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

59
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,...
59
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

80
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....
80
Transmission-Line Differential Equations01:26

Transmission-Line Differential Equations

192
Transmission lines are essential components of electrical power systems. They are characterized by the distributed nature of resistance (R), inductance (L), and capacitance (C) per unit length. To analyze these lines, differential equations are employed to model the variations in voltage and current along the line.
Line Section Model
A circuit representing a line section of length Δx helps in understanding the transmission line parameters. The voltage V(x) and current i(x) are measured...
192
Kinematic Equations: Problem Solving01:15

Kinematic Equations: Problem Solving

11.8K
When analyzing one-dimensional motion with constant acceleration, the problem-solving strategy involves identifying the known quantities and choosing the appropriate kinematic equations to solve for the unknowns. Either one or two kinematic equations are needed to solve for the unknowns, depending on the known and unknown quantities. Generally, the number of equations required is the same as the number of unknown quantities in the given example. Two-body pursuit problems always require two...
11.8K
Equation of Motion: General Plane motion - Problem Solving01:16

Equation of Motion: General Plane motion - Problem Solving

162
Consider a lawn roller with a mass of 100 kg, a radius of 0.2 meters, and a radius of gyration of 0.15 meters. A force of 200 N is applied to this roller, angled at 60 degrees from the horizontal plane. What will be the angular acceleration of the lawn roller?
The friction between the roller and the ground is characterized by two coefficients. The static friction coefficient is 0.15, while the kinetic friction coefficient is 0.1. These values are crucial in understanding the interaction between...
162
Kinematic Equations - II01:17

Kinematic Equations - II

9.2K
The second kinematic equation expresses the final position of an object in terms of its initial position, the distance traveled with the initial constant velocity, and the distance traveled due to a change in velocity. Similar to the first kinematic equation, this equation is also only valid when the acceleration is constant throughout the motion of an object.
Suppose a car merges into freeway traffic on a 200 m long ramp. If its initial velocity is 10 m/s and it accelerates at 2 m/s2, then the...
9.2K

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

An Exploration of Heart Rate Response and Blood Tetrahydrocannabinol (THC) Levels to Commercially Available Cannabis Edibles by Dose.

Research square·2026
Same author

Making highways and workplaces safer: An interpretable machine learning approach to predicting recent cannabis use and impairment.

Journal of safety research·2026
Same author

Multilevel functional quantile principal component analysis.

Biostatistics (Oxford, England)·2026
Same author

Advancing translational science through biostatistics, epidemiology, and research design consultations: A multi-perspective evaluation of the Georgia CTSA BERD program.

Journal of clinical and translational science·2026
Same author

Spinal cord stimulation for upper limb motor function in people with chronic post-stroke hemiparesis: a feasibility trial.

Nature medicine·2026
Same author

Correcting Measurement Error and Zero Inflation in Functional Covariates for Scalar-on-Function Quantile Regression.

Statistics in medicine·2026
Same journal

Inference on summaries of a model-agnostic longitudinal variable importance trajectory with application to suicide prevention.

The annals of applied statistics·2026
Same journal

A NOVEL BAYESIAN FRAMEWORK UNCOVERING BRAIN CONNECTIVITY-TO-SHAPE RELATIONSHIP IN PRECLINICAL ALZHEIMER'S DISEASE.

The annals of applied statistics·2026
Same journal

EVALUATING MULTIPLEX DIAGNOSTIC TEST USING PARTIALLY ORDERED BAYES CLASSIFIER.

The annals of applied statistics·2026
Same journal

BRIDGING THE GAP: ENHANCING THE GENERALIZABILITY OF EPIGENETIC CLOCKS THROUGH TRANSFER LEARNING.

The annals of applied statistics·2026
Same journal

TREATMENT EFFECT HETEROGENEITY AND IMPORTANCE MEASURES FOR MULTIVARIATE CONTINUOUS TREATMENTS.

The annals of applied statistics·2026
Same journal

FEDERATED LEARNING OF ROBUST INDIVIDUALIZED DECISION RULES WITH APPLICATION TO HETEROGENEOUS MULTIHOSPITAL SEPSIS POPULATION.

The annals of applied statistics·2026
查看所有相关文章

相关实验视频

Updated: May 20, 2025

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion
09:32

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion

Published on: April 11, 2018

9.6K

使用功能线性微分方程建模轨迹.

Julia Wrobel1, Britton Sauerbrei2, Eric A Kirk2

  • 1Department of Biostatistics and Bioinformatics, Emory University.

The annals of applied statistics
|March 26, 2025
PubMed
概括
此摘要是机器生成的。

这项研究使用一种新的动态系统方法模拟运动期间的肌肉激活和爪子运动. 研究结果表明,肌肉活动会影响脚的位置和速度,并且会在激活之后产生持久的影响.

关键词:
功能回归的功能回归动态系统是动态系统.非线性最小平方.常规微分方程 常规微分方程

更多相关视频

Image Processing Protocol for the Analysis of the Diffusion and Cluster Size of Membrane Receptors by Fluorescence Microscopy
12:15

Image Processing Protocol for the Analysis of the Diffusion and Cluster Size of Membrane Receptors by Fluorescence Microscopy

Published on: April 9, 2019

8.6K
Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.5K

相关实验视频

Last Updated: May 20, 2025

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion
09:32

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion

Published on: April 11, 2018

9.6K
Image Processing Protocol for the Analysis of the Diffusion and Cluster Size of Membrane Receptors by Fluorescence Microscopy
12:15

Image Processing Protocol for the Analysis of the Diffusion and Cluster Size of Membrane Receptors by Fluorescence Microscopy

Published on: April 9, 2019

8.6K
Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.5K

科学领域:

  • 生物力学 生物力学
  • 神经科学是一个神经科学.
  • 功能数据分析 功能数据分析

背景情况:

  • 运动涉及肌肉活动和四肢运动之间的复杂相互作用.
  • 现有的模型往往难以捕捉这种关系的动态性,随时间变化的性质.

研究的目的:

  • 开发和验证一种创新的回归方法,用于建模运动期间肌肉激活和脚位置之间的动态关系.
  • 分析小鼠的步态周期,将肌肉激活数据与脚位置跟踪集成.

主要方法:

  • 提出了一种结合普通微分方程 (ODEs) 和函数数据分析的新型通用回归方法.
  • 在所有步行周期曲线中同时估计了ODE参数,借鉴了观测中的强度.
  • 通过模拟验证了方法,并对脚位置的预测准确性进行了交叉验证.

主要成果:

  • 拟议的模型成功地捕获了与肌肉激活 (双,三) 与脚位置和速度相关的动态系统.
  • 发现肌肉激活在鼠标移动过程中动态地影响了爪子的速度和位置.
  • 观察到肌肉激活对脚运动的影响持续超过激活本身的时间.

结论:

  • 基于ODE的新型功能数据分析方法为理解动态生物系统提供了强大的工具.
  • 肌肉激活在运动期间控制四肢运动中起着至关重要的,时间延迟的作用.
  • 这种方法为涉及连续功能数据的复杂生物过程提供了改进的建模能力.