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Related Concept Videos

Types Of Transformers01:16

Types Of Transformers

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...
Transformers01:26

Transformers

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...
Instrument Transformers01:23

Instrument Transformers

Instrument transformers, comprising voltage transformers (VTs) and current transformers (CTs), play crucial roles in power substations by providing isolated replicas of current or voltage for measurement and protection purposes. Voltage transformers reduce the primary voltage to levels suitable for relay operation and measurement, while current transformers scale down the primary current. The primary winding of a current transformer often consists of a single turn, achieved by threading the...
The Ideal Transformer01:26

The Ideal Transformer

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 component...
Reducing Line Loss01:18

Reducing Line Loss

In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss in...
Equivalent Circuits for Practical Transformers01:28

Equivalent Circuits for Practical Transformers

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 winding...

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Related Experiment Video

Updated: Jul 17, 2026

All-electronic Nanosecond-resolved Scanning Tunneling Microscopy: Facilitating the Investigation of Single Dopant Charge Dynamics
11:33

All-electronic Nanosecond-resolved Scanning Tunneling Microscopy: Facilitating the Investigation of Single Dopant Charge Dynamics

Published on: January 19, 2018

Time-Local Transformer.

Billy Dickson1, James Mochizuki-Freeman1, Md Rysul Kabir1

  • 1Department of Computer Science, Indiana University Bloomington, Bloomington, Indiana USA.

Computational Brain & Behavior
|July 16, 2026
PubMed
Summary

We developed a time-local transformer by integrating working memory into AI models. This biologically-inspired approach processes sequential data effectively, mimicking human language capabilities without needing full input history.

Keywords:
Language processingLong-range dependenciesNeural plausibilitySequence modelingTransformer modelsWorking memory

Related Experiment Videos

Last Updated: Jul 17, 2026

All-electronic Nanosecond-resolved Scanning Tunneling Microscopy: Facilitating the Investigation of Single Dopant Charge Dynamics
11:33

All-electronic Nanosecond-resolved Scanning Tunneling Microscopy: Facilitating the Investigation of Single Dopant Charge Dynamics

Published on: January 19, 2018

Area of Science:

  • Cognitive Science
  • Artificial Intelligence
  • Computational Neuroscience

Background:

  • Human language processing captures long-range dependencies within limited working memory.
  • Current AI transformer models use fixed context windows, unlike dynamic human cognition.
  • A gap exists between AI sequence processing and human cognitive constraints.

Purpose of the Study:

  • To reconcile the disparity between human working memory and AI transformer architectures.
  • To develop a biologically-inspired AI model for human-like language processing.
  • To investigate AI's capacity for learning complex dependencies with limited context.

Main Methods:

  • Integrating a computational model of working memory into the transformer architecture.
  • Developing a novel time-local transformer model.
  • Evaluating the model's ability to learn dependencies without full input history.

Main Results:

  • The time-local transformer effectively processes sequential inputs.
  • The model learns complex dependencies without requiring the entire input history.
  • Transformer capacity for sequence processing is preserved.

Conclusions:

  • The proposed time-local transformer aligns AI with human cognitive principles.
  • This biologically-inspired AI advances understanding of neural language processing.
  • Opens new research avenues for brain-inspired AI and cognition.