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

Force Classification01:22

Force Classification

2.3K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
2.3K
Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

876
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...
876
Functional Classification of Joints01:09

Functional Classification of Joints

6.5K
Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
An...
6.5K
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

695
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...
695
One-Degree-of-Freedom System01:24

One-Degree-of-Freedom System

801
In mechanical engineering, one-degree-of-freedom systems form the basis of a wide range of electrical and mechanical components. Using these models, engineers can predict the behavior of various parts in a larger system, which gives them insight into how different forces interact with each other.
A one-degree-of-freedom system is defined by an independent variable that determines its state and behavior. One example of a one-degree-of-freedom system is a simple harmonic oscillator, such as a...
801
Absolute Motion Analysis- General Plane Motion01:24

Absolute Motion Analysis- General Plane Motion

531
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...
531

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

Impact of White Noise Therapy on Knee Joint Function and Quality of Life in Patients with Meniscus Injuries.

Noise & health·2026
Same author

Peptide molecular lock-engineered nanobodies enable an oriented dual-modal immunoassay for reliable detection of Cronobacter sakazakii.

Food chemistry·2026
Same author

Nonlinear quantum light source with van der Waals ferroelectric NbOX<sub>2</sub> (X = Br, I).

Nature communications·2026
Same author

The potential of TRPC channel-mediated autophagy in myocardial ischemia-reperfusion injury.

Journal of cardiothoracic surgery·2026
Same author

Study on Residual Strength of Pipelines with Single-Point Uniform Corrosion Defects Under Internal Pressure Loading.

Materials (Basel, Switzerland)·2026
Same author

Global research trends and prognostic risk factors for ST-segment elevation myocardial infarction: a bibliometric analysis from 2003 to 2025.

Journal of cardiothoracic surgery·2026

相关实验视频

Updated: Jan 14, 2026

Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking FLLIT
08:04

Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking FLLIT

Published on: April 23, 2020

7.2K

舞蹈分类使用预训练的深度学习模型与圆形的费尔马特模糊的MARCOS方法集成.

Yanru Wang1

  • 1School of Art, Dance Studies, Wuhan Sports University, Wuhan, 430079, Hubei, China. yanrudance@163.com.

Scientific reports
|October 23, 2025
PubMed
概括

这项研究引入了一个新的混合框架,使用圆形费尔马特模糊集 (CFFS) 来改进自动舞蹈分类. CFF-MARCOS方法提高了模型选择的准确性和决策清晰度,以识别不同的舞蹈风格.

科学领域:

  • 计算机科学 计算机科学
  • 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:

  • 深度学习模型用于通过运动分析进行自动舞蹈分类.
  • 转移学习可以提高跨数据集的识别准确性.
  • 为舞蹈分类选择最佳模型是由于不确定性而带来的挑战.

研究的目的:

  • 通过使用预训练的深度学习模型,提出一种混合框架来识别和分类舞蹈风格.
  • 引入一种新的循环费尔马特模糊测量替代品和基于妥协解决方案 (CFF-MARCOS) 方法的排名.
  • 解决模型选择专家评估中的不确定性和模两可.

主要方法:

  • 一个混合框架,将预训练的深度学习模型与一种新的决策方法相结合.
  • 将圆形费尔马特模糊集合 (CFFS) 集成到MARCOS方法中.
  • 评估使用一个案例研究,使用十个预训练模型,七个标准和三个专家.

主要成果:

  • 拟议的CFF-MARCOS方法在排名预训练模型的舞蹈分类方面表现出优越性.
  • 该框架产生了可靠和可解释的排名,提高了决策可靠性.
  • 在为自动舞蹈分类任务选择最佳模型时,实现了更好的清晰度.
关键词:
圆形费尔马特模糊集是一个模糊集.舞蹈的分类 舞蹈的分类深度学习模型深度学习模型这就是马可斯的方法.采用多个标准的决策.

更多相关视频

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.8K
Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
07:05

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

9.6K

相关实验视频

Last Updated: Jan 14, 2026

Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking FLLIT
08:04

Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking FLLIT

Published on: April 23, 2020

7.2K
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.8K
Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
07:05

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

9.6K

结论:

  • 与CFF-MARCOS的混合框架为自动舞蹈分类提供了更精细的方法.
  • 这种方法有效地处理专家决策中的犹和模两可.
  • 这项研究强调了各种舞蹈风格的自动识别方面的进展.