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相关概念视频

pV-Diagrams01:18

pV-Diagrams

4.6K
The pV diagram, which is a graph of pressure versus volume of the gas under study, is helpful in describing certain aspects of the substance. When the substance behaves like an ideal gas, the ideal gas equation describes the relationship between its pressure and volume. On a pV diagram, it is common to plot an isotherm, which is a curve showing p as a function of V with the number of molecules and the temperature fixed. Then, for an ideal gas, the product of the pressure of the gas and its...
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Structural Classification of Joints01:20

Structural Classification of Joints

8.0K
Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
8.0K
Virtual Work for a System of Connected Rigid Bodies01:06

Virtual Work for a System of Connected Rigid Bodies

858
Virtual work is a powerful method used to solve problems involving several connected rigid bodies. When the system is in equilibrium, virtual work is zero. This allows the calculation of the resulting forces when a system undergoes a virtual displacement. When attempting to analyze such a system, first, use a free-body diagram, where an independent coordinate represents the configuration of the links, and mark its deflected position resulting from the positive virtual displacement.
Next,...
858
Singularity Functions for Bending Moment01:18

Singularity Functions for Bending Moment

709
Singularity functions simplify the representation of bending moments in beams subjected to discontinuous loading, allowing the use of a single mathematical expression. For a supported beam AB, with uniform loading from its midpoint M to the right side end B, the approach involves conceptual 'cuts' at specific points to determine the bending moment in each segment. By cutting the beam at a point between A and M, the bending moment for the segment before reaching midpoint M is represented using a...
709
State Space Representation01:27

State Space Representation

785
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
785
Bewley Lattice Diagram01:12

Bewley Lattice Diagram

1.6K
The Bewley lattice diagram, developed by L. V. Bewley, effectively organizes the reflections occurring during transmission-line transients. It visually represents how voltage waves propagate and reflect within a transmission line, making it easier to understand the complex interactions that occur.
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相关实验视频

Updated: May 2, 2026

In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy
07:43

In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy

Published on: July 2, 2021

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RKHS-BA:一个强大的无对应多视图捆绑调整框架,用于语义点云.

Ray Zhang, Jingwei Song, Xiang Gao

    IEEE transactions on pattern analysis and machine intelligence
    |July 31, 2025
    PubMed
    概括

    本研究介绍了RKHS-BA,这是一个新的框架,用于使用连续地标表示的强大的3D姿势估计. 它为在充满挑战的环境中使用LiDAR绘图和测距等应用提供了通用的融合.

    科学领域:

    • 机器人技术 机器人技术 机器人技术
    • 计算机视觉 计算机视觉
    • 几何深度学习 几何深度学习

    背景情况:

    • 捆绑调整 (BA) 在3D重建中对准确的姿势估计至关重要.
    • 传统的BA方法与杂的数据和多样化的传感器输入作斗争.
    • 连续表示为更强大和更普遍的地标编码提供了潜力.

    研究的目的:

    • 开发一个新的多框架捆绑调整 (BA) 框架.
    • 为了使用连续的地标表示来实现稳健的姿势估计.
    • 为了证明超越经典的点明智方法的通用收.

    主要方法:

    • 介绍了RKHS-BA,一个利用重现内核希尔伯特空间 (RKHS) 的框架.
    • 采用连续地标表示,编码RGB-D/LiDAR和语义数据.
    • 使用了无对应的姿势图表配方,并使用了通用损失函数.

    主要成果:

    • 在极其杂的场景中实现了非常强大的姿势估计.
    • 在各种语义输入中表现出强大的概括性.
    • 在多视点云注册,测距和LiDAR映射中经过验证的性能.

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    In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy
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    Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

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    结论:

    • RKHS-BA为捆绑调整提供了通用和强大的方法.
    • 该框架显示,在具有挑战性的现实世界的场景中,有了显著的改进.
    • 在RKHS中连续地标表示增强了位数估计的准确性和可靠性.