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相关概念视频

Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

94
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
94
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

85
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,...
85
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

267
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
267
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

704
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
704
Reducing Line Loss01:18

Reducing Line Loss

156
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
156
Poisson Probability Distribution01:09

Poisson Probability Distribution

8.2K
A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...
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相关实验视频

Updated: Jul 12, 2025

Image-based Lagrangian Particle Tracking in Bed-load Experiments
10:32

Image-based Lagrangian Particle Tracking in Bed-load Experiments

Published on: July 20, 2017

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在数据包丢失下使用粒子过器进行基于非线性事件的状态估计.

Elhadi Gasmi1, Mohamed Amine Sid2, Oussama Hachana3

  • 1L.A.G.E, Universty of Kasdi Merbah, Ouargla, 30000, Algeria; Mechatronics Laboratory (LMETR) - E1764200 Optics and Precision Mechanics Institute Ferhat Abbas University Setif 1, 19000 Setif, Algeria.

ISA transactions
|October 26, 2023
PubMed
概括
此摘要是机器生成的。

本研究引入了一种事件触发的粒子过器,用于在非线性系统中进行远程状态估计,减少数据传输和有效处理数据包掉落. 该方法确保了准确的估计,尽管数据丢失引起的非高斯条件.

关键词:
克拉梅尔·拉奥下边界基于事件的状态估计.非线性过是一种非线性过.丢失的数据包 丢失的数据包颗粒过器可以过颗粒.

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Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques
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Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques

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Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions
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相关实验视频

Last Updated: Jul 12, 2025

Image-based Lagrangian Particle Tracking in Bed-load Experiments
10:32

Image-based Lagrangian Particle Tracking in Bed-load Experiments

Published on: July 20, 2017

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Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques
10:53

Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques

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Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions
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Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions

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科学领域:

  • 控制系统工程 控制系统工程
  • 信号处理 信号处理
  • 信息理论 信息理论

背景情况:

  • 远程状态估计对于监控复杂系统至关重要.
  • 非线性离散系统对估计提出了独特的挑战.
  • 事件触发的通信和数据包掉落会降低估计的准确性.

研究的目的:

  • 为非线性离散系统开发事件触发的粒子过器.
  • 为了应对数据包掉落所带来的挑战,并减少通信负载.
  • 在非高斯条件下实现准确的状态估计.

主要方法:

  • 使用SOD机制进行传感器调度,以尽量减少数据传输.
  • 使用伯努利分布式随机变量建模数据包掉落.
  • 开发一个事件触发粒子过算法,结合非线性和非高斯性.

主要成果:

  • 从考虑事件触发器和数据包掉落来推导出一个明确的概率函数.
  • 通过连续的蒙特卡洛算法近似后部分布.
  • 通过比较误差共变率与后部克拉梅尔-拉奥下限来评估估计器的性能.

结论:

  • 拟议的事件触发粒子过器有效降低了通信负担.
  • 该算法在带有包滴的非线性系统中实现了适当的估计准确性.
  • 数字示例验证了开发的估计策略的有效性.