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関連する概念動画

Types Of Transformers01:16

Types Of Transformers

1.4K
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...
1.4K
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

511
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...
511
Transformers01:26

Transformers

1.7K
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...
1.7K
Transformers in Distribution System01:27

Transformers in Distribution System

491
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...
491
The Ideal Transformer01:26

The Ideal Transformer

1.4K
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...
1.4K
Energy Losses in Transformers01:21

Energy Losses in Transformers

1.3K
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...
1.3K

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関連する実験動画

Updated: Jan 14, 2026

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

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

Published on: April 21, 2023

2.3K

グラフ・トランスフォーマー：サーベイ

Ahsan Shehzad, Feng Xia, Shagufta Abid

    IEEE transactions on neural networks and learning systems
    |January 12, 2026
    PubMed
    まとめ
    この要約は機械生成です。

    グラフ・トランスフォーマーは、グラフ学習とトランスフォーマーモデルを組み合わせて、グラフデータに対して強力なパフォーマンスを発揮する。本サーベイでは、機械学習における進捗、設計、応用、課題をレビューする。

    キーワード:
    グラフ・トランスフォーマーグラフニューラルネットワークトランスフォーマー機械学習サーベイ

    関連する実験動画

    Last Updated: Jan 14, 2026

    A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
    04:23

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

    Published on: April 21, 2023

    2.3K

    科学分野:

    • 機械学習
    • 人工知能
    • グラフ理論

    背景:

    • グラフ構造データは多くのドメインで普及しています。
    • 従来のモデルは複雑なグラフ関係に対処するのに苦労しています。
    • トランスフォーマーはシーケンスモデリングに優れていますが、グラフへの適応が必要です。

    研究 の 目的:

    • グラフ・トランスフォーマーの包括的なレビューを提供すること。
    • グラフ特徴の設計原則と統合を分析すること。
    • 既存のグラフ・トランスフォーマーモデルを分類し、将来の研究の方向性を特定すること。

    主な方法:

    • グラフ学習とトランスフォーマーの基礎概念のレビュー。
    • グラフの帰納的バイアスと注意機構を統合したアーキテクチャ設計の分析。
    • グラフ・トランスフォーマーを分類するための分類法の開発。
    • 応用と課題の議論。

    主要な成果:

    • グラフ・トランスフォーマーは、ノード、エッジ、グラフレベルのタスク全体で強力なパフォーマンスを示します。
    • 主な設計上の考慮事項には、帰納的バイアスと注意機構が含まれます。
    • 深さ、スケーラビリティ、事前学習に基づいた分類法が提案されています。
    • スケーラビリティ、堅牢性、解釈可能性の課題が特定されています。

    結論:

    • グラフ・トランスフォーマーは、グラフデータのための機械学習において大きな進歩を表します。
    • スケーラビリティ、一般化、解釈可能性の課題に対処するには、さらなる研究が必要です。
    • この分野は、多様な応用において大きな可能性を秘めています。