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Sequence Networks of Rotating Machines01:24

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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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Sequences01:29

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Updated: Dec 5, 2025

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
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Sequence Braiding: Visual Overviews of Temporal Event Sequences and Attributes.

Sara Di Bartolomeo, Yixuan Zhang, Fangfang Sheng

    IEEE Transactions on Visualization and Computer Graphics
    |October 19, 2020
    PubMed
    Summary

    Sequence Braiding offers a novel visualization for temporal event sequences, enabling faster understanding of high-level patterns. This method visually aligns numerous events and attributes simultaneously, improving temporal data analysis.

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    Area of Science:

    • Information Visualization
    • Human-Computer Interaction
    • Data Analysis

    Background:

    • Temporal event sequence alignment is crucial for visualizing changes over time but existing methods struggle with overview tasks and attribute integration.
    • Current approaches often focus on limited sentinel events, requiring complex interactions or multiple visualizations for comprehensive analysis.
    • Event attribute overviews are typically disconnected from sequence visualizations, hindering holistic understanding.

    Purpose of the Study:

    • To introduce Sequence Braiding, a novel visualization technique for temporal event sequences and their attributes.
    • To enable simultaneous visual alignment of numerous events and attribute groups, supporting complex sequence patterns like arbitrary ordering, absence, and duplication.
    • To evaluate the effectiveness of Sequence Braiding against existing methods in user task performance.

    Main Methods:

    • Developed Sequence Braiding, a visualization method based on a layered directed acyclic network.
    • Conducted a controlled experiment comparing Sequence Braiding with IDMVis.
    • Measured user task completion time, correctness, error rates, and confidence levels.

    Main Results:

    • Users employing Sequence Braiding demonstrated faster comprehension of high-level patterns and trends in temporal event sequences.
    • Performance in terms of correctness and error rates was comparable to existing methods.
    • Users reported similar confidence levels when using Sequence Braiding.

    Conclusions:

    • Sequence Braiding provides an effective overview visualization for temporal event sequences and attributes.
    • The novel approach facilitates quicker understanding of complex temporal data patterns compared to existing methods.
    • Sequence Braiding represents a significant advancement in visualizing and analyzing temporal event data.