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相关概念视频

Transformers in Distribution System01:27

Transformers in Distribution System

102
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...
102
Types Of Transformers01:16

Types Of Transformers

975
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...
975
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

152
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...
152
Energy Losses in Transformers01:21

Energy Losses in Transformers

872
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...
872
The Ideal Transformer01:26

The Ideal Transformer

391
In single-phase two-winding transformers, two windings are coiled around a magnetic core characterized by cross-sectional area A and magnetic permeability μ. A phasor current i1 enters the left winding while i2 exits the right winding, establishing the fundamental working of the transformer through electromagnetic principles.
Ampere's Law forms the basis of understanding the magnetic field within the transformer. It states that the integral of the magnetic field intensity's...
391
First Order Systems01:21

First Order Systems

90
First-order systems, such as RC circuits, are foundational in understanding dynamic systems due to their straightforward input-output relationship. Analyzing their responses to different input functions under zero initial conditions reveals significant insights into system behavior.
When a first-order system is subjected to a unit-step input, its response is characterized by its transfer function. By applying the Laplace transform of the unit-step input to the transfer function, expanding the...
90

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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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学习随机动态,并使用变压器预测新出现的行为.

Corneel Casert1,2, Isaac Tamblyn3,4,5, Stephen Whitelam6

  • 1Molecular Foundry, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, 94720, USA. ccasert@lbl.gov.

Nature communications
|February 29, 2024
PubMed
概括
此摘要是机器生成的。

一个变压器神经网络从单个轨迹中学习了复杂的系统动态. 这种人工智能模型准确地预测了在未见的条件下出现的行为,包括相位过渡.

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科学领域:

  • 物理 物理学 物理
  • 人工智能的人工智能
  • 复杂的系统复杂的系统.

背景情况:

  • 随机系统和活性物质表现出复杂的动态.
  • 在这些系统中预测新出现的行为往往需要详细了解底层规则.
  • 神经网络,特别是变压器,在学习复杂模式方面表现有前途.

研究的目的:

  • 为了研究一个变压器神经网络是否可以从有限的数据中学习一个随机系统的动态规则.
  • 评估受过训练的网络在新条件下对新出现的行为进行预测的能力.
  • 探索变压器在理解复杂物理系统中的应用.

主要方法:

  • 在活性物质格子模型的单个轨迹上训练变压器神经网络.
  • 模拟活性物质模型在特定密度下,它形成分散的集群.
  • 评估变压器在训练过程中未遇到的密度下对系统行为的预测.

主要成果:

  • 变压器成功地学习了随机系统的基本动态规则.
  • 经过训练的网络准确地预测了新密度的运动性诱导相位分离.
  • 该模型展示了能够表示众多和非局部动态规则的能力.

结论:

  • 变压器可以从观测数据中学习复杂的系统动态,即使只有一条轨迹.
  • 这种方法可以在未见的条件下准确预测新出现的行为和相位过渡.
  • 该方法提供了一种灵活的方法来研究各种物理系统,而无需明确的速率计数或粗粒度.