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相关概念视频

Transformers in Distribution System01:27

Transformers in Distribution System

491
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...
491
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

511
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...
511
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

426
An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
426
Types Of Transformers01:16

Types Of Transformers

1.4K
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...
1.4K
Instrument Transformers01:23

Instrument Transformers

432
Instrument transformers, comprising voltage transformers (VTs) and current transformers (CTs), play crucial roles in power substations by providing isolated replicas of current or voltage for measurement and protection purposes. Voltage transformers reduce the primary voltage to levels suitable for relay operation and measurement, while current transformers scale down the primary current. The primary winding of a current transformer often consists of a single turn, achieved by threading the...
432
Transformers01:26

Transformers

1.7K
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...
1.7K

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相关实验视频

Updated: Jan 13, 2026

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LiT:限量订单簿变压器

Yue Xiao1, Carmine Ventre1, Yuhan Wang2

  • 1Finance Hub, Department of Informatics, King's College London, London, United Kingdom.

Frontiers in artificial intelligence
|October 29, 2025
PubMed
概括
此摘要是机器生成的。

我们介绍了极限订单簿转换器 (LiT),这是一个用于金融市场预测的新型深度学习模型. LiT有效地捕捉了市场微观结构的动态,在限量订单簿分析中表现优于现有的方法.

关键词:
深度学习是一种深度学习.高频交易是一种高频交易.限量订单簿 限量订单簿市场微观结构 市场微观结构代表性学习学习学习转移学习转移学习变压器 变压器 变压器

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科学领域:

  • 人工智能的人工智能
  • 计算金融是指计算金融.
  • 深度学习 (Deep Learning) 是一种深度学习.

背景情况:

  • 变压器架构在NLP和计算机视觉方面取得了成功.
  • 变压器对于极限订单簿 (LOB) 预测的有限应用.
  • 需要在LOB数据中捕捉空间和时间依赖性的模型.

研究的目的:

  • 介绍极限订单簿转换器 (LiT),一种新的深度学习架构.
  • 使用高频限量订单簿数据预测短期市场变动.
  • 在LOB预测中捕捉空间和时间依赖性的地址限制.

主要方法:

  • 利用结构化补丁和基于变压器的自我注意力.
  • 在市场微观结构动态中模拟空间和时间特征.
  • 使用高频限量订单簿数据进行预测.

主要成果:

  • LiT的表现始终优于传统的机器学习方法.
  • LiT超越了最先进的深度学习基线.
  • 通过微调,LiT在分布变化下表现强.

结论:

  • LiT是一种用于LOB预测的新且有效的深度学习架构.
  • 对于动态的金融环境,LiT提供了一个实用的解决方案.
  • 该模型成功地捕捉了复杂的市场微观结构动态.