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相关概念视频

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
Stereotype Content Model02:16

Stereotype Content Model

15.3K
The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
15.3K
Transformers in Distribution System01:27

Transformers in Distribution System

485
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...
485
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

493
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...
493
Classification of Systems-I01:26

Classification of Systems-I

540
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
540

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相关实验视频

Updated: Jan 9, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

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

Published on: December 6, 2024

994

闭域语义问题答案系统作为基于变压器的BERT模型的用例

Debayan Bhattacharya1, Koj Sambyo2, Rana Majumdar3

  • 1Department of Computer Science and Engineering, National Institute of Technology Arunachal Pradesh; debayan.phd23@nitap.ac.in.

Journal of visualized experiments : JoVE
|December 1, 2025
PubMed
概括

谷歌-BERT通过理解同义词在问答 (QA) 方面表现出色,但与拼写错误作斗争. 这项研究比较了从文本中提取知识的变压器模型.

相关实验视频

Last Updated: Jan 9, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

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

Published on: December 6, 2024

994

科学领域:

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

背景情况:

  • 问答系统 (QAS) 对于从文本中提取知识至关重要.
  • 变压器模型的进步为直接响应质量保证任务提供了新的可能性.

研究的目的:

  • 评估和比较预训练过的变压器模型的推断能力,以确保质量.
  • 根据传统的TF-IDF模型来评估性能.

主要方法:

  • 使用语义封闭域QA (SCD-QA) 数据集与事实和非事实问题.
  • 将Google-BERT,DistilBERT和RoBERTa与SQuAD数据集上的TF-IDF模型进行比较.
  • 测量精确匹配 (EM) 得分和延迟.

主要成果:

  • 谷歌-BERT以90.0的EM得分和1.27秒的延迟实现了最高的性能.
  • 变压器模型表现出对语义意义的强烈理解,包括同义词.
  • 在有拼写错误的问题上表现下降,突出了对拼写错误的敏感性.

结论:

  • 谷歌-BERT是质量保证任务的高效模型,特别是在语义变化方面.
  • 需要进一步改进预处理,以解决对拼写错误的敏感性.
  • 该研究验证了变压器模型在知识提取和质量控制方面的有效性.