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

1.6K
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...
1.6K
Reducing Line Loss01:18

Reducing Line Loss

524
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...
524
The Ideal Transformer01:26

The Ideal Transformer

1.3K
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...
1.3K
Equivalent Circuits for Practical Transformers01:28

Equivalent Circuits for Practical Transformers

1.4K
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...
1.4K
Three-Winding Transformers01:19

Three-Winding Transformers

1.0K
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...
1.0K
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

739
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...
739

You might also read

Related Articles

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

Sort by
Same author

Raman evidence for a solvation-heterogeneity-mediated precipitation pathway of sodium sulfate in water-DMSO mixtures.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy·2026
Same author

Identification of targets of clinical FOXA1 mutants involved in EMT and immune suppression in prostate cancer.

Communications biology·2026
Same author

Liver transcriptome analysis revealed multiple immune processes and lipid metabolism pathways involved in the defense response of the turbot (Scophthalmus maximus) against Aeromonas salmonicida.

Comparative biochemistry and physiology. Part D, Genomics & proteomics·2026
Same author

Reinforcement learning in linear embedding space unlocks generalizable control across soft robot configurations.

Nature communications·2026
Same author

RBM10 suppresses porcine epidemic diarrhea virus replication by degrading nonstructural protein 3 through selective autophagy.

Veterinary microbiology·2026
Same author

Norepinephrine promotes the proliferation, migration, and phenotypic transformation of renal artery vascular smooth muscle cells via ROCK1.

Iranian journal of basic medical sciences·2026
Same journal

A Survey on Human-Centric Voice-Face Multimodal Learning.

IEEE transactions on neural networks and learning systems·2026
Same journal

Vision-Assisted Foundation Model for Solving Multitask Vehicle Routing Problems.

IEEE transactions on neural networks and learning systems·2026
Same journal

FP3O: Enabling Proximal Policy Optimization in Multiagent Cooperation With Parameter-Sharing Versatility.

IEEE transactions on neural networks and learning systems·2026
Same journal

Hierarchical Semantic Concept Modeling for Generalizable Myocardial Pathology Segmentation on Multisequence CMR Images.

IEEE transactions on neural networks and learning systems·2026
Same journal

Stability of Time-Varying Impulsive Systems With State-Dependent Delay and Its Application in Complex Networks.

IEEE transactions on neural networks and learning systems·2026
Same journal

Adaptive Learning Control of Uncertain Systems via Weight and Intrinsic Plasticity-Based Neural Networks.

IEEE transactions on neural networks and learning systems·2026
See all related articles

Related Experiment Video

Updated: May 5, 2026

Characterization of Anisotropic Leaky Mode Modulators for Holovideo
09:36

Characterization of Anisotropic Leaky Mode Modulators for Holovideo

Published on: March 19, 2016

7.9K

Modumer: Modulating Transformer for Image Restoration.

Yuning Cui, Mingyu Liu, Wenqi Ren

    IEEE Transactions on Neural Networks and Learning Systems
    |May 1, 2025
    PubMed
    Summary
    This summary is machine-generated.

    Modumer enhances image restoration by improving Transformer efficiency and dependency capture. This novel approach achieves state-of-the-art results in various single and complex image degradation tasks.

    More Related Videos

    Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques
    09:01

    Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques

    Published on: April 4, 2017

    8.6K
    Preparing an Isotopically Pure 229Th Ion Beam for Studies of 229mTh
    10:42

    Preparing an Isotopically Pure 229Th Ion Beam for Studies of 229mTh

    Published on: May 3, 2019

    6.6K

    Related Experiment Videos

    Last Updated: May 5, 2026

    Characterization of Anisotropic Leaky Mode Modulators for Holovideo
    09:36

    Characterization of Anisotropic Leaky Mode Modulators for Holovideo

    Published on: March 19, 2016

    7.9K
    Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques
    09:01

    Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques

    Published on: April 4, 2017

    8.6K
    Preparing an Isotopically Pure 229Th Ion Beam for Studies of 229mTh
    10:42

    Preparing an Isotopically Pure 229Th Ion Beam for Studies of 229mTh

    Published on: May 3, 2019

    6.6K

    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Image Processing

    Background:

    • Transformer-based methods excel in image restoration but suffer from high complexity and limited long-range dependency capture.
    • Existing models struggle with omni-range feature dependencies, impacting overall restoration performance.

    Purpose of the Study:

    • To develop an effective and efficient image restoration model named Modumer.
    • To address the limitations of Transformer architectures in capturing omni-range dependencies for improved image restoration.

    Main Methods:

    • Introduced Modumer, a novel architecture revisiting Transformer blocks and modulation design.
    • Integrated cascaded modulation with downsampled Transformer blocks for attention layers, enabling omni-kernel modulation.
    • Incorporated a bio-inspired parameter-sharing mechanism and a dual-domain feed-forward network (DFFN) for enhanced efficiency and representation.

    Main Results:

    • Achieved state-of-the-art performance on ten datasets across five single-degradation tasks (deblurring, deraining, dehazing, desnowing, low-light enhancement).
    • Demonstrated strong generalization capabilities in all-in-one image restoration.
    • Showcased competitive performance in composite-degradation image restoration tasks.

    Conclusions:

    • Modumer offers a significant advancement in effective and efficient image restoration.
    • The proposed architecture overcomes limitations of previous Transformer-based methods, improving performance and generalization.