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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

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Cross-Modal Multivariate Pattern Analysis
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Cross-Modal Scene Networks.

Yusuf Aytar, Lluis Castrejon, Carl Vondrick

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |September 19, 2017
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new method for learning cross-modal scene representations, enabling better transfer across different data types like images and text. The research facilitates improved cross-modal retrieval by creating a shared, modality-agnostic representation.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Humans recognize scenes across various modalities, not just natural images.
    • Current convolutional neural networks (CNNs) learn modality-specific representations, hindering cross-modal transfer.
    • Existing methods lack aligned intermediate representations for effective cross-modal applications.

    Purpose of the Study:

    • To investigate methods for learning cross-modal scene representations that transfer effectively across different modalities.
    • To develop a shared, modality-agnostic representation for scene understanding.
    • To improve cross-modal retrieval performance.

    Main Methods:

    • Introduction of a novel cross-modal scene dataset.
    • Development of regularization techniques for cross-modal CNNs.
    • Creation of a shared representation invariant to the input modality.

    Main Results:

    • The proposed scene representation facilitates cross-modal transfer for retrieval tasks.
    • Visualizations reveal emergent units in the shared representation that activate on consistent concepts.
    • The learned representation is independent of the modality, demonstrating successful cross-modal alignment.

    Conclusions:

    • The developed method enables effective cross-modal scene understanding and retrieval.
    • A shared, modality-agnostic representation is crucial for successful cross-modal transfer.
    • The approach shows promise for applications requiring scene recognition across diverse data types.