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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
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A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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Classification of Systems-II01:31

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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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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: Jul 23, 2025

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TCGAN: Convolutional Generative Adversarial Network for time series classification and clustering.

Fanling Huang1, Yangdong Deng1

  • 1School of Software, Tsinghua University, Beijing, China.

Neural Networks : the Official Journal of the International Neural Network Society
|July 11, 2023
PubMed
Summary

This study introduces Time-series Convolutional GAN (TCGAN) for time series classification. TCGAN effectively learns representations from unlabeled data, outperforming existing methods and enabling accurate classification with limited labels.

Keywords:
ClassificationClusteringDeep Neural NetworksGenerative Adversarial NetworksRepresentation learningTime series

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Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Data Science

Background:

  • Supervised Convolutional Neural Networks (CNNs) excel at time series classification but require extensive labeled data.
  • Acquiring labeled time series data is often costly and impractical.
  • Generative Adversarial Networks (GANs) show promise in unsupervised and semi-supervised learning but their application to general time series representation learning is underexplored.

Purpose of the Study:

  • To introduce a novel Generative Adversarial Network (GAN) model, TCGAN, for unsupervised time series representation learning.
  • To evaluate TCGAN's effectiveness in enabling accurate time series classification and clustering.
  • To demonstrate TCGAN's utility in scenarios with limited or imbalanced labeled data.

Main Methods:

  • Developed Time-series Convolutional GAN (TCGAN), an adversarial framework using two 1D CNNs (generator and discriminator).
  • TCGAN learns representations from unlabeled time series data through an adversarial game.
  • Reused parts of the trained TCGAN to create a representation encoder for downstream recognition tasks.

Main Results:

  • TCGAN demonstrated faster and more accurate performance compared to existing time-series GANs on synthetic and real-world datasets.
  • Learned representations from TCGAN significantly improved the performance of simple classification and clustering methods.
  • TCGAN maintained high efficacy even with few-labeled and imbalanced-labeled time series data.

Conclusions:

  • TCGAN offers a powerful approach for unsupervised representation learning from time series data.
  • The model effectively addresses the challenge of limited labeled data in time series analysis.
  • TCGAN provides a viable method to leverage abundant unlabeled time series data for recognition tasks.