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相关概念视频

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

45
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
45
Active Filters01:25

Active Filters

792
Active filters are electronic circuits that use operational amplifiers (op-amps), resistors, and capacitors to filter out unwanted frequency components from a signal. A first-order low-pass active filter is designed to pass signals with a frequency lower than a certain cutoff frequency and attenuate frequencies higher than that cutoff frequency. The transfer function for a first-order low-pass active filter is:
792
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

179
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
179
Passive Filters01:27

Passive Filters

523
Passive filters are utilized to shape the frequency spectrum of signals across a diverse array of applications. These filters, using only passive elements like resistors (R), inductors (L), and capacitors (C), are capable of selectively allowing or blocking certain frequency ranges without the need for external power sources.
Low-Pass Filters
Low-pass filters are designed to transmit signals with frequencies lower than the cutoff frequency, ωc, and attenuate those above it. The cutoff...
523
¹H NMR: Interpreting Distorted and Overlapping Signals01:02

¹H NMR: Interpreting Distorted and Overlapping Signals

1.0K
Spin systems where the difference in chemical shifts of the coupled nuclei is greater than ten times J are called first-order spin systems. These nuclei are weakly coupled, and their chemical shifts and coupling constant can generally be estimated from the well-separated signals in the spectrum.
As Δν decreases and the signals move closer, the doublets appear increasingly distorted. The intensities of the inner lines increase at the cost of those of the outer lines as the signals are...
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Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

215
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...
215

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Synthetic, Multi-Layer, Self-Oscillating Vocal Fold Model Fabrication
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基于物理的增强模型用于高阶马尔科夫过.

Shuo Tang1, Tales Imbiriba1, Jindřich Duník2

  • 1Electrical and Computer Engineering Department, Northeastern University, Boston, MA 02115, USA.

Sensors (Basel, Switzerland)
|September 28, 2024
PubMed
概括
此摘要是机器生成的。

我们为高阶马尔科夫模型引入了基于物理的增强模型 (APBM),增强了状态估计. 我们的新方法减少了复杂动态系统中的估计误差和计算成本.

关键词:
高级的马尔科夫.混合神经网络是一种神经网络.非线性过是一种非线性过.

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科学领域:

  • 控制理论 控制理论
  • 机器学习 机器学习
  • 动态系统 动态系统

背景情况:

  • 基于物理学的增强模型 (APBM) 将物理定律与可解释模型的数据驱动方法相结合.
  • 高级马尔科夫模型需要状态增强来准确地估计状态,通常需要对系统动态的完整知识.

研究的目的:

  • 使用状态增强 (AG-APBM) 将APBM扩展到高阶马尔科夫模型.
  • 开发一个近似状态APBM (AP-APBM),以减少计算负担.
  • 评估AG-APBM和AP-APBM的性能与标准APBM相比.

主要方法:

  • 为高阶马尔科夫模型 (AG-APBM) 增加状态空间以过去的状态.
  • 实现一个近似状态APBM (AP-APBM) 使用过去时间步骤总结.
  • 在自回归和目标追踪场景中测试模型,并延迟反控制.

主要成果:

  • 在减少估计误差方面,AG-APBM和AP-APBM都超过了标准APBM.
  • AG-APBM将自回归模型估计误差减少了31.1%;AP-APBM将其减少了26.7%.
  • 与AG-APBM相比,AP-APBM实现了时间成本 (37.5%) 和内存使用率 (20%) 的显著降低.

结论:

  • 拟议的AG-APBM和AP-APBM有效地处理高阶马尔科夫模型,而不需要完全的动态知识.
  • AP-APBM提供了一个计算效率高的替代AG-APBM,性能降低最小.
  • 这些方法在复杂的控制系统中提高了状态估计的准确性和效率.