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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.
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Related Experiment Video

Updated: Nov 3, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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Tracklet Pair Proposal and Context Reasoning for Video Scene Graph Generation.

Gayoung Jung1, Jonghun Lee1, Incheol Kim1

  • 1Department of Computer Science, Kyonggi University, Suwon-si 16227, Korea.

Sensors (Basel, Switzerland)
|June 2, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces VSGG-Net, a novel deep neural network for video scene graph generation (ViDSGG). The model enhances visual scene understanding by effectively processing object tracklets and spatio-temporal context.

Keywords:
graph neural networkspatio-temporal context reasoningtracklet pair proposalvideo scene graphvisual relationship detection

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Video scene graph generation (ViDSGG) is crucial for visual scene understanding but remains challenging.
  • Existing methods like segment-based and sliding-window approaches have limitations.

Purpose of the Study:

  • To propose a novel deep neural network model, VSGG-Net, for improved video scene graph generation.
  • To address limitations of current ViDSGG methods by enhancing object tracklet detection and context utilization.

Main Methods:

  • Developed VSGG-Net, a deep neural network utilizing a sliding window for object tracklet detection.
  • Introduced a novel tracklet pair proposal method integrating pretrained networks and statistical information.
  • Employed spatio-temporal context graphs, graph neural networks, and class weighting for enhanced reasoning and sparse relationship detection.

Main Results:

  • VSGG-Net demonstrated high performance in video scene graph generation.
  • The model effectively utilized spatio-temporal context for deeper visual scene understanding.
  • Class weighting improved detection performance for sparse relationships.

Conclusions:

  • VSGG-Net offers a significant advancement in video scene graph generation.
  • The proposed methods effectively capture spatio-temporal context and handle sparse relationships.
  • Experimental results on VidOR and VidVRD datasets validate the model's effectiveness.