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Related Concept Videos

Types Of Transformers01:16

Types Of Transformers

987
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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Transformers01:26

Transformers

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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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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...
162
The Ideal Transformer01:26

The Ideal Transformer

407
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...
407
Transformers in Distribution System01:27

Transformers in Distribution System

104
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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Force Classification01:22

Force Classification

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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
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Related Experiment Video

Updated: Jul 11, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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Twitter-based gender recognition using transformers.

Zahra Movahedi Nia1,2, Ali Ahmadi3,4, Bruce Mellado1,5

  • 1Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), York University, Canada.

Mathematical Biosciences and Engineering : MBE
|November 3, 2023
PubMed
Summary

This study introduces a transformer-based model to predict user gender from images and tweets on social media, enhancing health research by accessing private demographic data. The multimodal approach achieves high accuracy, outperforming existing methods.

Keywords:
BERTELECTRALeViTRoBERTaSwin transformerViTgender recognitionsocial mediatransformers

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Area of Science:

  • Computational social science
  • Machine learning for health research
  • Natural language processing and computer vision for social media analysis

Background:

  • Social media data offers valuable insights for health research, including mental health and socio-economic inequality.
  • User demographics, such as gender, are crucial for deeper analysis but are often private and unavailable.
  • Existing methods for gender prediction from social media are limited, especially when users do not provide explicit gender information or identifiable images.

Purpose of the Study:

  • To develop and evaluate a multimodal deep learning model for predicting user gender from both images and text (tweets) on social media.
  • To compare the performance of various transformer-based models for image and text classification tasks in gender prediction.
  • To demonstrate the benefit of combining image and text data for more accurate and robust gender identification in social media research.

Main Methods:

  • Fine-tuning transformer models, including Vision Transformers (ViT), LeViT, and Swin Transformer, for gender classification using profile images and user-posted images from Kaggle and PAN-18 datasets.
  • Fine-tuning transformer models like BERT, RoBERTa, and ELECTRA for gender prediction based on user tweets.
  • Evaluating the significance of image and text classification models using the Mann-Whitney U test and combining models for improved accuracy.

Main Results:

  • Transformer models fine-tuned on image data (Kaggle and PAN-18) and text data (tweets) showed significant gender prediction capabilities.
  • The combined multimodal approach significantly improved accuracy over individual image or text models.
  • The multimodal model achieved high overall accuracy (88.11% on Kaggle, 89.24% on PAN-18), outperforming state-of-the-art methods.

Conclusions:

  • A multimodal approach integrating image and text analysis effectively predicts user gender from social media data.
  • Transformer-based models demonstrate strong performance in both image and text classification for gender recognition.
  • This method provides a valuable tool for researchers needing demographic data, particularly gender, to advance health-related social media studies.