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相关概念视频

Transformers in Distribution System01:27

Transformers in Distribution System

475
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...
475
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

491
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...
491
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
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
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

467
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...
467
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

404
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...
404

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相关实验视频

Updated: Jan 8, 2026

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
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Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

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语境优先关注使变压器中的线性时间序列建模成为可能.

Karim Ben Khaled1,2, Davy Monticolo3,4

  • 1University of Lorraine, Nancy, France. karim.ben-khaled@univ-lorraine.fr.

Scientific reports
|December 17, 2025
PubMed
概括

语境优先注意 (CPA) 为变压器模型提供了一个新的解决方案,减少长序列的计算复杂性. 这种高效的注意力机制实现了线性扩展并保持了性能,为更有能力的大型语言模型铺平了道路.

科学领域:

  • 人工智能的人工智能
  • 自然语言处理自然语言处理.
  • 机器学习 机器学习

背景情况:

  • 变压器模型是NLP的基础,由于自我注意的二次复杂性,它们面临着计算限制,这阻碍了它们在长序列中使用.
  • 现有的高效的注意力机制往往接近全注意力矩阵,可能会牺牲性能.
  • 需要注意力机制,可以有效地扩展序列长度,而不会影响上下文理解.

研究的目的:

  • 引入上下文优先注意 (CPA),一种新的注意力机制,旨在克服变压器模型中标准自我注意的二次复杂性.
  • 从理论和经验上证明CPA实现线性计算缩放的能力,同时保持基本的上下文建模能力.
  • 评估CPA在各种NLP任务中的有效性,特别是涉及长序列的任务.

主要方法:

  • 开发了CPA,一个全球上下文驱动的优先级系统,计算全球上下文向量 (GCV) 来指导稀疏的注意力分配.
  • 在编码器-解码器架构的编码器中实现了CPA,在解码器中保留了对序列到序列任务的标准注意力.
  • 进行了理论分析和广泛的语言理解,翻译和长文档任务 (8K+代币) 的实验.

主要成果:

  • 在理论上,CPA可以将计算复杂性从O{\displaystyle O} n^2} 降低到O{\displaystyle O} n),并通过实验观察到线性缩放.

相关实验视频

Last Updated: Jan 8, 2026

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

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  • 在各种任务中,CPA实现了与标准自我注意力相当的性能.
  • 在长序基准测试中,CPA的表现优于传统的变压器和其他高效的注意力变体,使用的计算资源要少得多.
  • 结论:

    • 对于变压器模型来说,CPA比标准的自我注意力提供了显著的效率提升,特别是对于长序列.
    • 这些发现表明,对对象互动可能对有效的上下文建模不至关重要,挑战现有的假设.
    • CPA为开发更高效,更可扩展的大规模语言模型提供了一个有希望的方向.