Jove
Visualize
联系我们

相关概念视频

Mechanical Efficiency of Real Machines01:14

Mechanical Efficiency of Real Machines

1.6K
The mechanical efficiency of a machine is a fundamental concept that describes how effectively a machine can convert input work into output work. According to this concept, the efficiency of a machine is equal to the ratio of the output work to the input work. An ideal machine, meaning a machine that has no energy losses, has an efficiency of one. This implies that the input work and the output work are equal.
However, in reality, no machine can be truly ideal, and all of them experience some...
1.6K
Machines: Problem Solving II01:30

Machines: Problem Solving II

791
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
791
PD Controller: Design01:26

PD Controller: Design

761
In automotive engineering, car suspension systems often employ Proportional Derivative (PD) controllers to enhance performance. PD controllers are utilized to adjust the damping force in response to road conditions. A controller, acting as an amplifier with a constant gain, demonstrates proportional control, with output directly mirroring input.
Designing a continuous-data controller requires selecting and linking components like adders and integrators, which are fundamental in Proportional,...
761
Response Surface Methodology01:16

Response Surface Methodology

904
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
904
Application of Linearization and Approximation01:29

Application of Linearization and Approximation

193
A drone flying through complex terrain often relies on more than one sensing method to estimate small changes in altitude. Along with direct measurements, air pressure provides a useful indirect indicator of vertical movement. Atmospheric pressure decreases as altitude increases, and this relationship is commonly described using an exponential model. Although accurate, converting pressure measurements into altitude values requires calculations that are too complex to perform repeatedly during...
193
Applications of Integration to Find Hydrostatic Pressure01:30

Applications of Integration to Find Hydrostatic Pressure

367
Hydrostatic force is a fluid's total force at rest on a surface. For a horizontal surface submerged at a fixed depth, the pressure is constant and calculated as the product of fluid density, gravitational acceleration, and depth. In the case of a vertical dam wall submerged in water, this force is not evenly distributed due to the increasing pressure with depth. This variation arises from the cumulative weight of the water above each point. Integration is used to account for the continuous...
367

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

Legendre Polynomial Fitting-Based Permutation Entropy Offers New Insights into the Influence of Fatigue on Surface Electromyography (sEMG) Signal Complexity.

Entropy (Basel, Switzerland)·2024
Same author

Impact of PCA Pre-Normalization Methods on Ground Reaction Force Estimation Accuracy.

Sensors (Basel, Switzerland)·2024
Same author

On the Genuine Relevance of the Data-Driven Signal Decomposition-Based Multiscale Permutation Entropy.

Entropy (Basel, Switzerland)·2023
Same author

The Refined Composite Downsampling Permutation Entropy Is a Relevant Tool in the Muscle Fatigue Study Using sEMG Signals.

Entropy (Basel, Switzerland)·2021
Same author

The Impact of Linear Filter Preprocessing in the Interpretation of Permutation Entropy.

Entropy (Basel, Switzerland)·2021
Same author

Improvement of Statistical Performance of Ordinal Multiscale Entropy Techniques Using Refined Composite Downsampling Permutation Entropy.

Entropy (Basel, Switzerland)·2020
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

相关实验视频

Updated: May 2, 2026

Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis
11:16

Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis

Published on: July 22, 2014

16.2K

使用压力内的监督机器学习和深度学习方法之间的地面反应力组件估计精度的比较.

Amal Kammoun1,2, Philippe Ravier1, Olivier Buttelli1,3

  • 1PRISME Laboratory, University of Orleans, 12 Rue de Blois, 45100 Orleans, France.

Sensors (Basel, Switzerland)
|August 29, 2024
PubMed
概括

监督机器学习 (SML) 方法,特别是随机森林 (RF),使用内传感器准确估计地面反应力 (GRF) 组件,在静态活动中优于深度学习 (DL) 方法.

关键词:
电力电网组件估计部分深度学习是一种深度学习.强力板测量测量方法进sole压力测量测量器的压力测量手动处理材料手动处理材料监督机器学习是指监督机器学习.步行活动 步行活动

更多相关视频

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
10:52

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Published on: April 13, 2016

8.8K
Predictive Measurement for Windlass Change in Length and Selected Treatment Outcomes in Chronic Plantar Fasciitis
02:15

Predictive Measurement for Windlass Change in Length and Selected Treatment Outcomes in Chronic Plantar Fasciitis

Published on: March 1, 2024

471

相关实验视频

Last Updated: May 2, 2026

Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis
11:16

Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis

Published on: July 22, 2014

16.2K
Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
10:52

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Published on: April 13, 2016

8.8K
Predictive Measurement for Windlass Change in Length and Selected Treatment Outcomes in Chronic Plantar Fasciitis
02:15

Predictive Measurement for Windlass Change in Length and Selected Treatment Outcomes in Chronic Plantar Fasciitis

Published on: March 1, 2024

471

科学领域:

  • 生物力学 生物力学
  • 传感器技术 传感器技术
  • 机器学习 机器学习

背景情况:

  • 估计地面反应力 (GRF) 组件对于生物力学分析至关重要.
  • 压力内底传感器为GRF估计提供了一种便携式方法.
  • 对比各种机器学习算法用于GRF估计对于实际应用至关重要.

研究的目的:

  • 估计GRF组件 (Fx,Fy,Fz) 使用压力内底传感器在六个活动中,包括新的静态和手动物料处理场景.
  • 为了比较六种不同的方法的准确性,三种深度学习 (DL) 和三种监督机器学习 (SML) 用于GRF组件估计.
  • 确定不同活动中GRF估计的最准确方法.

主要方法:

  • 评估了六种方法:人工神经网络,长期短期记忆,卷积神经网络 (DL) 和最小平方,支持向量回归,随机森林 (SML).
  • 从9名从事6种不同的活动的受试者收集了数据.
  • 根平均平方误差 (RMSE) 用于量化对力板数据的估计准确性.

主要成果:

  • 随机森林 (RF) 方法在估计静态活动的GRF组件方面表现出最高的准确性.
  • 对于静态情况,RF实现的RMSE平均值明显低于参考测量值.
  • 监督机器学习方法,特别是射频,在GRF估计准确度方面超过了经过测试的深度学习方法.

结论:

  • 压力内底传感器与监督机器学习相结合,特别是随机森林,提供准确的GRF组件估计.
  • 该研究将GRF估计扩展到新的活动中,为生物力学和人体工程学提供了宝贵的见解.
  • 在静态和潜在的其他活动中,RF为GRF估计提供了Deep Learning方法的优越替代方案.