Jove
Visualize
Contact Us

Related Concept Videos

Reducing Line Loss01:18

Reducing Line Loss

151
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...
151
Energy Losses in Transformers01:21

Energy Losses in Transformers

864
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...
864
Generator Voltage Control01:21

Generator Voltage Control

147
Generator voltage control is crucial for maintaining the stable operation of synchronous generators and wind turbines. In older models, a DC generator driven by the rotor delivers DC power to the rotor's field winding, and the power is transferred through slip rings and brushes. In the latest models, static or brushless exciters are used. Static exciters rectify AC power from the generator terminals and then transfer the DC power directly to the rotor. Brushless exciters, on the other hand,...
147
Lossy Lines and Overvoltages01:22

Lossy Lines and Overvoltages

88
Transmission-line series resistance and shunt conductance cause three primary effects: attenuation, distortion, and power losses.
Attenuation
When constant series resistance and shunt conductance are present, voltage and current equations are modified. The propagation constant indicates that voltage and current waves consist of both forward and backward traveling components. These waves attenuate as they propagate, with the attenuation factor related to the resistance and conductance. In a...
88
Line Loss01:10

Line Loss

245
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...
245
Boundary Conditions: Lossless Lines01:21

Boundary Conditions: Lossless Lines

92
Consider a single-phase, two-wire, lossless transmission line terminated by an impedance at the receiving end and a source with Thevenin voltage and impedance at the sending end. The line, with length, has a surge impedance and wave velocity determined by the line's inductance and capacitance.
At the receiving end, the boundary condition states that the voltage equals the product of the receiving-end impedance and current. This relationship is expressed as a function of the incident and...
92

You might also read

Related Articles

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

Sort by
Same author

Bounds on the Excess Minimum Risk via Generalized Information Divergence Measures.

Entropy (Basel, Switzerland)·2025
Same author

Rényi Cross-Entropy Measures for Common Distributions and Processes with Memory.

Entropy (Basel, Switzerland)·2023
Same author

On Decoder Ties for the Binary Symmetric Channel with Arbitrarily Distributed Input.

Entropy (Basel, Switzerland)·2023
Same journal

Research on a Regional Availability Evaluation Model for Road-Area High-Entropy Energy Based on Synergy Factors.

Entropy (Basel, Switzerland)·2026
Same journal

Atmospheric Turbulence Channel Modeling and Performance Analysis of a CO-ZP-OFDM Coherent Optical Communication System for UAV Air-to-Ground Scenarios.

Entropy (Basel, Switzerland)·2026
Same journal

Information Geometry and Asymptotic Theory for SMML Estimators.

Entropy (Basel, Switzerland)·2026
Same journal

Correlation Entropy and Power-Law Kinetics.

Entropy (Basel, Switzerland)·2026
Same journal

Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks-From the Perspective of Complex Networks and Machine Learning.

Entropy (Basel, Switzerland)·2026
Same journal

The Statistical-Mechanical Meaning of the Wave Function of Quantum Mechanics.

Entropy (Basel, Switzerland)·2026
See all related articles
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 Experiment Video

Updated: Jun 27, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

524

A Unifying Generator Loss Function for Generative Adversarial Networks.

Justin Veiner1, Fady Alajaji1, Bahman Gharesifard2

  • 1Department of Mathematics and Statistics, Queen's University, Kingston, ON K7L 3N6, Canada.

Entropy (Basel, Switzerland)
|April 26, 2024
PubMed
Summary
This summary is machine-generated.

A new Lα-GAN unifies various generative adversarial network (GAN) loss functions. This framework generalizes Jensen-Shannon divergence, offering a flexible approach for GAN training and improving model performance on image datasets.

Keywords:
Jensen-f-divergencedeep learningf-divergencegenerative adversarial networksparameterized loss functions

More Related Videos

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.0K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

393

Related Experiment Videos

Last Updated: Jun 27, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

524
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.0K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

393

Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Computer Vision

Background:

  • Generative Adversarial Networks (GANs) are powerful deep learning models for generating data.
  • Existing GAN variants use diverse objective functions, leading to fragmentation in research and application.
  • A unified framework is needed to generalize and simplify GAN training.

Purpose of the Study:

  • To introduce a unifying α-parametrized generator loss function for dual-objective GANs.
  • To develop the Lα-GAN system, a novel framework that generalizes existing GAN loss functions.
  • To demonstrate the theoretical underpinnings and practical applicability of the Lα-GAN.

Main Methods:

  • Introduced a unifying α-parametrized generator loss function (Lα) for GANs.
  • Developed the Lα-GAN system, integrating Lα with canonical discriminator losses.
  • Analyzed the generator's optimization problem under an optimal discriminator, linking it to Jensen-fα-divergence.
  • Showcased Lα-GAN as a generalization of VanillaGAN, LSGAN, LkGAN, and (αD,αG)-GAN.

Main Results:

  • The Lα-GAN framework unifies several existing GAN loss functions.
  • The generator's optimization problem is shown to minimize a Jensen-fα-divergence.
  • Experimental validation on MNIST, CIFAR-10, and Stacked MNIST datasets demonstrates Lα-GAN's effectiveness.
  • The proposed method offers a flexible and generalized approach to GAN training.

Conclusions:

  • The Lα-GAN provides a unified and flexible framework for generative adversarial networks.
  • This generalization simplifies the understanding and application of various GAN models.
  • The Lα-GAN system shows promising performance across diverse image generation tasks.