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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...
11.8K
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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Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

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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,...
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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:
10.2K
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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Kinematic Equations for Rotation01:30

Kinematic Equations for Rotation

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In mechanics, when one observes a rigid body in rotational motion with constant angular acceleration, it is possible to establish equations for its rotational kinematics. This process resembles how linear kinematics are dealt with in simpler motion studies.
For instance, imagine a point A on a rigid body engaged in circular motion. The translational velocity of this particular point can be calculated by taking the time derivatives of the displacement equation, which essentially measures the...
301

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Updated: May 24, 2025

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

Published on: April 11, 2018

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基于知识的深度学习,以节省时间的反向动力学.

Shuhao Ma, Yu Cao, Ian D Robertson

    IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
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    概括
    此摘要是机器生成的。

    这项研究引入了一个新的深度学习框架,用于更快的肌肉骨建模. 它从运动数据中准确预测肌肉激活和力量,帮助神经康复和疾病治疗.

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    科学领域:

    • 生物力学 生物力学
    • 计算生物学 计算生物学
    • 神经科学是一个神经科学.

    背景情况:

    • 准确的肌肉激活和力量估计对于神经康复和肌肉骨疾病治疗至关重要.
    • 计算肌肉骨建模是一个关键的非侵入性工具,但传统的反向动力学方法是计算密集的.
    • 现有的方法通常需要大量的标记数据和大量的计算时间,这限制了它们的临床适用性.

    研究的目的:

    • 开发一个基于知识的深度学习框架,用于反向动态分析.
    • 能够从关节动力学数据中直接预测肌肉激活和力量,而不需要标记训练数据.
    • 提高肌肉骨建模的速度和可访问性,用于临床应用.

    主要方法:

    • 开发了一个基于知识的深度学习框架,使用双向门式循环单元 (BiGRU) 神经网络.
    • 从前进动态和生理标准的物理知识被整合到损失函数中,以指导网络训练.
    • 该模型在健康受试者的上肢和下肢运动数据集上进行了训练和验证.

    主要成果:

    • 拟议的BiGRU模型与其他神经网络架构相比,表现出优越的性能.
    • 该框架实现了时间效率高的逆动态分析,准确预测肌肉激活和力量.
    • 整合先前的物理知识显著提高了模型的有效性和稳定性.

    结论:

    • 开发的基于知识的深度学习框架为肌肉骨模型提供了强大的和高效的解决方案.
    • 这种方法加快了肌肉激活和力量的估计,支持神经康复和肌肉骨疾病治疗的进步.
    • 这些发现强调了将先前的物理知识整合到深度学习中,用于复杂的生物力学分析的潜力.