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

951
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...
951
Transformers in Distribution System01:27

Transformers in Distribution System

99
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...
99
Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

11.9K
Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
11.9K
Energy Losses in Transformers01:21

Energy Losses in Transformers

839
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...
839
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
Equivalent Circuits for Practical Transformers01:28

Equivalent Circuits for Practical Transformers

398
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...
398

You might also read

Related Articles

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

Sort by
Same author

DisenTS: Disentangled Channel Evolving Pattern Modeling for Multivariate Time Series Forecasting.

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

ActionX: pre-training action experts with reinforcement learning for vision-language action models.

Frontiers in neurorobotics·2026
Same author

Rein++: Efficient Generalization and Adaptation for Semantic Segmentation with Vision Foundation Models.

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

Survey of Computerized Adaptive Testing: a Machine Learning Perspective.

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

Learning Deep Tree-Based Retriever for Efficient Recommendation: Theory and Method.

IEEE transactions on pattern analysis and machine intelligence·2025
Same author

Learning Deep ISP for High-Speed Cameras: Achieving DSLR-Quality Imaging Under High Frame Rates.

IEEE transactions on pattern analysis and machine intelligence·2025
Same journal

A Novel One-Step Small Object Detector for Autonomous Aerial Vehicles.

IEEE transactions on cybernetics·2026
Same journal

Online Data-Driven-Based Optimal Output Tracking Control Without Initial Stabilizing Policy.

IEEE transactions on cybernetics·2026
Same journal

Digital Redesign-Based Interval State Estimation for Continuous Systems With Aperiodic Discrete Measurements.

IEEE transactions on cybernetics·2026
Same journal

Output Prediction-Based Event-Triggered Interval Estimation for Continuous-Time Switched Systems.

IEEE transactions on cybernetics·2026
Same journal

Differentially Private Distributed Algorithms for Aggregative Games Over Directed Graphs With Linear Convergence.

IEEE transactions on cybernetics·2026
Same journal

Communication Delay-Based Under-Actuated MASVs Distributed Formation Tracking Control With Unknown Ocean Disturbances and Input Quantization.

IEEE transactions on cybernetics·2026
See all related articles
  1. Home
  3. Grakerformer: A Transformer With Graph Kernel For Unsupervised Graph Representation Learning.
  1. Home
  3. Grakerformer: A Transformer With Graph Kernel For Unsupervised Graph Representation Learning.

Related Experiment Video

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

1.8K

GraKerformer: A Transformer With Graph Kernel for Unsupervised Graph Representation Learning.

Lixiang Xu, Haifeng Liu, Xin Yuan

    IEEE Transactions on Cybernetics
    |October 8, 2024

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    GraKerformer enhances unsupervised graph representation learning by integrating graph neural networks and shortest-path graph kernels into the Transformer architecture. This approach captures comprehensive graph structures, improving performance over traditional methods.

    More Related Videos

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

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    376
    Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
    08:51

    Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

    Published on: November 1, 2019

    5.6K

    Related Experiment Videos

    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

    1.8K
    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    376
    Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
    08:51

    Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

    Published on: November 1, 2019

    5.6K

    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Graph Representation Learning

    Background:

    • Transformer models excel in NLP but struggle with unsupervised graph representation learning (UGRL).
    • Existing UGRL methods often focus on local graph structures, limiting their ability to capture global information and leading to poor generalization.
    • A need exists for advanced models that can effectively represent complex graph structures.

    Purpose of the Study:

    • To introduce GraKerformer, a novel Transformer-based model designed to improve unsupervised graph representation learning.
    • To address the limitations of conventional UGRL approaches in capturing comprehensive graph structural information.
    • To enhance the performance and generalization capabilities of models in UGRL tasks.

    Main Methods:

    • Proposed GraKerformer, a modified Transformer architecture incorporating graph neural networks.
    • Utilized the shortest-path graph kernel (SPGK) to weight attention scores within the Transformer.
    • Integrated SPGK with graph neural networks to encode nuanced graph structural information.

    Main Results:

    • GraKerformer demonstrated superior performance in unsupervised graph representation learning tasks.
    • The model effectively captured comprehensive structural information of graphs, overcoming limitations of local-structure focused methods.
    • Evaluations on benchmark graph classification datasets validated the enhanced performance.

    Conclusions:

    • GraKerformer offers a significant advancement in unsupervised graph representation learning.
    • The integration of SPGK and GNNs within a Transformer framework effectively encodes complex graph structures.
    • The proposed model achieves state-of-the-art performance, paving the way for more robust graph-based deep learning applications.