Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

386
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
386
Velocity and Position by Graphical Method01:34

Velocity and Position by Graphical Method

7.5K
Velocity and position can be calculated from the known function of acceleration as a function of time. The total area under the acceleration-time graph and the velocity-time graph gives the change in velocity and position, respectively. In the case of an airplane, its acceleration is tracked using the inertial navigation system. The pilot provides the input of the airplane's initial position and velocity before takeoff. The inertial navigation system then uses the acceleration data to...
7.5K
Relative Velocity in Two Dimensions01:11

Relative Velocity in Two Dimensions

7.3K
Relative velocity is the velocity of an object as observed from a particular reference frame, or the velocity of one reference frame with respect to another reference frame. The concept of relative velocity can be used to describe motion in two dimensions. Consider a particle P and two reference frames S and S′. The position of the origin of S′ as measured in S is , the position of P as measured in S′ is , and the position of P as measured in S is , which can be evaluated by...
7.3K
Planar Rigid-Body Motion01:22

Planar Rigid-Body Motion

474
Understanding the movement of a rigid body in planar motion involves recognizing that every particle within this body is traversing a path that maintains a consistent distance from a specific plane. This concept is fundamental in the study of physics and mechanical engineering, and it allows us to comprehend better how objects move in space.
Planar motion is typically divided into three distinct categories. The first is rectilinear translation, demonstrated by a subway train that moves along...
474
Absolute Motion Analysis- General Plane Motion01:24

Absolute Motion Analysis- General Plane Motion

241
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...
241
Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

486
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...
486

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Cross-Level Topological Framework: Learning Explainable Region-Channel Representations from EEG Signals for Emotional Decoding.

IEEE journal of biomedical and health informatics·2026
Same author

Efficient, Robust, and Anti-Collusion Fingerprinting of Image Diffusion Models.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Single-Image Reflection Removal via Iterative Prompt Learning of Reflection Level.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

High-fat diet-induced obesity impairs endothelium-dependent relaxation in rabbits: association with MLCK upregulation and partial <i>ex vivo</i> improvement by ML-7.

Frontiers in cardiovascular medicine·2026
Same author

Hydrophobic Phenolic/Silica Hybrid Aerogels for Thermal Insulation: Effect of Methyl Modification Method.

Gels (Basel, Switzerland)·2026
Same author

Texture-Consistent 3D Scene Style Transfer via Transformer-Guided Neural Radiance Fields.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2025

相关实验视频

Updated: Jul 21, 2025

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

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.6K

STGlow:一个基于流量的生成框架,具有用于行人轨迹预测的双图形仪.

Rongqin Liang, Yuanman Li, Jiantao Zhou

    IEEE transactions on neural networks and learning systems
    |July 26, 2023
    PubMed
    概括

    我们介绍STGlow,一种用于行人轨迹预测的新型生成流框架. 这种方法通过优化精确的日志概率来准确地模拟行人运动,优于现有的生成对抗网络和条件变化自动编码器.

    科学领域:

    • 计算机视觉 计算机视觉
    • 人工智能的人工智能
    • 机器人技术 机器人技术 机器人技术

    背景情况:

    • 对于自动驾驶和机器人导航等智能系统来说,行人轨迹预测至关重要.
    • 由于各种运动行为和复杂的社会相互作用,准确的预测是具有挑战性的.
    • 像GAN和CVAE这样的现有方法在建模数据分布方面存在局限性,导致偏差或不准确的轨迹.

    研究的目的:

    • 提出一种新的基于生成流的框架,STGlow,用于更准确的行人轨迹预测.
    • 解决现有的生成模型在准确捕捉底层数据分布方面的局限性.
    • 增强步行者之间的时间依赖和空间相互作用的建模.

    主要方法:

    • 开发了一个基于流量的生成框架 (STGlow),可以优化运动行为的确切日志概率.
    • 实施了前进过程来简化复杂的行为和反向过程来演变简单的行为.
    • 引入了一个带有图形结构的双图形制造器来建模时间依赖和空间相互作用.

    主要成果:

    • STGlow精确地模拟了行人运动的底层数据分布.
    • 该框架为模拟运动演变提供了清晰的物理解释.
    • 对基准的实验结果显示,与最先进的方法相比,性能显著提高.

    更多相关视频

    A Protocol for Real-time 3D Single Particle Tracking
    10:16

    A Protocol for Real-time 3D Single Particle Tracking

    Published on: January 3, 2018

    15.0K
    Motor Dual-Tasks for Gait Analysis and Evaluation in Post-Stroke Patients
    05:23

    Motor Dual-Tasks for Gait Analysis and Evaluation in Post-Stroke Patients

    Published on: March 11, 2021

    2.4K

    相关实验视频

    Last Updated: Jul 21, 2025

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

    Trajectory Data Analyses for Pedestrian Space-time Activity Study

    Published on: February 25, 2013

    13.6K
    A Protocol for Real-time 3D Single Particle Tracking
    10:16

    A Protocol for Real-time 3D Single Particle Tracking

    Published on: January 3, 2018

    15.0K
    Motor Dual-Tasks for Gait Analysis and Evaluation in Post-Stroke Patients
    05:23

    Motor Dual-Tasks for Gait Analysis and Evaluation in Post-Stroke Patients

    Published on: March 11, 2021

    2.4K

    结论:

    • STGlow为行人轨迹预测提供了更准确,更强大的方法.
    • 生成流和双图形有效地捕捉复杂的运动动态和相互作用.
    • 这一进步对智能系统的安全性和效率产生了重大影响.