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

Line Loss01:10

Line Loss

545
The different configurations of source-load connections include wye (star) and delta connections. The relationship between line and phase voltages and currents varies depending on the configuration. When the source is supplying power, it is transmitted through the wires to the load, and during this transmission, some power is absorbed by the wires, leading to line loss.
Line loss impacts power delivery efficiency in a balanced three-phase circuit. The symmetry in such a circuit simplifies the...
545
Reducing Line Loss01:18

Reducing Line Loss

390
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 in...
390
Energy Losses in Transformers01:21

Energy Losses in Transformers

1.3K
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...
1.3K
Major Losses in Pipes01:28

Major Losses in Pipes

2.0K
When a fluid flows through a pipe, it experiences energy losses due to frictional resistance along the pipe walls, known as major losses. These energy losses result in a pressure drop, which varies based on the flow conditions — whether laminar or turbulent — and the specific physical properties of the fluid and pipe.
Fluid flow can be classified as laminar or turbulent, primarily based on the Reynolds number. This dimensionless number reflects the relative influence of inertial to viscous...
2.0K
Minor Losses in Pipes01:25

Minor Losses in Pipes

2.0K
In pipe systems, minor losses refer to energy losses arising from components such as valves, bends, fittings, expansions, and other features that disrupt the steady flow of fluid. These disturbances cause energy dissipation through turbulence and resistance, which engineers quantify to manage system efficiency effectively.
Valves play a significant role in generating minor losses by obstructing or redirecting the fluid flow. When a valve is closed or partially closed, it restricts the flow...
2.0K
Adaptations that Reduce Water Loss01:57

Adaptations that Reduce Water Loss

28.2K
Though evaporation from plant leaves drives transpiration, it also results in loss of water. Because water is critical for photosynthetic reactions and other cellular processes, evolutionary pressures on plants in different environments have driven the acquisition of adaptations that reduce water loss.
28.2K

You might also read

Related Articles

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

Sort by
Same author

Exploring the causal effects of hazardous driving behaviors on pedestrian-vehicle crash injury severity based on causal machine learning.

International journal of injury control and safety promotion·2026
Same author

Novel recombinant protein PK5-Gal-3C enhances the anti-tumor activity of T cells via binding with TRPV2.

Cancer immunology, immunotherapy : CII·2026
Same author

Investigating determinants of injury severity in motorcycle-vehicle crashes using interpretable machine learning models.

Traffic injury prevention·2025
Same author

Molecular Dynamics Simulation Study on the Cooling Behavior and Mechanical Properties of Silicone Carbide/Aluminum Composites.

Materials (Basel, Switzerland)·2025
Same author

Bayesian networks for identifying causal effects of factors on crash injury severity at signalized intersections.

International journal of injury control and safety promotion·2025
Same author

Preparation and Characterization of Graphene-Nanosheet-Reinforced Ni-17Mo Alloy Composites for Advanced Nuclear Reactor Applications.

Materials (Basel, Switzerland)·2025
Same journal

Intervention Feasible Region and Driver Risk Capacity Aware Human-Machine Collaborative Safe Trajectory Planning.

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

A Unified Differential Denoising Learning Framework With a Pre-Trained Model and Fuzzy Graph Networks for Drug-Drug Interaction Prediction.

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

Self-Supervised Continuous Dynamic Graph Representation Learning via Hawkes Processes.

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

cPU: Consistent Risk Estimator for Positive-Unlabeled Learning.

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

Tuning-Free Latent Diffusion Models for Ultrahigh-Resolution Image Editing.

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

Hidden Data Recovery and Forecasting via Next-Generation Reservoir Computing With Multiscale Delay Selection.

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

Related Experiment Video

Updated: Feb 8, 2026

Fluorescent Labeling of Drosophila Heart Structures
11:12

Fluorescent Labeling of Drosophila Heart Structures

Published on: October 13, 2009

14.2K

Sequential Labeling With Structural SVM Under Nondecomposable Losses.

Guopeng Zhang, Massimo Piccardi, Ehsan Zare Borzeshi

    IEEE Transactions on Neural Networks and Learning Systems
    |July 11, 2018
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces new training algorithms for structural support vector machines (SSVMs) to improve sequential labeling tasks. The novel methods effectively minimize complex loss functions, enhancing classification accuracy in diverse applications.

    More Related Videos

    Specific Labeling of Mitochondrial Nucleoids for Time-lapse Structured Illumination Microscopy
    07:53

    Specific Labeling of Mitochondrial Nucleoids for Time-lapse Structured Illumination Microscopy

    Published on: June 4, 2020

    7.8K
    Covalent Labeling with Diethylpyrocarbonate for Studying Protein Higher-Order Structure by Mass Spectrometry
    10:36

    Covalent Labeling with Diethylpyrocarbonate for Studying Protein Higher-Order Structure by Mass Spectrometry

    Published on: June 15, 2021

    6.1K

    Related Experiment Videos

    Last Updated: Feb 8, 2026

    Fluorescent Labeling of Drosophila Heart Structures
    11:12

    Fluorescent Labeling of Drosophila Heart Structures

    Published on: October 13, 2009

    14.2K
    Specific Labeling of Mitochondrial Nucleoids for Time-lapse Structured Illumination Microscopy
    07:53

    Specific Labeling of Mitochondrial Nucleoids for Time-lapse Structured Illumination Microscopy

    Published on: June 4, 2020

    7.8K
    Covalent Labeling with Diethylpyrocarbonate for Studying Protein Higher-Order Structure by Mass Spectrometry
    10:36

    Covalent Labeling with Diethylpyrocarbonate for Studying Protein Higher-Order Structure by Mass Spectrometry

    Published on: June 15, 2021

    6.1K

    Area of Science:

    • Machine Learning
    • Computer Vision
    • Genomics
    • Data Science

    Background:

    • Sequential labeling is crucial for analyzing data in computer vision, finance, and genomics.
    • Hidden Markov Models (HMMs) are traditional but limited by decomposable loss functions.
    • Structural Support Vector Machines (SSVMs) offer higher accuracy but are restricted in loss function choices.

    Purpose of the Study:

    • To develop SSVM training algorithms capable of minimizing arbitrary loss functions based on classification contingency tables.
    • To specifically address and minimize Average Precision (AP) loss for improved sequential labeling performance.
    • To enhance the flexibility and effectiveness of SSVMs for sequential data analysis.

    Main Methods:

    • Proposed a novel training algorithm for SSVMs to handle any loss function derived from a classification contingency table.
    • Developed a specialized training algorithm focused on minimizing Average Precision (AP) loss.
    • Conducted experiments on diverse datasets: TUM Kitchen, CMU Multimodal Activity, and Ozone Level Detection.

    Main Results:

    • The proposed training algorithms demonstrated significant improvements in F1 measure and AP compared to conventional SSVM.
    • Achieved performance on par with or exceeding other state-of-the-art sequential labeling methods.
    • Validated the effectiveness of minimizing non-decomposable loss functions for sequential labeling.

    Conclusions:

    • The developed SSVM training algorithms offer a more effective approach to sequential labeling by accommodating advanced loss functions.
    • These methods provide significant performance gains, particularly in metrics like F1 and AP.
    • The research advances sequential data analysis capabilities in fields requiring precise classification.