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相关概念视频

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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相关实验视频

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时间序列深度学习模型用于随处可见的表格数据,具有独特的3D张量器操纵.

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
概括

本研究介绍了一种新的深度学习 (DL) 方法,使用堆叠双向长期短期记忆 (LSTM) 网络和表格数据的3D张量模型. 该方法实现了竞争性性能,即使是小数据集,为大数据集提供快速培训.

关键词:
堆叠的双向LSTM可以使用.深度学习是一种深度学习.深度神经网络架构的深度神经网络架构.使用表格数据进行预测.表格式数据集是一个表格式数据集.时间序列预测算法时间序列预测算法

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科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 深度学习 (DL) 模型与传统方法相比,表格数据存在局限性.
  • 表格数据的尺寸,结构和上下文挑战了传统的DL应用程序.
  • 现有的DL算法在表格数据集上往往表现不佳.

研究的目的:

  • 为表格数据分析提出一种新的深度学习方法.
  • 为了利用堆叠双向长短期内存 (LSTM) 网络进行模式发现.
  • 集成定制的3D张量模型,以增强神经网络输入.

主要方法:

  • 开发一种使用堆叠双向LSTM的深度学习模型.
  • 实现定制的3D张量模型用于表格数据表示.
  • 在六个不同的,公开可用的数据集上进行实证验证.

主要成果:

  • 拟议的DL模型在表格数据上表现出令人满意的性能.
  • 该模型有效地与表格数据的传统机器学习算法竞争.
  • 实现了快速模型培训,即使对于大型数据集.
  • 即使使用非常小的数据集,也可以获得特殊的预测结果.

结论:

  • 深度学习,特别是3D张量建模的堆叠双向LSTM,对于表格数据是有效的.
  • 拟议的方法克服了表格数据集上的DL的局限性.
  • 这种方法为表式数据建模提供了一个简单,快速和高性能的解决方案.