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相关概念视频

Kinematic Equations: Problem Solving01:15

Kinematic Equations: Problem Solving

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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...
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Kinematic Equations - II01:17

Kinematic Equations - II

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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...
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Kinematic Equations - I01:26

Kinematic Equations - I

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When an object moves with constant acceleration, the velocity of the object changes at a constant rate throughout the motion. The kinematic equations of motions are derived for such cases where the acceleration of the object is constant. The first kinematic equation gives an insight into the relationship between velocity, acceleration, and time. We can see, for example:
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Kinematic Equations - III01:18

Kinematic Equations - III

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The first two kinematic equations have time as a variable, but the third kinematic equation is independent of time. This equation expresses final velocity as a function of the acceleration and distance over which it acts. The fourth kinematic equation does not have an acceleration term and provides the final position of the object at time t in terms of the initial and final velocities. This equation is useful when the value of the constant acceleration is unknown.
Using the kinematic equations,...
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One-Degree-of-Freedom System01:24

One-Degree-of-Freedom System

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In mechanical engineering, one-degree-of-freedom systems form the basis of a wide range of electrical and mechanical components. Using these models, engineers can predict the behavior of various parts in a larger system, which gives them insight into how different forces interact with each other.
A one-degree-of-freedom system is defined by an independent variable that determines its state and behavior. One example of a one-degree-of-freedom system is a simple harmonic oscillator, such as a...
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Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

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Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
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相关实验视频

Updated: Jun 27, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study
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在嵌入式系统上实施行走动力轨迹预测模型.

Madina Shayne1, Leonardo A Molina2, Bin Hu3

  • 1Department of Mechanical and Manufacturing Engineering, University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, Canada.

Sensors (Basel, Switzerland)
|April 27, 2024
PubMed
概括
此摘要是机器生成的。

通过反复拓 (FReT) 预测提供了一种计算效率高的方法,用于预测可穿戴辅助设备中的步态动力学. 这种智能算法提高了准确性和平衡性能,为先进的传感器技术铺平了道路.

关键词:
嵌入式系统嵌入式系统预测 预测 预测 预测步态 步态 步态 步态传感器 传感器 传感器可以穿戴的可穿戴设备.

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相关实验视频

Last Updated: Jun 27, 2025

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3D Kinematic Gait Analysis for Preclinical Studies in Rodents
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科学领域:

  • 生物力学和机器人技术
  • 机器学习用于可穿戴技术

背景情况:

  • 像假肢和外骨架这样的可穿戴辅助设备需要智能算法来预测步态.
  • 嵌入式系统的计算负载限制阻碍了复杂的步态模型.
  • 现有的模型在适应性和资源需求方面扎.

研究的目的:

  • 在嵌入式系统上部署和评估通过循环拓 (FReT) 算法进行预测,用于步态动力学预测.
  • 与神经网络模型对比,评估FReT的准确性,计算时间和精度.
  • 确定FReT是否适用于辅助设备中的实时应用.

主要方法:

  • FRET算法部署在嵌入式系统上,使用15名受试者的下肢运动传感器数据.
  • 与预训练和代更新的NNET和深度NNET (D-NNET) 模型进行比较.
  • 评估指标包括准确性 (规范化根-平均平方误差),计算时间和精度.

主要成果:

  • FRET显著优于NNET和D-NNET模型,将正常化平方根平均误差降低了近50%.
  • FRET在准确性,计算效率和精度之间取得了卓越的平衡.
  • 该算法证明可以适应不断变化的数据结构,并进行代更新.

结论:

  • 嵌入式系统上的FRET框架为步态动态预测提供了更好的性能.
  • FRET代表了传感器辅助技术在辅助门诊设备中的重大进步.
  • 轻量级和自适应算法对于下一代可穿戴辅助技术至关重要.