Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Types Of Transformers01:16

Types Of Transformers

1.0K
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...
1.0K
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
The Ideal Transformer01:26

The Ideal Transformer

556
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...
556
Transformers in Distribution System01:27

Transformers in Distribution System

143
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...
143
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

196
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...
196
Reducing Line Loss01:18

Reducing Line Loss

188
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...
188

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Identification of G4-regulated immune-related drug targets for prostate cancer based on G4 screen and machine learning.

Frontiers in immunology·2026
Same author

GPNMB<sup>+</sup> macrophages promote osteogenic differentiation of nucleus pulposus cells through PDGF signaling in intervertebral disc degeneration.

Cell reports. Medicine·2026
Same author

[Ultrasound-synergized targeted nanoparticles suppress proliferation, migration and invasion of hypoxic lung cancer cells <i>in vitro</i>].

Nan fang yi ke da xue xue bao = Journal of Southern Medical University·2026
Same author

Forensics Adapter: Unleashing CLIP for Generalizable Face Forgery Detection.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Synergistic Effect of Gradient Conductivity and Gradient Microstructures Enabled Ultrasensitive and Ultrabroad Linear Flexible Tactile Sensors.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Bound polyphenols from carrot dietary fiber delay aging in <i>Caenorhabditis elegans</i> through the IIS pathway and metabolic remodeling.

Food & function·2026
Same journal

HardFlow: Hard-Constrained Sampling for Flow-Matching Models Via Trajectory Optimization.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Industrial Brain: Self-Evolving Neuro-Symbolic Autonomy with Causal Resilience for Cyber-Physical Systems.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Adaptive Hardness-Driven Dictionary Distillation for Incomplete Streaming View Clustering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Mixture of Global and Local Experts with Diffusion Transformer for Controllable Face Generation.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Task-KV: Task-aware KV Cache Optimization via Semantic Differentiation of Attention Heads.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Achieving Text-based Person Retrieval with Any Granularity.

IEEE transactions on pattern analysis and machine intelligence·2026
See all related articles

Related Experiment Video

Updated: Aug 28, 2025

A Human Cerebral Organoid Model of Neural Cell Transplantation
08:58

A Human Cerebral Organoid Model of Neural Cell Transplantation

Published on: July 21, 2023

1.3K

Transformer for Image Harmonization and Beyond.

Zonghui Guo, Zhaorui Gu, Bing Zheng

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |September 15, 2022
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces HarmonyTransformer, a novel approach using Transformer models for realistic image harmonization. It effectively adjusts foreground lighting to match backgrounds, achieving state-of-the-art results across multiple vision tasks.

    More Related Videos

    Analyzing Dendritic Morphology in Columns and Layers
    08:41

    Analyzing Dendritic Morphology in Columns and Layers

    Published on: March 23, 2017

    9.4K
    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

    Related Experiment Videos

    Last Updated: Aug 28, 2025

    A Human Cerebral Organoid Model of Neural Cell Transplantation
    08:58

    A Human Cerebral Organoid Model of Neural Cell Transplantation

    Published on: July 21, 2023

    1.3K
    Analyzing Dendritic Morphology in Columns and Layers
    08:41

    Analyzing Dendritic Morphology in Columns and Layers

    Published on: March 23, 2017

    9.4K
    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

    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Image Processing

    Background:

    • Image harmonization is crucial for realistic composite images but challenging due to differing lighting conditions.
    • Existing methods often use convolutional neural networks (CNNs) to capture image context.

    Purpose of the Study:

    • To develop a novel image harmonization method using Transformer models.
    • To leverage Transformer's ability to model long-range dependencies for light adjustment.

    Main Methods:

    • Proposed two vision Transformer frameworks for image harmonization.
    • Adjusted foreground light to match background light while preserving structure and semantics.

    Main Results:

    • Achieved state-of-the-art performance on image harmonization.
    • Demonstrated effectiveness on additional tasks: image enhancement, inpainting, white-balance editing, and portrait relighting.

    Conclusions:

    • Transformer models offer a powerful alternative for image harmonization.
    • The proposed methods show superiority and versatility across various vision and graphics tasks.