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相关概念视频

Classification of Signals01:30

Classification of Signals

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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 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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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
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Precession can be demonstrated effectively through a spinning top. If a spinning top is placed on a flat surface near the surface of the Earth at a vertical angle and is not spinning, it will fall over due to the force of gravity producing a torque acting on its center of mass. However, if the top is spinning on its axis, it precesses about the vertical direction, rather than topple over due to this torque. Precessional motion is a combination of a steady circular motion of the axis and the...
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Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
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基于深度学习的人工智能算法来分类手绘螺旋的震动.

Reghu Anandapadmanabhan1, Aayushi Vishnoi1, Geetha Raman2

  • 1Department of Neurology, All India Institute of Medical Sciences (AIIMS), New Delhi, India.

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概括
此摘要是机器生成的。

深度学习算法可以比人类专家更准确地从手绘的螺旋来分类综合征. 这项技术提供了一个客观的工具来诊断和分类各种震动条件.

关键词:
准确度 准确度 准确度 准确度深度学习是一种深度学习.模型模型模型模型模型模型螺旋的螺旋形状是什么,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,

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科学领域:

  • 神经学 神经学
  • 生物医学工程 生物医学工程
  • 人工智能的人工智能

背景情况:

  • 目前缺乏用于诊断和分类综合征的客观生物标志物.
  • 的分类在很大程度上依赖于主观的临床评估.
  • 开发客观的诊断工具对于有效的患者管理至关重要.

研究的目的:

  • 开发和验证一种深度学习 (DL) 算法,用于使用手绘螺旋来分类震.
  • 评估算法的性能与专家评级者对比.
  • 提供一个客观的,独立于特征的震分类方法.

主要方法:

  • 招募了患有各种震综合征的参与者 (静脉震,基本震,帕金森病,小脑动症) 和健康的志愿者.
  • 使用手绘螺旋来通过转移学习训练DL算法 (InceptionResNetV2,Keras序列模型).
  • 在独立队列上对该模型进行外部验证,将其准确性和F1分数与专家临床医生的分数进行比较.

主要成果:

  • DL分类器最初的整体准确率为81%,在重新分析后的调整准确率为70%.
  • 对1535个螺旋图的外部验证结果的准确率为61% (调整后为59%).
  • DL算法显著超过了人类评分人员,他们获得了46%的准确性.

结论:

  • 监督的DL算法可以有效地从简单的手绘螺旋来检测和分类震综合征.
  • 这种方法提供了无偏见的,功能独立的分类,超过了人类评分器的性能.
  • 基于DL的螺旋图的分析为震诊断提供了一个有希望的客观工具.