative permeability. Therefore, the magnetic field lines generated due to the current in one winding are almost entirely confined within the core, such that the same magnetic flux permeates each turn of both...
1.1K
Velocity and Position by Graphical Method01:34

Velocity and Position by Graphical Method

7.5K
Velocity and position can be calculated from the known function of acceleration as a function of time. The total area under the acceleration-time graph and the velocity-time graph gives the change in velocity and position, respectively. In the case of an airplane, its acceleration is tracked using the inertial navigation system. The pilot provides the input of the airplane's initial position and velocity before takeoff. The inertial navigation system then uses the acceleration data to...
7.5K

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相关实验视频

Updated: Jul 24, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

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探索变压器和图形卷积网络用于人类移动性建模.

Riccardo Corrias1, Martin Gjoreski1, Marc Langheinrich1

  • 1Computer Systems Institute, Faculty of Informatics, Università della Svizzera italiana (USI), 6900 Lugano, Switzerland.

Sensors (Basel, Switzerland)
|July 11, 2023
PubMed
概括

使用通用变压器 (GPT) 和图形卷积网络 (GCN) 的新人工智能模型显示出预测人类下一个位置的流动性的前景. 虽然在稀疏数据上的专业模型略高于它们的表现,但在密集数据集上的表现表明了先进的移动性估计的未来潜力.

科学领域:

  • 人工智能的人工智能
  • 人类流动性研究 人类流动性研究
  • 数据科学数据科学数据科学

背景情况:

  • 准确估计人类流动模式对于城市规划,污染控制和疾病管理至关重要.
  • 对于移动性估计至关重要的下一个位置预测器,尚未利用像GPT和GCNs这样的先进AI技术.

研究的目的:

  • 探索基于通用变压器 (GPT) 和图形卷积网络 (GCN) 模型的有效性,以预测下一个位置.
  • 在稀疏和密集的移动数据集上,将GPT和GCN模型的性能与最新的模型 (Flashback-LSTM) 进行比较.

主要方法:

  • 开发基于GPT和GCN的模型,根据一般时间序列预测架构进行调整.
  • 通过使用两个稀疏 (入住) 和一个密集 (连续GPS) 人类移动数据集来评估模型性能.

主要成果:

  • 基于GPT的模型在基于GCN的模型 (1.03.2 p.p.) 上显示出轻微的精度优势. 在测试过的数据集上.
  • Flashback-LSTM在稀疏的数据集 (1.03.5pp) 上略高于GPT和GCN模型的表现. 差异) 的区别.
  • 所有评估的模型在密集的GPS数据集上都具有可比的性能.

结论:

关键词:
深度学习是一种深度学习.图表 卷积网络 卷积网络机器学习是机器学习.移动性的建模模型.变压器 变压器

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  • 在下一个位置的预测中,GPT和GCN模型的性能与最先进的方法相美.
  • 随着未来的移动数据变得更加密集 (例如,来自智能手机),稀疏数据性能的相关性可能会减少.
  • 对于GPT和GCN模型来说,有很大的潜力来推进超越当前最先进的能力的人类流动性预测.