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Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
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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: May 5, 2026

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells
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Deep-learning reconstruction of complex dynamical networks from incomplete data.

Xiao Ding1, Ling-Wei Kong2, Hai-Feng Zhang1

  • 1The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Mathematical Science, Anhui University, Hefei 230601, China.

Chaos (Woodbury, N.Y.)
|April 4, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning framework to reconstruct complex networks and predict their dynamics using incomplete data. The method enhances both network inference and dynamical prediction accuracy, outperforming existing approaches.

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Area of Science:

  • Complex Systems Science
  • Network Science
  • Computational Science

Background:

  • Reconstructing complex networks and predicting their dynamics are challenging due to incomplete information.
  • Real-world applications often suffer from missing data, hindering accurate analysis.

Purpose of the Study:

  • To develop a unified deep-learning framework for network reconstruction and dynamic prediction with incomplete data.
  • To improve the accuracy of inferring network structures and estimating unobserved states.

Main Methods:

  • A collaborative deep-learning framework with three modules: network inference, state estimation, and dynamical learning.
  • An alternating parameter updating strategy to enhance inference and prediction.
  • Validation on synthetic and empirical datasets, including influenza and PM2.5 data.

Main Results:

  • The proposed framework significantly outperforms baseline methods in network inference and dynamical prediction.
  • A reciprocal relationship was observed, where improved network inference enhances dynamical prediction accuracy, and vice versa.
  • Demonstrated superior performance on real-world influenza and PM2.5 datasets.

Conclusions:

  • The unified deep-learning framework effectively addresses challenges of incomplete data in network science.
  • The developed method offers a robust approach for reconstructing complex networks and predicting their dynamics.
  • The findings highlight the synergistic relationship between network structure inference and dynamical prediction.