Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

162
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...
162
Types Of Transformers01:16

Types Of Transformers

987
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...
987
Three-Winding Transformers01:19

Three-Winding Transformers

237
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...
237
The Ideal Transformer01:26

The Ideal Transformer

407
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...
407
Energy Losses in Transformers01:21

Energy Losses in Transformers

883
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...
883
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

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Covalent immobilization of anthocyanins onto cellulose nanofibrils for intelligent food packaging films.

Food chemistry: X·2026
Same author

Boosting O<sub>2</sub> activation and substrate adsorption over single-atom Zr-doped Pt/CeO<sub>2</sub> for enhanced glucose oxidation to glucaric acid.

Bioresource technology·2026
Same author

Number Needed to Treat with Biologics in Type-2 Inflammation COPD: A Systematic Review and Meta-Analysis.

COPD·2026
Same author

Notch Signaling Regulates the Neuroprotective Effects of hUCMSCs in a Mouse Model of Cerebral Ischemia.

Journal of molecular neuroscience : MN·2026
Same author

Elimination of detrimental grain boundary segregation in Garnets.

Nature communications·2026
Same author

Corrigendum to "The protective effect of blueberry anthocyanins on the intestinal barrier in pup and adult mice via the TLR4 signaling pathway" [Phytomedicine 155 (2026) 158076].

Phytomedicine : international journal of phytotherapy and phytopharmacology·2026

相关实验视频

Updated: Jul 11, 2025

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

1.9K

MR变压器:用于多变量时间序列预测的多分辨率变压器.

Siying Zhu, Jiawei Zheng, Qianli Ma

    IEEE transactions on neural networks and learning systems
    |November 6, 2023
    PubMed
    概括

    本研究介绍了用于多变量时间序列 (MTS) 预测的多分辨率变压器 (MR-Transformer). 这种新型模型有效地捕捉了跨多个变量的短期和长期模式,大大提高了预测准确性.

    科学领域:

    • 机器学习 机器学习
    • 时间序列分析时间序列分析
    • 深度学习 (Deep Learning) 是一种深度学习.

    背景情况:

    • 多变量时间序列 (MTS) 预测对于现实世界的应用至关重要.
    • 基于变压器的方法有希望,但往往忽视短期时间动态.
    • 现有的模型可能无法完全捕捉多个变量的一致和特定特征.

    研究的目的:

    • 提出一种新的多分辨率变压器 (MR-Transformer),用于增强MTS预测.
    • 从时间分辨率和可变分辨率有效建模MTS.
    • 改进短期模式的提取,并考虑变量之间的关系.

    主要方法:

    • 引入了一个长期短期变压器,以捕捉短期 (在分段内) 和长期 (固有的注意力) 时间模式.
    • 开发了一个时间卷积模块,以单独捕捉每个变量的特定特征.
    • 跨时间步骤和变量集成的多解析度功能,用于全面的MTS建模.

    主要成果:

    • 拟议的MR-Transformer在现实数据集上显著优于现有的最先进的MTS预测模型.
    • 证明模型能够有效地捕捉时间依赖性和变量特定特征.
    • 可视化分析证实了多分辨率方法在MTS预测中的有效性.

    更多相关视频

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    435
    Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
    07:12

    Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

    Published on: July 1, 2014

    12.3K

    相关实验视频

    Last Updated: Jul 11, 2025

    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

    1.9K
    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    435
    Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
    07:12

    Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

    Published on: July 1, 2014

    12.3K

    结论:

    • 通过整合多分辨率分析,MR-Transformer提供了一种优越的MTS预测方法.
    • 该模型的架构成功地解决了捕捉短期动态和可变相互依存的先前方法的局限性.
    • 这些发现强调了考虑时间和可变分辨率对于准确的MTS预测的重要性.