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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.
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Related Experiment Video

Updated: Mar 2, 2026

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
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Graph Regularized Restricted Boltzmann Machine.

Dongdong Chen, Jiancheng Lv, Zhang Yi

    IEEE Transactions on Neural Networks and Learning Systems
    |May 16, 2017
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new graph regularized Restricted Boltzmann Machine (RBM) that preserves data manifold structure for better feature learning. The novel RBM model achieves superior performance in image classification and clustering tasks.

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    Area of Science:

    • Machine Learning
    • Artificial Intelligence
    • Computer Vision

    Background:

    • Restricted Boltzmann Machines (RBMs) are effective for unsupervised feature learning in tasks like image classification and speech recognition.
    • Existing RBM models often overlook the preservation of data manifold structure, which is crucial as data often resides on low-dimensional manifolds.
    • Understanding and preserving the underlying data manifold is essential for robust representation learning.

    Purpose of the Study:

    • To propose a novel graph regularized Restricted Boltzmann Machine (RBM) that incorporates data manifold structure.
    • To develop a model that learns sparse and discriminative representations by considering local manifold properties.
    • To enhance feature extraction and representation learning by explicitly preserving the data manifold.

    Main Methods:

    • Introduced a graph regularized RBM incorporating manifold-based locality constraints on the hidden layer.
    • The model imposes constraints to preserve the local structure of the data manifold during the learning process.
    • Utilized benchmark image datasets for evaluating the proposed model's effectiveness.

    Main Results:

    • The proposed graph regularized RBM learns representations that reflect data distributions while preserving local manifold structure.
    • Achieved superior performance in unsupervised clustering and supervised classification tasks compared to existing methods.
    • Demonstrated the effectiveness of incorporating manifold structure for improved feature learning.

    Conclusions:

    • The novel graph regularized RBM effectively captures latent features and learns discriminative representations by preserving data manifold structure.
    • This approach offers a significant improvement over traditional RBMs, particularly for high-dimensional data residing on low-dimensional manifolds.
    • The model shows strong potential for various computer vision applications, including image classification and clustering.