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

Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

12.0K
Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
12.0K
Aggregates Classification01:29

Aggregates Classification

310
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
310
Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

239
In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
239
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

105
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
105
Multiple Bar Graph01:07

Multiple Bar Graph

5.1K
As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
5.1K
Convolution Properties I01:20

Convolution Properties I

143
Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
143

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

Longitudinal Association Between Possible Sarcopenia and Stroke Under the AWGS 2025 Criteria: A Nationwide Prospective Cohort Study With a 9-Year Follow-Up.

Geriatrics & gerontology international·2026
Same author

Audiogram Configuration Predicts Treatment Response in Sudden Sensorineural Hearing Loss.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery·2026
Same author

Temperature-responsive PtFe nanowire peroxidase mimetic array for colorimetric discrimination of biogenic amines.

Talanta·2026
Same author

Angiodysplasia as a rare cause of acute hematochezia in a 33-year-old with vascular risk factors: a case report.

Frontiers in medicine·2026
Same author

A machine learning approach for early Parkinson's disease diagnosis based on brain texture features from T1-weighted imaging.

Neurological research·2026
Same author

Sulfur-Doped High-Entropy Spinel Oxide (FeCoNiCuCrAlZn)<sub>3</sub>O<sub>4</sub> Electrocatalyst for Seawater Electrolysis.

ChemSusChem·2026
Same journal

HardFlow: Hard-Constrained Sampling for Flow-Matching Models Via Trajectory Optimization.

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

Industrial Brain: Self-Evolving Neuro-Symbolic Autonomy with Causal Resilience for Cyber-Physical Systems.

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

Adaptive Hardness-Driven Dictionary Distillation for Incomplete Streaming View Clustering.

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

Mixture of Global and Local Experts with Diffusion Transformer for Controllable Face Generation.

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

Task-KV: Task-aware KV Cache Optimization via Semantic Differentiation of Attention Heads.

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

Achieving Text-based Person Retrieval with Any Granularity.

IEEE transactions on pattern analysis and machine intelligence·2026
查看所有相关文章

相关实验视频

Updated: Jun 17, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

380

图形多重卷积和注意力聚合用于图形分类.

Yuhua Xu, Junli Wang, Mingjian Guang

    IEEE transactions on pattern analysis and machine intelligence
    |August 14, 2024
    PubMed
    概括
    此摘要是机器生成的。

    这项研究引入了一种新的图形多重卷积和注意力聚合 (GMCAP) 方法用于图形分类. 通过融合节点特征和在聚合过程中保留图形信息,GMCAP有效地学习图形级别表示.

    更多相关视频

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    496
    Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
    06:37

    Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

    Published on: December 15, 2023

    2.7K

    相关实验视频

    Last Updated: Jun 17, 2025

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    380
    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    496
    Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
    06:37

    Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

    Published on: December 15, 2023

    2.7K

    科学领域:

    • 图形神经网络 图形神经网络
    • 机器学习 机器学习
    • 数据挖掘 数据挖掘

    背景情况:

    • 图形分类对于分析复杂结构化数据至关重要.
    • 现有的方法在聚合过程中扎于节点特征融合和信息丢失.
    • 对多跳的邻里信息的有限关注阻碍了性能.

    研究的目的:

    • 为增强图形分类提出一种新的图形多重卷积和注意力聚合 (GMCAP) 方法.
    • 为了解决节点特征融合和图形聚合中的信息保存方面的局限性.
    • 为了提高学习有效的图表级别表示.

    主要方法:

    • 开发了用于显式节点功能融合的图形多重卷积 (GMConv) 层.
    • 实现了基于重量的聚合模块,以利用多节点邻里信息.
    • 引入了本地信息和基于全球关注的聚合 (LGAPool) 以尽量减少信息丢失.

    主要成果:

    • 与最先进的方法相比,GMCAP显示出更高的性能.
    • 该方法有效地从不同的角度融合了节点特征.
    • LGAPool成功地减少了图形聚合期间的信息丢失.

    结论:

    • GMCAP提供了一种有效的方法来学习图表级别的表示.
    • 拟议的方法提高了图形分类的准确性.
    • 对于分析图形结构数据,GMCAP提供了一个强大的解决方案.