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相关概念视频

Time-Series Graph00:54

Time-Series Graph

4.3K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
4.3K
Absolute Motion Analysis- General Plane Motion01:24

Absolute Motion Analysis- General Plane Motion

218
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...
218
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

98
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
98
Relative Motion Analysis - Velocity01:24

Relative Motion Analysis - Velocity

343
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...
343
Relative Motion Analysis - Acceleration01:10

Relative Motion Analysis - Acceleration

341
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...
341
Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

450
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...
450

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相关实验视频

Updated: Jun 12, 2025

Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders
05:49

Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders

Published on: November 1, 2024

733

人类运动深度神经网络中的模糊同步概率图 时间序列分析

Elham Mottaghi, Mohammad-R Akbarzadeh-T

    IEEE journal of biomedical and health informatics
    |September 25, 2024
    PubMed
    概括

    本研究引入了一种新的模糊同步概率 (FSL) 图形方法来分析生物时间序列,通过捕捉复杂的变量相互作用来提高康复炼评估的可解释性.

    科学领域:

    • 多变量时间序列分析.
    • 生物数据分析 生物数据分析
    • 基于图形的机器学习

    背景情况:

    • 变量交互性是生物时间序列的关键,但图形构造方法通常是昂贵的或忽略数据复杂性.
    • 现有的方法在计算费用,培训需求以及处理非线性和非静止性方面扎.

    研究的目的:

    • 提出一种用于构建图形的新方法,以表示生物多变量时间序列中的可变相互作用.
    • 将这种方法应用于使用人类关节运动数据的自动化康复运动评估.
    • 提高复杂生物系统中决策过程的可解释性.

    主要方法:

    • 使用模糊同步概率 (FSL) 标准构建图形,重点关注定性相似性和变量依赖性.
    • 将FSL构建的图表应用于从康复练习中获得的人体关节运动数据.
    • 扩展了一个深度混合密度神经网络 (DMDN) 具有卷积层来处理FSL图,创建基于FSL图的深度神经网络 (FSLGDN).

    主要成果:

    • FSLGDN模型的性能优于使用线性相关性和人类解剖学进行图形构建的方法.
    • 对关节运动相互作用的基于任务的分析比基于解剖学的图表更有益.
    • 与线性相关联方法相比,FSLGDN模型从非静止运动数据中获取了更多信息.

    更多相关视频

    Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis
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    Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis

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    Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
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    Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

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    相关实验视频

    Last Updated: Jun 12, 2025

    Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders
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    Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders

    Published on: November 1, 2024

    733
    Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis
    05:59

    Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis

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    Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
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    Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

    Published on: July 24, 2019

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    结论:

    • 拟议的FSL图形构造为分析多变量时间序列中的变量依赖提供了更直观和更易于解释的方法.
    • FSLGDN方法为自动化康复运动评估提供了一个强大的工具,为关节动力学提供了更深入的见解.
    • 这种方法通过提供更清晰的特征依赖关系表示来增强决策过程.