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Sequence Networks of Rotating Machines01:24

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...

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Related Experiment Video

Updated: May 11, 2026

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
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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
PubMed
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:
generationgenerative adversarial networkprogressive neural networkreconstructionvariational autoencodervoxel

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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.