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Transformers01:26

Transformers

1.1K
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...
1.1K
Equivalent Circuits for Practical Transformers01:28

Equivalent Circuits for Practical Transformers

494
The practical equivalent circuits of single-phase two-winding transformers exhibit significant deviations from their idealized versions due to the inherent properties of winding resistance and finite core permeability. These properties result in real and reactive power losses, affecting the transformer's performance. Understanding these deviations is crucial for designing more efficient transformers.
In a practical transformer, each winding exhibits resistance and leakage reactance. The...
494
Energy Losses in Transformers01:21

Energy Losses in Transformers

927
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...
927
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

189
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...
189
Bridge rectifier01:24

Bridge rectifier

734
The bridge rectifier is essential in electronics for efficiently converting alternating current (AC) to direct current (DC). Comprised of four diodes configured in a bridge layout, this rectifier effectively processes both the positive and negative halves of the AC waveform, making it superior to half-wave and full-wave center-tapped rectifiers in terms of voltage regulation and output stability.
Operationally, the bridge rectifier allows current flow through two of its diodes during each...
734
Types Of Transformers01:16

Types Of Transformers

1.0K
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...
1.0K

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Updated: Aug 9, 2025

Electric and Magnetic Field Devices for Stimulation of Biological Tissues
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On the effectiveness of compact biomedical transformers.

Omid Rohanian1,2, Mohammadmahdi Nouriborji2, Samaneh Kouchaki3

  • 1Department of Engineering Science, University of Oxford, Oxford, UK.

Bioinformatics (Oxford, England)
|February 24, 2023
PubMed
Summary

We developed six lightweight biomedical language models, achieving performance comparable to larger models like BioBERT. These efficient models are smaller and faster, making them more practical for various biomedical tasks.

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Area of Science:

  • Biomedical Natural Language Processing
  • Computational Linguistics
  • Machine Learning

Background:

  • Large pre-trained language models like BioBERT excel in biomedical tasks but are computationally intensive.
  • Model compression techniques (pruning, quantization, knowledge distillation) aim to reduce resource requirements.
  • There is a need for efficient, lightweight biomedical language models for practical applications.

Purpose of the Study:

  • Introduce six novel lightweight biomedical language models.
  • Evaluate their performance on downstream biomedical tasks.
  • Identify efficient models that match the performance of larger counterparts.

Main Methods:

  • Developed six lightweight models: BioDistilBERT, BioTinyBERT, BioMobileBERT, DistilBioBERT, TinyBioBERT, and CompactBioBERT.
  • Employed knowledge distillation from a biomedical teacher and continual pre-training on the PubMed dataset.
  • Compared model performance against BioBERT-v1.1 on three biomedical tasks.

Main Results:

  • Models range from 15 to 65 million parameters, significantly smaller than BioBERT's 110 million.
  • Distilled and PubMed-pre-trained models retained up to 98.8% and 98.6% of BioBERT-v1.1 performance, respectively.
  • BioMobileBERT (under 30M parameters) and DistilBioBERT/CompactBioBERT (over 30M parameters) showed superior performance, retaining up to 98.2% and 98.8%.

Conclusions:

  • Lightweight biomedical language models can achieve performance comparable to larger models.
  • Knowledge distillation and continual pre-training are effective strategies for creating efficient models.
  • The developed models offer practical, resource-efficient solutions for biomedical NLP tasks.