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相关概念视频

Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

460
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...
460
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

402
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...
402
Absolute Motion Analysis- General Plane Motion01:24

Absolute Motion Analysis- General Plane Motion

219
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...
219
Relative Motion Analysis - Velocity01:24

Relative Motion Analysis - Velocity

359
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...
359
Relative Motion Analysis - Acceleration01:10

Relative Motion Analysis - Acceleration

353
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...
353
Relative Motion Analysis using Rotating Axes - Acceleration01:22

Relative Motion Analysis using Rotating Axes - Acceleration

334
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...
334

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Updated: Jun 29, 2025

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring
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基于修改后的YOLOv8框架的增强实时人类姿势估计方法.

Chengang Dong1, Guodong Du2

  • 1Nanjing University of Aeronautics and Astronautics, Nanjing, 210000, Jiangsu, China.

Scientific reports
|April 5, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了CCAM-Person,这是一种用于实时人类姿势估计 (HPE) 的新型深度学习模型. 它通过改进特征提取和注意力机制来提高准确性,优于对基准数据集的现有方法.

关键词:
注意力机制注意力机制深度学习是一种深度学习.功能金字塔网络的特点是:人类姿势估计估计这就是YOLOv8的意义.

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科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 基于深度学习的人体姿势估计 (HPE) 旨在预测图像/视频中的人体姿势.
  • 实时HPE准确性受到部分封闭和受限模型受感场所的限制.
  • 现有的模型面临特征损失和受感场限制的问题,影响着姿势准确性.

研究的目的:

  • 提出基于YOLOv8框架的新型实时人类姿势估计模型CCAM-Person.
  • 通过解决特征损失和受感场限制,提高HPE的准确性.
  • 为了提高对阻塞和背景噪声的稳定性,以实现更精确的关键点回归.

主要方法:

  • 修改了YOLOv8x-pose模型的骨干和部,以减轻功能损失和受感场问题.
  • 引入了上下文协调注意模块 (CCAM),以加强对突出特征的关注.
  • 在MS COCO 2017和CrowdPose数据集上评估了CCAM-Person模型.

主要成果:

  • CCAM-Person模型在两个数据集的多个指标上展示了竞争性表现.
  • 与YOLOv8x-pose.相比,MS COCO 2017和CrowdPose的平均精度分别提高了2.8%和3.5%,与YOLOv8x-pose.相比.
  • 该CCAM模块有效地减少了背景噪声,并提高了关键点回归精度,特别是肢体阻塞.

结论:

  • 拟议的CCAM-Person模型显著提高了实时人类姿势估计的准确性.
  • 对YOLOv8x-pose框架的改进,特别是CCAM模块,有效地解决了HPE的关键挑战.
  • 这种方法为现实世界HPE应用提供了更强大,更准确的解决方案.