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

Time-Series Graph00:54

Time-Series Graph

3.8K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
3.8K
Transformation of Plane Strain01:12

Transformation of Plane Strain

673
When analyzing elongated structures like bars subjected to uniformly distributed loads, it is essential to understand the transformation of plane strain when coordinate axes are rotated. This transformation helps to assess how material deformation characteristics vary with orientation, which is crucial in materials science and structural engineering.
Under plane strain conditions, typical for members where one dimension significantly exceeds the others, deformations and resultant strains are...
673
Structural Classification of Joints01:20

Structural Classification of Joints

8.0K
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...
8.0K
Curvilinear Motion: Rectangular Components01:23

Curvilinear Motion: Rectangular Components

1.6K
Curvilinear motion characterizes the movement of a particle or object along a curved path, notably evident when envisioning a car navigating a winding road. If the car starts at point A, its position vector is established within a fixed frame of reference, where the ratio of the position vector to its magnitude signifies the unit vector pointing in the position vector's direction.
As the car advances, its position evolves over time. Quantifying the car's velocity involves computing the...
1.6K
Absolute Motion Analysis- General Plane Motion01:24

Absolute Motion Analysis- General Plane Motion

751
Visualize a drone, with its propellers spinning rapidly, hovering mid-air. The fascinating movements and operations of this drone can be comprehended by applying the principle of general plane motion.
As the drone's propellers rotate, an upward force is generated that counteracts the force of gravity, enabling the drone to lift off from the ground. This initial movement of the drone is along a straight path, representing a form of translational motion. In this phase, every point on the...
751
Hyperbolas01:30

Hyperbolas

661
A hyperbola is a conic section produced when a double-napped cone is intersected by a plane at an angle steeper than the slope of the cone, such that it cuts through both nappes. This intersection yields two separate, mirror-image curves known as branches, which open away from each other along the transverse axis. The nearest points on each branch to the hyperbola’s center are termed vertices, and the distance from the center to a vertex is denoted by a. Perpendicular to the transverse...
661

You might also read

Related Articles

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

Sort by
Same author

Kernel PCA for out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation.

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

Hull-Less Barley (<i>Hordeum vulgare</i> L. var. <i>nudum</i> Hook. f.): A Review of Its Phytochemistry, Bioactivities, Pharmacology and Applications.

Journal of agricultural and food chemistry·2026
Same author

Multi-emission metal-organic frameworks for ratiometric detection of tetracycline antibiotics.

Food chemistry·2026
Same author

Click chemistry driven aggregate reaction dramatically enhances the sensitivity of dynamic light scattering immunosensor.

Biosensors & bioelectronics·2026
Same author

One hotspot <i>RB1</i> mutation disrupt <i>RB1</i> function founded in a Chinese patient.

Frontiers in oncology·2026
Same author

Unraveling the dynamic flavor profile of Tongchuan Douchi: an integrated multi-omics and flavor characterization.

Food research international (Ottawa, Ont.)·2026

Related Experiment Video

Updated: Apr 30, 2026

Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

335

Hinging hyperplanes for time-series segmentation.

Xiaolin Huang, Marin Matijas, Johan A K Suykens

    IEEE Transactions on Neural Networks and Learning Systems
    |May 9, 2014
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel regression-based time series segmentation method using hinging hyperplanes. It improves accuracy for noisy data, outperforming traditional interpolation techniques.

    More Related Videos

    Acquiring Fluorescence Time-lapse Movies of Budding Yeast and Analyzing Single-cell Dynamics using GRAFTS
    17:01

    Acquiring Fluorescence Time-lapse Movies of Budding Yeast and Analyzing Single-cell Dynamics using GRAFTS

    Published on: July 18, 2013

    12.4K
    Three and Four-Dimensional Visualization and Analysis Approaches to Study Vertebrate Axial Elongation and Segmentation
    12:59

    Three and Four-Dimensional Visualization and Analysis Approaches to Study Vertebrate Axial Elongation and Segmentation

    Published on: February 28, 2021

    3.4K

    Related Experiment Videos

    Last Updated: Apr 30, 2026

    Automated Joint Space Detection Improves Bone Segmentation Accuracy
    06:45

    Automated Joint Space Detection Improves Bone Segmentation Accuracy

    Published on: November 28, 2025

    335
    Acquiring Fluorescence Time-lapse Movies of Budding Yeast and Analyzing Single-cell Dynamics using GRAFTS
    17:01

    Acquiring Fluorescence Time-lapse Movies of Budding Yeast and Analyzing Single-cell Dynamics using GRAFTS

    Published on: July 18, 2013

    12.4K
    Three and Four-Dimensional Visualization and Analysis Approaches to Study Vertebrate Axial Elongation and Segmentation
    12:59

    Three and Four-Dimensional Visualization and Analysis Approaches to Study Vertebrate Axial Elongation and Segmentation

    Published on: February 28, 2021

    3.4K

    Area of Science:

    • Data Science
    • Machine Learning
    • Signal Processing

    Background:

    • Time series segmentation is crucial for data analysis.
    • Traditional linear interpolation methods are sensitive to noise.
    • Existing methods struggle with noisy time-series data.

    Purpose of the Study:

    • To develop an advanced time series segmentation technique.
    • To address limitations of interpolation-based methods in noisy environments.
    • To enable the use of regression for continuous signal reconstruction in segmentation.

    Main Methods:

    • Utilized hinging hyperplanes for a compact representation of piecewise linear functions.
    • Developed an explicit expression for segmentation enabling regression.
    • Implemented a least squares support vector machine with lasso and a hinging feature map.
    • Established a novel segmentation algorithm and its online version.

    Main Results:

    • The proposed method achieves continuous signal reconstruction.
    • Demonstrated superior performance on both synthetic and real-world datasets.
    • Outperformed existing time series segmentation algorithms in numerical experiments.

    Conclusions:

    • The novel regression-based approach offers significant advantages for time series segmentation, especially with noisy data.
    • The use of hinging hyperplanes and advanced machine learning techniques provides a robust alternative to traditional methods.
    • The developed algorithm and its online version are effective for practical applications.