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Types Of Transformers

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...

Transformers

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...

Transformers with Off-Nominal Turns Ratios

Transformers with Off-Nominal Turns Ratios

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...

The Ideal Transformer

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...

Source Transformation

Source Transformation

Source transformation is a fundamental technique employed in circuit analysis, offering a valuable tool for simplifying complex electrical circuits. This technique involves the replacement of either a voltage source in series with a resistor by a current source in parallel with a resistor, or vice versa. The key concept here is that when the original sources are deactivated (turned off), the equivalent resistance at the circuit's end terminals remains the same.
It is essential to note that when...

Transformers in Distribution System

Transformers in Distribution System

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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Related Experiment Video

Updated: Jan 7, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Graph transformer for link prediction on N-ary facts.

Bin Hu¹, Xiongjie Tao², Hui Guo³

¹Faculty of Humanities and Arts, Macau University of Science and Technology, Taipa, 999078, Macau, China.

Scientific Reports

|December 20, 2025

Summary

This study introduces the N-ary graph Transformer (NAGT) model to improve N-ary Fact Link Prediction in hyper-relational knowledge graphs. NAGT enhances structural information utilization for more accurate association identification in recommendations.

Keywords:

Graph neural network Hyper-relational knowledge graph Link prediction N-ary fact knowledge graph

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Published on: April 21, 2023

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Last Updated: Jan 7, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

Area of Science:

Artificial Intelligence
Data Science
Graph Theory

Background:

Hyper-relational knowledge graphs extend traditional KGs with multi-dimensional auxiliary information, increasing representational complexity.
N-ary Fact Link Prediction faces challenges due to the complex and varied expression forms of N-ary facts compared to binary relations.

Purpose of the Study:

To address the insufficient utilization of heterogeneous graph structure information in existing N-ary fact representation methods.
To propose an N-ary graph Transformer (NAGT) model for enhanced N-ary Fact Link Prediction.

Main Methods:

Development of an N-ary graph Transformer (NAGT) model.
Incorporation of a novel attention mechanism based on N-ary structural bias.
Improving the representation of N-ary heterogeneous graphs.

Main Results:

The NAGT model demonstrates superior performance in extracting structural information compared to existing methods.
Experimental validation on JF17K, Wikipeople, and WD50K datasets confirms NAGT's effectiveness.
The model accurately identifies key associations in recommendation scenarios.

Conclusions:

The proposed NAGT model effectively completes knowledge graphs and shows efficiency and robustness in N-ary Fact Link Prediction tasks.
NAGT enhances the representation of N-ary heterogeneous graphs, leading to improved prediction accuracy.