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

Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

487
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
487
Graphical Representation of Inequalities01:28

Graphical Representation of Inequalities

177
The graph of the equation where y equals x squared forms a curve known as a parabola. This curve acts as a boundary in the coordinate plane, dividing it into distinct regions based on the relative position of points.When the equality sign in the equation is replaced with an inequality—such as greater than, less than, greater than or equal to, or less than or equal to—the graphical representation changes from a single curve into a broader shaded area that signifies the set of all...
177
Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

16.9K
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...
16.9K
Constraints and Statical Determinacy01:26

Constraints and Statical Determinacy

949
In structural engineering, the equilibrium of a system is not only determined by its equations of equilibrium but also with the help of constraints. Constraints refer to restrictions on the motion of a system. The proper combinations of constraints can minimize the total number of constraints needed to maintain a system in mechanical equilibrium. When this happens, the system is said to be statically determinate. For such systems, the unknown reaction supports can be estimated using equilibrium...
949
Structural Classification of Joints01:20

Structural Classification of Joints

7.0K
Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
7.0K
Mesh Analysis01:20

Mesh Analysis

1.4K
Mesh analysis is a valuable method for simplifying circuit analysis using mesh currents as key circuit variables. Unlike nodal analysis, which focuses on determining unknown voltages, mesh analysis applies Kirchhoff's voltage law (KVL) to find unknown currents within a circuit. This method is particularly convenient in reducing the number of simultaneous equations that need to be solved.
A fundamental concept in mesh analysis is the definition of meshes and mesh currents. A mesh is a closed...
1.4K

You might also read

Related Articles

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

Sort by
Same author

Faster quantum subroutine for matrix chain multiplication via Chebyshev approximation.

Scientific reports·2025
Same author

A cutting-edge neural network approach for predicting the thermoelectric efficiency of defective gamma-graphyne nanoribbons.

Scientific reports·2025
Same author

Structural Characterization and Antioxidant Activity of β-Glucans from Highland Barley Obtained with Ultrasonic-Microwave-Assisted Extraction.

Molecules (Basel, Switzerland)·2024
Same author

DrPOCS: Drug Repositioning Based on Projection Onto Convex Sets.

IEEE/ACM transactions on computational biology and bioinformatics·2018
Same author

[Sampling survey of schistosomiasis prevention knowledge among middle school students in endemic areas of Hubei Province].

Zhongguo xue xi chong bing fang zhi za zhi = Chinese journal of schistosomiasis control·2014
Same author

Quantitative proteomics analysis by iTRAQ in human nuclear cataracts of different ages and normal lens nuclei.

Proteomics. Clinical applications·2014
Same journal

An Evolutionary Algorithm Assisted by an Ensemble of Pareto-Optimal Surrogate Models.

IEEE transactions on cybernetics·2026
Same journal

A Quantum Self-Attention Neural Network Model on Quantum Circuits.

IEEE transactions on cybernetics·2026
Same journal

Semi-Explicit Solution of Some Discrete-Time Higher-Order-Cost Mean-Field-Type Control.

IEEE transactions on cybernetics·2026
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
See all related articles

Related Experiment Video

Updated: Jan 19, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.5K

Elastic Net Constraint-Based Tensor Model for High-Order Graph Matching.

Hu Zhu, Chunfeng Cui, Lizhen Deng

    IEEE Transactions on Cybernetics
    |September 20, 2019
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel elastic net constraint tensor model for high-order graph matching in computer vision. The method balances sparsity and accuracy, demonstrating superior performance on synthetic and natural image data.

    More Related Videos

    Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
    09:44

    Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

    Published on: March 8, 2024

    5.8K
    A Protocol for Computer-Based Protein Structure and Function Prediction
    16:41

    A Protocol for Computer-Based Protein Structure and Function Prediction

    Published on: November 3, 2011

    69.7K

    Related Experiment Videos

    Last Updated: Jan 19, 2026

    Modeling the Functional Network for Spatial Navigation in the Human Brain
    05:55

    Modeling the Functional Network for Spatial Navigation in the Human Brain

    Published on: October 13, 2023

    1.5K
    Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
    09:44

    Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

    Published on: March 8, 2024

    5.8K
    A Protocol for Computer-Based Protein Structure and Function Prediction
    16:41

    A Protocol for Computer-Based Protein Structure and Function Prediction

    Published on: November 3, 2011

    69.7K

    Area of Science:

    • Computer Vision
    • Machine Learning
    • Optimization

    Background:

    • Establishing correspondences between feature points is crucial for computer vision tasks.
    • Existing graph matching methods face challenges in balancing result sparsity and accuracy.

    Purpose of the Study:

    • To propose a novel elastic net constraint-based tensor model for high-order graph matching.
    • To enhance the trade-off between sparsity and accuracy in matching results.

    Main Methods:

    • An elastic net constraint was integrated into a tensor-based graph matching model.
    • A nonmonotone spectral projected gradient (NSPG) method was developed for optimization.
    • A projection algorithm for elastic net constraints was proposed.

    Main Results:

    • The proposed model effectively controls the trade-off between sparsity and accuracy.
    • The NSPG method demonstrated global convergence for solving the matching model.
    • Experimental results on synthetic and natural images validated the method's superiority.

    Conclusions:

    • The elastic net constraint-based tensor model offers an effective solution for high-order graph matching.
    • The NSPG optimization method provides a robust and convergent approach.
    • The proposed method advances computer vision applications requiring accurate feature point correspondence.