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Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

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...
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

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...

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Related Experiment Video

Updated: Jun 29, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Time Sequence Deep Learning Model for Ubiquitous Tabular Data with Unique 3D Tensors Manipulation.

Adaleta Gicic1, Dženana Đonko1, Abdulhamit Subasi2,3

  • 1Faculty of Electrical Engineering, University of Sarajevo, 71000 Sarajevo, Bosnia and Herzegovina.

Entropy (Basel, Switzerland)
|September 27, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel deep learning (DL) method using Stacked Bidirectional Long Short-Term Memory (LSTM) networks and 3D tensor modeling for tabular data. The approach achieves competitive performance, even with small datasets, offering fast training for large ones.

Keywords:
Stacked Bidirectional LSTMdeep learningdeep neural network architecturesprediction with tabular datatabular datasetstime sequence forecasting algorithms

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Deep learning (DL) models show limitations with tabular data compared to traditional methods.
  • Tabular data's size, structure, and context challenge conventional DL applications.
  • Existing DL algorithms often underperform on tabular datasets.

Purpose of the Study:

  • To propose a novel deep learning method for tabular data analysis.
  • To leverage Stacked Bidirectional Long Short-Term Memory (LSTM) networks for pattern discovery.
  • To integrate customized 3D tensor modeling for enhanced neural network input.

Main Methods:

  • Development of a deep learning model utilizing Stacked Bidirectional LSTM.
  • Implementation of customized 3D tensor modeling for tabular data representation.
  • Empirical validation on six diverse, publicly available datasets.

Main Results:

  • The proposed DL model demonstrates satisfactory performance on tabular data.
  • The model effectively competes with traditional machine learning algorithms for tabular data.
  • Fast model training is achieved, even for large datasets.
  • Exceptional predictive results are obtained even with very small datasets.

Conclusions:

  • Deep learning, specifically Stacked Bidirectional LSTM with 3D tensor modeling, is effective for tabular data.
  • The proposed method overcomes limitations of DL on tabular datasets.
  • This approach offers a simple, fast, and high-performing solution for tabular data modeling.