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Related Concept Videos

Types Of Transformers01:16

Types Of Transformers

1.7K
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...
1.7K
The Ideal Transformer01:26

The Ideal Transformer

1.5K
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 tangential...
1.5K
Equivalent Circuits for Practical Transformers01:28

Equivalent Circuits for Practical Transformers

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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...
1.5K
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

660
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...
660
Three-Winding Transformers01:19

Three-Winding Transformers

885
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...
885
Source Transformation for AC Circuits01:11

Source Transformation for AC Circuits

1.2K
The process of source transformation in the frequency domain entails the conversion of a voltage source, positioned in series with an impedance, into a current source that is parallel to an impedance, or the other way around. It is essential to maintain the following relationships while transitioning from one source type to another.
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Updated: Mar 18, 2026

A Rapid Method for Modeling a Variable Cycle Engine
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AttentionSmithy: A Modular Framework for Rapid Transformer Development.

Caleb Cranney1, Jesse G Meyer1

  • 1Cedars Sinai Medical Center, Department of Computational Biomedicine.

Transactions on Machine Learning Research
|March 16, 2026
PubMed
Summary
This summary is machine-generated.

AttentionSmithy simplifies transformer development with modular components, enabling rapid prototyping and adaptation for AI experts. This software package accelerates innovation in diverse fields like NLP and genomics.

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computational Biology

Background:

  • Transformer architectures, powered by attention mechanisms, are key to AI.
  • Customizing transformers is complex for domain experts lacking low-level implementation knowledge.

Purpose of the Study:

  • Introduce AttentionSmithy, a modular software package to simplify transformer innovation.
  • Lower the barrier for domain experts to build and customize transformer models.

Main Methods:

  • Decompose transformers into reusable building blocks: attention modules, feed-forward networks, normalization, and positional encodings.
  • Provide modular integration for various positional encoding strategies (sinusoidal, learned, rotary, ALiBi) and attention methods (standard, Longformer, Linformer).
  • Integrate with neural architecture search (NAS) for automated design exploration.

Main Results:

  • Successfully replicated the original transformer model, showing robust performance on machine translation.
  • Identified an optimized model configuration via NAS that outperformed the baseline.
  • Achieved over 95% accuracy in cell type classification using gene-specific modeling with a BERT-style architecture.

Conclusions:

  • AttentionSmithy enables specialized experimentation across diverse domains (NLP, genomics) without complex low-level coding.
  • The framework facilitates rapid prototyping, adaptation, and evaluation of transformer variants.
  • AttentionSmithy is poised to accelerate research and development in scientific and industrial fields through creative transformer solutions.