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

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 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
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

294
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...
294

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

Mechanism of Precipitation Formation and Solubilization Strategy Research in Baihe Gujin Oral Liquid.

Biomedical chromatography : BMC·2026
Same author

Preparation and pharmacological study of <i>Cnidium monnieri</i> volatile oil cream.

Biomedical reports·2026
Same author

Multi-omics reveals the effects and mechanisms of Xinxue Granules against idiopathic pulmonary fibrosis through activating the cAMP/EPAC/CREB axis.

Journal of ethnopharmacology·2026
Same author

Study on the percutaneous absorption and synergistic anti-rheumatoid arthritis effect of the Zanthoxylum bungeanum essential oil on Qin Jiao-Du Huo.

Fitoterapia·2026
Same author

Chinese patent medicine Wenxin Keli: A review on its chemical constituents, pharmacological activities, quality control, and clinical applications.

Journal of ethnopharmacology·2026
Same author

Central corneal thickness, corneal endothelial cell density, and morphology in myopic eyes of young Chinese candidates for refractive surgery.

Frontiers in ophthalmology·2026
Same journal

Granular Ball-Based Noise-Resistant Fuzzy Multineighborhood Feature Selection via Label Enhancement and Feature Graph.

IEEE transactions on neural networks and learning systems·2026
Same journal

Fighting Evolving Spam With ARTMAP Models: A Noise-Resilient Online Detection Framework.

IEEE transactions on neural networks and learning systems·2026
Same journal

HyperSAT: Unsupervised Hypergraph Neural Networks for Weighted MaxSAT Problems.

IEEE transactions on neural networks and learning systems·2026
Same journal

Negation of Basic Belief Assignment in Multisource Information Fusion on Dempster-Shafer Theory With Applications in Pattern Classification.

IEEE transactions on neural networks and learning systems·2026
Same journal

Intervention Feasible Region and Driver Risk Capacity Aware Human-Machine Collaborative Safe Trajectory Planning.

IEEE transactions on neural networks and learning systems·2026
Same journal

A Unified Differential Denoising Learning Framework With a Pre-Trained Model and Fuzzy Graph Networks for Drug-Drug Interaction Prediction.

IEEE transactions on neural networks and learning systems·2026
查看所有相关文章

相关实验视频

Updated: Jun 12, 2025

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
12:39

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

7.6K

图形注意网络用于上下文感知视觉跟踪.

Yanyan Shao, Dongyan Guo, Ying Cui

    IEEE transactions on neural networks and learning systems
    |September 25, 2024
    PubMed
    概括
    此摘要是机器生成的。

    这项研究介绍了SiamGAT*,一种用于对象跟踪的新型上下文感知语图表注意力网络. 它通过适应性匹配目标特征和整合上下文信息来提高追踪精度,优于现有方法.

    更多相关视频

    VisioTracker, an Innovative Automated Approach to Oculomotor Analysis
    05:51

    VisioTracker, an Innovative Automated Approach to Oculomotor Analysis

    Published on: October 12, 2011

    11.0K
    Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior
    07:09

    Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior

    Published on: November 14, 2018

    10.6K

    相关实验视频

    Last Updated: Jun 12, 2025

    A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
    12:39

    A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

    Published on: January 18, 2020

    7.6K
    VisioTracker, an Innovative Automated Approach to Oculomotor Analysis
    05:51

    VisioTracker, an Innovative Automated Approach to Oculomotor Analysis

    Published on: October 12, 2011

    11.0K
    Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior
    07:09

    Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior

    Published on: November 14, 2018

    10.6K

    科学领域:

    • 计算机视觉 计算机视觉
    • 人工智能的人工智能
    • 机器学习 机器学习

    背景情况:

    • 语网络追踪器使用交叉相关性来追踪对象,但与固定的目标特征大小扎,导致背景或前景信息丢失.
    • 现有追踪器中的全球匹配忽视了关键的部分级结构和上下文目标信息.

    研究的目的:

    • 通过提出一个上下文意识的语图表注意力网络 (SiamGAT*) 来解决语追踪器的局限性.
    • 通过实现适应性目标特征匹配和整合上下文信息来增强对象跟踪.

    主要方法:

    • 开发了一个语图表注意力网络 (SiamGAT*),使用完整的双部分图表建立部分对应.
    • 实现了一个图表注意力机制,用于将模板对象信息传播到搜索区域.
    • 引入了基于上下文的特征匹配机制,以整合目标和上下文信息.

    主要成果:

    • 与最先进的追踪器相比,SiamGAT*在具有挑战性的基准指标上表现优越,如GOT-10k,TrackingNet,LaSOT,VOT2020和OTB-100.
    • 拟议的方法适应性地处理对象大小和尺寸比的变化.
    • 在对象跟踪任务中取得了领先的性能.

    结论:

    • 拟议的SiamGAT*有效地克服了传统姆追踪器中固定尺寸特征的局限性.
    • 情境感知特征匹配和图表注意力机制显著提高了跟踪准确性和稳定性.
    • SiamGAT* 代表了对象跟踪技术的重大进步.