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

Constraints and Statical Determinacy01:26

Constraints and Statical Determinacy

957
In structural engineering, the equilibrium of a system is not only determined by its equations of equilibrium but also with the help of constraints. Constraints refer to restrictions on the motion of a system. The proper combinations of constraints can minimize the total number of constraints needed to maintain a system in mechanical equilibrium. When this happens, the system is said to be statically determinate. For such systems, the unknown reaction supports can be estimated using equilibrium...
957
How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

37.0K
Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
37.0K
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

43.1K
A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
43.1K
Classification of Skeletal Muscle Fibers01:48

Classification of Skeletal Muscle Fibers

59.4K
Skeletal muscles continuously produce ATP to provide the energy that enables muscle contractions. Skeletal muscle fibers can be categorized into three types based on differences in their contraction speed and how they produce ATP, as well as physical differences related to these factors. Most human muscles contain all three muscle fiber types, albeit in varying proportions.
Slow-Twitch Muscle Fibers
Slow oxidative, muscle fibers appear red due to large numbers of capillaries and high levels of...
59.4K
Data Reporting and Recording01:24

Data Reporting and Recording

5.4K
Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
5.4K
DNA Base Pairing02:27

DNA Base Pairing

33.0K
Erwin Chargaff’s rules on DNA equivalence paved the way for the discovery of base pairing in DNA. Chargaff’s rules state that in a double-stranded DNA molecule,
33.0K

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

[Study on effect of coptidis rhizoma on red blood cells of normal mice and its antioxidant property].

Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China journal of Chinese materia medica·2013
Same author

General framework to histogram-shifting-based reversible data hiding.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2013
Same author

The prevalences of Neisseria gonorrhoeae and Chlamydia trachomatis infections among female sex workers in China.

BMC public health·2013
Same author

[Study on allocation rules of common nutrients in Scutellaria baicalensis in different phenological periods by ICP-OES].

Guang pu xue yu guang pu fen xi = Guang pu·2013
Same author

Sesterterpenoids.

Natural product reports·2013
Same author

14-3-3 sigma is a useful immunohistochemical marker for diagnosing ovarian granulosa cell tumors and steroid cell tumors.

International journal of gynecological pathology : official journal of the International Society of Gynecological Pathologists·2013

相关实验视频

Updated: Jan 24, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.4K

一个基于条件的GAN框架,用于稀疏的sEMG数据增强与肌肉协同先前的约束.

Meiju Li, Zijun Wei, Zhi-Qiang Zhang

    IEEE journal of biomedical and health informatics
    |January 22, 2026
    PubMed
    概括

    这项研究引入了一种新的肌肉协同约束条件GAN (MS-cGAN) 来产生现实的多通道表面电肌图 (sEMG) 信号. 该MS-cGAN框架克服了现有方法的局限性,提高了数据真实性和深度学习模型的性能.

    科学领域:

    • 生物医学工程 生物医学工程
    • 机器学习 机器学习
    • 信号处理 信号处理

    背景情况:

    • 由于伦理和隐私问题,高质量的表面电肌图 (sEMG) 数据很少,这阻碍了深度学习模型的开发.
    • 现有的生成模型在多通道sEMG信号中的通道间相关性和生理学合理性方面扎.
    • 错误积累和缺乏生物忠实性限制了当前sEMG生成技术的临床应用.

    研究的目的:

    • 开发一个新的框架来产生生理上可信的多通道sEMG信号.
    • 解决现有的生成方法的局限性,包括错误积累和道间关系的不充分建模.
    • 为了提高合成sEMG数据的生物忠实性和临床实用性.

    主要方法:

    • 提出了一个肌肉协同受约束的条件生成对抗网络 (MS-cGAN) 框架.
    • 引入了一个基于图形卷积网络 (GCN) 的生成器,以在稀疏的sEMG信号中建模通道间的关系.
    • 综合肌肉协同 (MS) 作为动态损失函数的先前约束,以确保生理学上的可信性.

    主要成果:

    • MS-cGAN成功地产生了多通道的sEMG信号,其真实性和生物机械真实性得到了提高.
    • 基于GCN的发电机有效地捕获复杂的通道间相关性,减轻错误积累.

    更多相关视频

    Watershed Planning within a Quantitative Scenario Analysis Framework
    12:44

    Watershed Planning within a Quantitative Scenario Analysis Framework

    Published on: July 24, 2016

    8.5K
    Author Spotlight: Advancing Biotherapeutic Mass Calculation by Introducing mAbScale, a Python-Based Desktop Application
    04:24

    Author Spotlight: Advancing Biotherapeutic Mass Calculation by Introducing mAbScale, a Python-Based Desktop Application

    Published on: June 16, 2023

    2.3K

    相关实验视频

    Last Updated: Jan 24, 2026

    Constructing and Visualizing Models using Mime-based Machine-learning Framework
    06:19

    Constructing and Visualizing Models using Mime-based Machine-learning Framework

    Published on: July 22, 2025

    2.4K
    Watershed Planning within a Quantitative Scenario Analysis Framework
    12:44

    Watershed Planning within a Quantitative Scenario Analysis Framework

    Published on: July 24, 2016

    8.5K
    Author Spotlight: Advancing Biotherapeutic Mass Calculation by Introducing mAbScale, a Python-Based Desktop Application
    04:24

    Author Spotlight: Advancing Biotherapeutic Mass Calculation by Introducing mAbScale, a Python-Based Desktop Application

    Published on: June 16, 2023

    2.3K
  • 实验表明MS-cGAN在sEMG数据集上的性能优于传统的GAN和扩散模型.
  • 生成的数据显著提高了下游任务性能和动力学预测精度.
  • 结论:

    • 该MS-cGAN框架提供了一个强大的解决方案,用于生成高保真度的合成sEMG数据.
    • 这种方法有效补充了稀缺的sEMG数据集,使得更可靠的深度学习模型培训成为可能.
    • 该方法确保了生理一致性,使得生成的sEMG信号适合临床应用,并提高了预测准确性.