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相关概念视频

State Space Representation01:27

State Space Representation

165
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
165
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

64
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
64
Transfer Function to State Space01:23

Transfer Function to State Space

196
State-space representation is a powerful tool for simulating physical systems on digital computers, necessitating the conversion of the transfer function into state-space form. Consider an nth-order linear differential equation with constant coefficients, like those encountered in an RLC circuit. The state variables are selected as the output and its n−1 derivatives. Differentiating these variables and substituting them back into the original equation produces the state equations.
In an...
196
State Space to Transfer Function01:21

State Space to Transfer Function

174
The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
174

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相关实验视频

Updated: Jun 7, 2025

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable
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使用状态空间模型量化婴儿自发运动.

E Passmore1,2,3,4, A K L Kwong3,5,6, J E Olsen5,6

  • 1Developmental Imaging, MCRI, Melbourne, Australia.

Scientific reports
|November 20, 2024
PubMed
概括
此摘要是机器生成的。

自动姿势估计追踪婴儿的运动,将其建模成八个运动状态. 这种技术有助于评估神经发育结果,并更有效地识别高风险婴儿.

关键词:
隐藏的马尔科夫模型高危的婴儿高危的婴儿汽车发展 汽车发展神经发育 神经发育位置估计 位置估计

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科学领域:

  • 发育神经科学的发展神经科学.
  • 计算生物学是一种计算生物学.
  • 医学成像分析分析 医学成像分析

背景情况:

  • 婴儿早期的自发,动的总体运动是以后神经发育结果的关键指标.
  • 缺乏动的动作是几个神经发育和认知障碍的标志.
  • 手动评估婴儿运动模式是劳动密集型和耗时的,阻碍了广泛的临床应用.

研究的目的:

  • 开发和验证一个自动化的,无标记的姿势估计系统,用于分析早期婴儿的运动模式.
  • 通过使用统计方法,将婴儿运动模型作为一系列不同的运动状态.
  • 调查与神经发育障碍高风险婴儿的运动状态的与年龄相关的变化和差异.

主要方法:

  • 利用计算机视觉和基于深度学习的姿势估计技术,从视频数据中无标记地跟踪婴儿身体部位.
  • 编制了来自330名婴儿的486个婴儿运动视频的数据集.
  • 应用自回归状态空间模型,以统计模型婴儿运动作为八个不同的运动状态的序列.

主要成果:

  • 证明婴儿运动可以有效地被模拟为八个年龄不同的运动状态的序列.
  • 展示了这些运动状态的表达在被确定为神经发育不良结果的高风险婴儿中显著不同.
  • 验证了自动化运动分析在区分典型和非典型早期发育方面的潜力.

结论:

  • 自动无标记物姿势估计提供了一个可扩展和高效的方法来分析早期婴儿的运动.
  • 鉴定的运动状态为了解婴儿运动发育和神经发育轨迹提供了一个新的框架.
  • 这种方法有可能改善神经发育障碍风险的婴儿的早期识别和干预.