Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Sequence Networks of Rotating Machines

Sequence Networks of Rotating Machines

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...

You might also read

Related Articles

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

Sort by

Same author

Diffusion-based skin disease data augmentation with fine-grained detail preservation and interpolation for data diversity.

PloS one·2025

Same author

Implementation of Smart Farm Systems Based on Fog Computing in Artificial Intelligence of Things Environments.

Sensors (Basel, Switzerland)·2024

Same author

Optimization of Deep Learning Parameters for Magneto-Impedance Sensor in Metal Detection and Classification.

Sensors (Basel, Switzerland)·2023

Same author

Graph Convolutional Network for 3D Object Pose Estimation in a Point Cloud.

Sensors (Basel, Switzerland)·2022

Same author

Numerical Approach to Facial Palsy Using a Novel Registration Method with 3D Facial Landmark.

Sensors (Basel, Switzerland)·2022

Same author

Change in Blink Rate in the Metaverse VR HMD and AR Glasses Environment.

International journal of environmental research and public health·2022

Search research articles

Related Experiment Video

Updated: May 11, 2026

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

A Variational Autoencoder Cascade Generative Adversarial Network for Scalable 3D Object Generation and

Min-Su Yu¹, Tae-Won Jung², Dai-Yeol Yun³

¹Department of Smart Convergence, Kwangwoon University, Seoul 01897, Republic of Korea.

Sensors (Basel, Switzerland)

|February 10, 2024

Summary

This summary is machine-generated.

This study introduces a novel Generative Adversarial Network (GAN) and Variational Autoencoder (VAE) hybrid model for advanced 3D shape generation and reconstruction. The progressive growth approach enhances 3D model quality and detail representation.

Keywords:

generation generative adversarial network progressive neural network reconstruction variational autoencoder voxel

More Related Videos

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells

Published on: September 28, 2019

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

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

Published on: December 15, 2023

Related Experiment Videos

Last Updated: May 11, 2026

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells

Published on: September 28, 2019

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

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

Published on: December 15, 2023

Area of Science:

Computer Vision and Machine Learning
3D Graphics and Modeling
Artificial Intelligence

Background:

Generative Adversarial Networks (GANs) are increasingly used for 3D volume generation and reconstruction tasks.
Existing methods face challenges including limited data, high computational demands, and mode collapse.
Need for enhanced methods for generating and reconstructing intricate voxel-based 3D shapes.

Purpose of the Study:

To propose a hybrid Generative Adversarial Network (GAN) and Variational Autoencoder (VAE) model.
To introduce a stable and scalable progressive growth approach for 3D shape generation and reconstruction.
To improve the quality, convergence speed, and detail representation of generated 3D models.

Main Methods:

Combined Variational Autoencoder (VAE) and Generative Adversarial Network (GAN) architectures.
Implemented a cascade-structured network with incremental layer addition (progressive growth).
Supervised the discriminator with ground-truth labels at each growth stage to model broader voxel spaces.

Main Results:

Achieved enhanced convergence speed and improved quality of generated 3D models.
Demonstrated stable growth, facilitating accurate representation of intricate voxel-level details.
Comparative experiments showed superior voxel quality, variation, and diversity compared to existing methods.

Conclusions:

The proposed VAE-GAN hybrid with progressive growth effectively generates and reconstructs intricate 3D shapes.
The method offers improved accuracy in 3D evaluation metrics and visual quality.
Generated models are valuable for applications in virtual reality, the metaverse, and gaming.