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相关概念视频

Transformers01:26

Transformers

1.0K
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...
1.0K
Types Of Transformers01:16

Types Of Transformers

943
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...
943
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

129
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...
129
Energy Losses in Transformers01:21

Energy Losses in Transformers

819
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...
819
Transformers in Distribution System01:27

Transformers in Distribution System

98
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...
98
The Ideal Transformer01:26

The Ideal Transformer

342
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...
342

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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可能性主题建模与变压器表示.

Arik Reuter, Anton Thielmann, Christoph Weisser

    IEEE transactions on neural networks and learning systems
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    此摘要是机器生成的。

    我们介绍了变压器表示神经主题模型 (TNTM),这是一个新的方法,将变压器嵌入与概率主题建模相结合. 这种方法提高了自然语言处理任务的主题连贯性和多样性.

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    科学领域:

    • 自然语言处理 (NLP) 是一种自然语言处理.
    • 机器学习 机器学习
    • 人工智能的人工智能

    背景情况:

    • 主题建模传统上依赖于贝叶斯图形模型.
    • 最近的进步利用了变压器嵌入式用于主题发现.
    • 现有的基于变压器的方法通常使用简单的集群,缺乏概率深度.

    研究的目的:

    • 提出变压器表示神经主题模型 (TNTM).
    • 整合基于变压器的嵌入与概率主题建模.
    • 增强NLP的主题连贯性和多样性.

    主要方法:

    • 开发了TNTM,将变压器嵌入式与概率模型统一.
    • 利用变量自编码器 (VAE) 框架进行高效的推断.
    • 杆式基于变压器的嵌入空间用于主题表示.

    主要成果:

    • 在嵌入连贯性方面,TNTM 实现了最先进的性能.
    • 该模型保持了高度的主题多样性.
    • 实验结果表明,与现有方法相比,其性能具有竞争力.

    结论:

    • TNTM为主题建模提供了一种强大的混合方法.
    • 该模型成功地结合了变压器嵌入和概率方法的优势.
    • TNTM提供了一种灵活而有效的工具,用于发现文本数据中隐藏的主题.