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Related Concept Videos

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...
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Types Of Transformers01:16

Types Of Transformers

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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...
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Stereotype Content Model02:16

Stereotype Content Model

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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...
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Transformers in Distribution System01:27

Transformers in Distribution System

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

Transformers with Off-Nominal Turns Ratios

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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...
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Classification of Systems-I01:26

Classification of Systems-I

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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:
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Related Experiment Video

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

Closed Domain Semantic Question Answering System as a Use Case of Transformer-Based BERT Models.

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
Summary
This summary is machine-generated.

Google-BERT excels at question answering (QA) by understanding synonyms but struggles with spelling errors. This research compares transformer models for knowledge extraction from text.

Related Experiment Videos

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

Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Machine Learning

Background:

  • Question Answering Systems (QAS) are crucial for extracting knowledge from text.
  • Advancements in transformer models offer new possibilities for direct-answer QA tasks.

Purpose of the Study:

  • To evaluate and compare the inference capabilities of pre-trained transformer models for QA.
  • To assess performance against a traditional TF-IDF model.

Main Methods:

  • Utilized a Semantic Closed-domain QA (SCD-QA) dataset with factoid and non-factoid questions.
  • Compared Google-BERT, DistilBERT, and RoBERTa against a TF-IDF model on the SQuAD dataset.
  • Measured Exact Match (EM) scores and latency.

Main Results:

  • Google-BERT achieved the highest performance with a 90.0 EM score and 1.27s latency.
  • Transformer models demonstrated strong understanding of semantic meaning, including synonyms.
  • Performance degraded on questions with spelling errors, highlighting sensitivity to misspellings.

Conclusions:

  • Google-BERT is a highly effective model for QA tasks, particularly with semantic variations.
  • Further improvements in preprocessing are needed to address sensitivity to spelling errors.
  • The study validates the effectiveness of transformer models in knowledge extraction and QA.