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Related Concept Videos

Reducing Line Loss01:18

Reducing Line Loss

501
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...
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Three-Winding Transformers01:19

Three-Winding Transformers

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Three identical single-phase transformers can be configured to form a three-phase transformer connection, which involves high-voltage and low-voltage windings. The high-voltage windings are denoted by capital letters A-B-C, while the low-voltage windings are labeled with lowercase letters a-b-c, representing their respective phases. This notation helps distinguish between the high and low voltage sides of the transformer.
In the per-unit equivalent circuit of a grounded Y-Y three-phase...
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Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

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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.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
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Per-Unit Sequence Models01:26

Per-Unit Sequence Models

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An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
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Related Experiment Video

Updated: Apr 22, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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SegT: Separated edge-guidance transformer network for polyp segmentation.

Feiyu Chen1, Haiping Ma1, Weijia Zhang1,2

  • 1Department of Mathematics, Physics and Information Sciences, Shaoxing University, Shaoxing, China.

Mathematical Biosciences and Engineering : MBE
|December 5, 2023
PubMed
Summary

A new SegT network improves colon polyp segmentation using a transformer encoder and edge-guidance module. This approach enhances accuracy in medical image analysis for better polyp detection and intervention.

Keywords:
cascade fusionpolyp segmentationseparated edge-guidancetransformer network

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Area of Science:

  • Medical Image Analysis
  • Computer Vision
  • Surgical Interventions

Background:

  • Accurate colon polyp segmentation is crucial for medical image analysis and surgical interventions.
  • Existing encoder-decoder models struggle with preserving local features and precise edge delineation.
  • Global information reliance in current methods can lead to loss of critical local regional details.

Purpose of the Study:

  • To introduce a novel SegT network for effective colon polyp segmentation.
  • To address limitations in existing models regarding local feature preservation and edge accuracy.
  • To improve the robustness and precision of polyp segmentation in colonoscopic images.

Main Methods:

  • Proposed a novel separated edge-guidance transformer (SegT) network.
  • Utilized a transformer encoder for robust feature representation.
  • Implemented a separated edge-guidance module with separator and edge-guidance blocks.
  • Employed a cascade fusion module to integrate refined multi-level features.

Main Results:

  • The SegT network achieved state-of-the-art performance on five challenging public datasets.
  • Demonstrated enhanced accuracy in segmenting diverse colonoscopic polyps.
  • Successfully improved the understanding of edges between polyps and benign regions.

Conclusions:

  • The proposed SegT network offers a significant advancement in colon polyp segmentation.
  • The novel architecture effectively overcomes limitations of previous methods.
  • SegT shows great potential for improving diagnostic accuracy and guiding surgical interventions.