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Related Concept Videos

Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

149
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...
149
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
Types Of Transformers01:16

Types Of Transformers

959
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...
959
Equivalent Circuits for Practical Transformers01:28

Equivalent Circuits for Practical Transformers

406
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...
406
Three-Winding Transformers01:19

Three-Winding Transformers

217
Three identical single-phase transformers can be configured to form a three-phase transformer connection, which involves high-voltage and low-voltage windings. The high-voltage windings are denoted by capital letters A-B-C, while the low-voltage windings are labeled with lowercase letters a-b-c, representing their respective phases. This notation helps distinguish between the high and low voltage sides of the transformer.
In the per-unit equivalent circuit of a grounded Y-Y three-phase...
217
Energy Losses in Transformers01:21

Energy Losses in Transformers

852
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...
852

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Related Experiment Video

Updated: Jun 18, 2025

Transduction-Transplantation Mouse Model of Myeloproliferative Neoplasm
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Transformer models in biomedicine.

Sumit Madan1,2, Manuel Lentzen3,4, Johannes Brandt5

  • 1Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, 53757, Germany. sumit.madan@scai.fraunhofer.de.

BMC Medical Informatics and Decision Making
|July 29, 2024
PubMed
Summary

Transformer models, a type of deep neural network, are increasingly used in AI for analyzing diverse biomedical data. This review covers their applications, explainability, and future research directions in the field.

Keywords:
BiomedicineDeep learningLife ScienceMachine learningNeural networksTransformer

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Area of Science:

  • Artificial Intelligence
  • Bioinformatics
  • Computational Biology

Background:

  • Deep neural networks (DNNs) have transformed artificial intelligence (AI).
  • Transformer models, initially for natural language processing, now excel with sequential data like biological sequences and electronic health records.
  • Biomedical transformer models (e.g., BioBERT, MedBERT) are emerging for scientific inquiry.

Purpose of the Study:

  • To review the development and applications of transformer models in analyzing biomedical data.
  • To explore explainable AI (XAI) strategies for transformer model interpretability.
  • To discuss current limitations and future research avenues for transformer models in biomedicine.

Main Methods:

  • Review of transformer model architectures and their adaptations for biomedical data.
  • Analysis of applications across diverse biomedical datasets: text, protein sequences, EHRs, images, and graphs.
  • Examination of explainable AI techniques applied to transformer-based biomedical models.

Main Results:

  • Transformers demonstrate significant utility in processing various biomedical data types.
  • Specific models like BioBERT and MedBERT show promise in biomedical question answering and analysis.
  • Explainable AI methods are crucial for understanding and trusting these complex models.

Conclusions:

  • Transformer models are powerful tools for advancing biomedical research.
  • Further development is needed to address limitations and enhance model interpretability.
  • Future directions include novel applications and improved explainability techniques for AI in biomedicine.