Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

81
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,...
81
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

89
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....
89
Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

259
The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...
259
Feedback control systems01:26

Feedback control systems

307
Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
307
Basic Discrete Time Signals01:16

Basic Discrete Time Signals

204
The unit step sequence is defined as 1 for zero and positive values of the integer n. This sequence can be graphically displayed using a set of eight sample points, showing a step function starting from n=0 and remaining constant thereafter.
The unit impulse or sample sequence is mathematically expressed as zero for all n values except at n=0, where it is one. The unit impulse sequence, denoted by δ(n), is the first difference of the unit step sequence, while the unit step sequence u(n) is...
204
Exponential Fourier series01:24

Exponential Fourier series

200
In audio signal processing, the exponential Fourier series plays a crucial role in sound synthesis, allowing complex sounds to be broken down into simpler sinusoidal components. This decomposition process is fundamental in analyzing and reconstructing musical notes and other audio signals. The exponential Fourier series expresses periodic signals as the sum of complex exponentials at both positive and negative harmonic frequencies, providing a powerful tool for signal analysis.
Euler's identity...
200

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

A national-scale database of groundwater level data for Switzerland.

Scientific data·2026
Same author

A Global-Scale Time Series Dataset for Groundwater Studies within the Earth System.

Scientific data·2026
Same author

The Potential of Horizontal Wells for Aquifer Storage and Recovery in Saline Aquifers.

Ground water·2026
Same author

Quantification and Analysis of Hydrograph Behavior Using Groundwater Signatures.

Ground water·2025
Same author

Linked Data-Driven, Physics-Based Modeling of Pumping-Induced Subsidence with Application to Bangkok, Thailand.

Ground water·2024
Same author

The Effective Vertical Anisotropy of Layered Aquifers.

Ground water·2024

相关实验视频

Updated: Jun 29, 2025

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
11:14

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

10.9K

使用可变和模型生成的合成数据对非线性头部动力学的时间序列分析.

Martin A Vonk1,2, Raoul A Collenteur3, Sorab Panday4

  • 1Department of Water Management, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, South Holland, The Netherlands.

Ground water
|April 6, 2024
PubMed
概括
此摘要是机器生成的。

本研究使用合成数据评估时间序列模型来预测地下水头部. 非线性模型准确地模拟了地下水的动态,在水文预测方面表现优于线性模型.

更多相关视频

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
12:09

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

18.0K
Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression
11:26

Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression

Published on: December 10, 2014

12.4K

相关实验视频

Last Updated: Jun 29, 2025

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
11:14

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

10.9K
Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
12:09

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

18.0K
Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression
11:26

Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression

Published on: December 10, 2014

12.4K

科学领域:

  • 水文地质学 水文地质学
  • 环境建模环境建模
  • 时间序列分析时间序列分析

背景情况:

  • 地下水的头部波动受到复杂的水文过程的影响,包括降水和蒸发.
  • 精确模拟这些动态对于有效的水资源管理至关重要.
  • 现有的时间序列模型可能无法完全捕捉地下水系统的非线性行为.

研究的目的:

  • 评估线性和非线性时间序列模型在模拟合成地下水头部数据中的性能.
  • 将这些模型的准确性与数值理查德方程模型进行比较.
  • 为评估数据驱动的水文模型提供工具.

主要方法:

  • 使用数值模型解决理查德斯方程用于变量和流量的合成地下水头系列.
  • 在不同的土壤类型和不和区域厚度下,模拟头部对降水和蒸发的反应.
  • 应用和评估了使用R平方值的线性和非线性时间序列模型.

主要成果:

  • 线性时间序列模型实现了从0.67到0.96.9的R平方值.
  • 非线性时间序列模型,结合根区储库,始终在0.9.9以上实现R平方值.
  • 非线性模型的降水事件反应与数值模型的输出密切匹配.

结论:

  • 与线性模型相比,非线性时间序列模型在模拟地下水头动态方面表现优越.
  • 开发的合成数据生成脚本可以用于测试各种数据驱动的水文模型.
  • 通过先进的时间序列技术,可以准确模拟地下水充电和头部反应.