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相关概念视频

Relative Motion Analysis - Velocity01:24

Relative Motion Analysis - Velocity

394
A stroke engine has a slider-crank mechanism that converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider.
When an external force is exerted, it sets the crank into a rotational movement. This, in turn, instigates the motion of the connecting rod, leading to what is referred to as a general plane motion. This process involves two key points - point A on the connecting rod...
394
Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

489
Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
However, to express the relative position of point B relative to point A, an additional frame of reference, denoted as x'y', is necessary. This additional frame not only translates but also rotates relative to the fixed frame, making it...
489
Absolute Motion Analysis- General Plane Motion01:24

Absolute Motion Analysis- General Plane Motion

243
Visualize a drone, with its propellers spinning rapidly, hovering mid-air. The fascinating movements and operations of this drone can be comprehended by applying the principle of general plane motion.
As the drone's propellers rotate, an upward force is generated that counteracts the force of gravity, enabling the drone to lift off from the ground. This initial movement of the drone is along a straight path, representing a form of translational motion. In this phase, every point on the...
243
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

424
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...
424
Relative Motion Analysis - Acceleration01:10

Relative Motion Analysis - Acceleration

382
A slider-crank mechanism converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider. The movement of the slider-crank is an example of general plane motion as the fluctuating angle between the crank and the connecting rod. Consider a segment AB where point A is at the end of the slider and point B is on the diametrically opposite end to point A, on a crack. The variance in...
382
Relative Motion Analysis using Rotating Axes - Acceleration01:22

Relative Motion Analysis using Rotating Axes - Acceleration

357
Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame. The absolute velocity of point B is determined by adding the absolute velocity of point A, the relative velocity of point B in the rotating frame, and the effects caused by the angular velocity within the rotating frame.
Time differentiation is...
357

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相关实验视频

Updated: Jul 23, 2025

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

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Published on: February 25, 2013

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基于流动的时空结构预测运动动态.

Mohsen Zand, Ali Etemad, Michael Greenspan

    IEEE transactions on pattern analysis and machine intelligence
    |July 18, 2023
    PubMed
    概括

    MotionFlow引入了条件规范流 (CNF) 来建模复杂的时空数据. 这种新的方法有效地捕捉了各种预测任务的高维数据集的变化.

    科学领域:

    • 机器学习 机器学习
    • 生成型模型 生成型模型
    • 时间空间数据分析.

    背景情况:

    • 条件规范化流 (CNF) 对复杂分布具有强大作用,但对多变量时空数据未得到充分探索.
    • 现有的方法可能无法完全捕捉高维度,时间变化的数据集中的复杂依赖关系.

    研究的目的:

    • 介绍MotionFlow,一种用于建模多变量时空数据的新型条件规范流 (CNF) 方法.
    • 调查CNF在捕捉复杂的时间动态和维度间相关性的有效性.
    • 开发一个概率神经生成模型,用于结构化输出学习在时间依赖的场景.

    主要方法:

    • 开发了MotionFlow,一种自回归的规范化流量模型,根据时空输入特征对输出分布进行条件化.
    • 在CNF中结合确定性和随机性表示,用于概率模型.
    • 利用条件先验来对潜在空间进行因数分解,用于时间依赖的建模.
    • 在CNF框架内,使用掩面卷曲作为自回归条件.

    主要成果:

    • 演示了MotionFlow模拟复杂的依赖时间的条件分布的能力.
    • 成功地将该方法应用于各种任务,包括轨迹预测,运动预测,时间序列预测和二进制细分.
    • 展示了模型在时空数据中处理高维度和大尺寸间相关性的能力.

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    结论:

    • 在多变量预测任务中,MotionFlow有效地利用规范化流程来学习复杂的依赖时间的条件分布.
    • 拟议的方法提供了一个灵活和富有表现力的概率方法,用于结构化的输出学习与时空数据.
    • 这项工作为将先进的生成模型应用于复杂的现实世界动态开辟了新的途径.