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Related Concept Videos

Per-Unit Sequence Models01:26

Per-Unit Sequence Models

347
An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
347
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

412
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.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
412
Prediction Intervals01:03

Prediction Intervals

3.0K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Time-Series Graph00:54

Time-Series Graph

4.9K
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...
4.9K
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

1.0K
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
1.0K
Basic Continuous Time Signals01:22

Basic Continuous Time Signals

589
Basic continuous-time signals include the unit step function, unit impulse function, and unit ramp function, collectively referred to as singularity functions. Singularity functions are characterized by discontinuities or discontinuous derivatives.
The unit step function, denoted u(t), is zero for negative time values and one for positive time values, exhibiting a discontinuity at t=0. This function often represents abrupt changes, such as the step voltage introduced when turning a car's...
589

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Related Experiment Videos

2D Convolutional Neural Markov Models for Spatiotemporal Sequence Forecasting.

Calvin Janitra Halim1, Kazuhiko Kawamoto2

  • 1Department of Applied and Cognitive Informatics, Graduate School of Science and Engineering, Chiba University, Chiba-shi, Chiba 263-8522, Japan.

Sensors (Basel, Switzerland)
|August 1, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel deep Markov model (DMM) using 2D convolutional neural networks to improve spatiotemporal forecasting. The enhanced model effectively handles noisy data and preserves spatial characteristics, outperforming existing deep learning methods.

Keywords:
CNNDMMLSTMdeep Markov modeldeep neural networksspatiotemporal forecastingtime series prediction

Related Experiment Videos

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Data Science

Background:

  • Deep neural networks, particularly recurrent neural networks, are used for complex spatiotemporal forecasting.
  • Real-world spatiotemporal data is often noisy and chaotic, necessitating probabilistic models for robustness.
  • Existing deep Markov models (DMMs) struggle to maintain spatial characteristics, often converting data to 1D.

Purpose of the Study:

  • To propose a novel DMM that preserves spatial characteristics in spatiotemporal forecasting.
  • To enhance the robustness of time series forecasting models against noise and large data variance.
  • To improve the accuracy of forecasting over longer periods.

Main Methods:

  • Developed a DMM incorporating 2D convolutional neural networks to retain spatial information.
  • Utilized synthetic data with high variance to test the model's robustness.
  • Compared the proposed model against naive forecasting, vanilla DMM, and convolutional LSTM.

Main Results:

  • The proposed 2D convolutional DMM demonstrated superior robustness to noisy data compared to baseline methods.
  • The model outperformed deep neural network models, including convolutional LSTM, in longer forecast periods.
  • The study identified limitations in forecasting real-world precipitation data.

Conclusions:

  • The novel 2D convolutional DMM effectively models spatiotemporal sequences while preserving spatial information and handling noise.
  • This approach offers a more robust alternative to existing methods for complex time series forecasting.
  • Future work should address limitations with real-world data and explore further research potentials.