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

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

129
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...
129
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

182
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
182
Cross Product01:25

Cross Product

280
The cross product is a fundamental concept in vector algebra that is a vector operation on two different vectors to obtain a third vector. Unlike the scalar product, the cross product results in a vector quantity perpendicular to both the original vectors.
The magnitude of the cross product is obtained by multiplying the magnitude of both the vectors and the sine of the angle between them. This means that a larger angle between the vectors will lead to a greater magnitude of the cross product.
280
Cross-Sectional Research01:50

Cross-Sectional Research

11.4K
In cross-sectional research, a researcher compares multiple segments of the population at the same time. If they were interested in people's dietary habits, the researcher might directly compare different groups of people by age. Instead of following a group of people for 20 years to see how their dietary habits changed from decade to decade, the researcher would study a group of 20-year-old individuals and compare them to a group of 30-year-old individuals and a group of 40-year-old...
11.4K
Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

4.3K
In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
4.3K
Collisions in Multiple Dimensions: Introduction01:05

Collisions in Multiple Dimensions: Introduction

5.5K
It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a...
5.5K

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

RoLiC: A Robust LiDAR-Camera Fusion Framework for 3D Object Detection.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

The effect of customer incivility on proactive customer service performance mediated by emotional exhaustion and moderated by proactive personality.

Scientific reports·2026
Same author

Omnidirectional dual-femtosecond absolute ranging for high-precision multi-target coordinate measurement.

Optics express·2026
Same author

The identification of metabolites from gut microbiota in HPV infection via network pharmacology.

PloS one·2026
Same author

A Triple Strategy to Enhance Energy Storage and Power Generation Performances of a Rechargeable Zn-H<sub>2</sub>O Fuel Cell.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same author

Sparse Variational Information Bottleneck Gaussian Processes for Uncertainty Estimation.

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

Hidden Data Recovery and Forecasting via Next-Generation Reservoir Computing With Multiscale Delay Selection.

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

CAFF-CIL: Causality-Aware Freedom Forgetting Approach for Class-Incremental Learning.

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

Harmonic Autoencoding Framework for Multiple Tasks in Magnetic Particle Imaging Reconstruction.

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

A Survey on Human-Centric Voice-Face Multimodal Learning.

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

Vision-Assisted Foundation Model for Solving Multitask Vehicle Routing Problems.

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

FP3O: Enabling Proximal Policy Optimization in Multiagent Cooperation With Parameter-Sharing Versatility.

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

相关实验视频

Updated: Jul 19, 2025

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.0K

部分多视图表示学习与交叉视图生成

Wenbo Dong, Shiliang Sun

    IEEE transactions on neural networks and learning systems
    |August 16, 2023
    PubMed
    概括
    此摘要是机器生成的。

    本研究引入了一种新的深度学习模型,PMvCG,通过重建缺失的视图和学习全面的表示来有效处理不完整的多视图数据. PMvCG 提高了集群和分类等任务的性能.

    更多相关视频

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

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    442
    Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
    07:13

    Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

    Published on: October 27, 2023

    1.2K

    相关实验视频

    Last Updated: Jul 19, 2025

    Cross-Modal Multivariate Pattern Analysis
    13:51

    Cross-Modal Multivariate Pattern Analysis

    Published on: November 9, 2011

    20.0K
    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    442
    Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
    07:13

    Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

    Published on: October 27, 2023

    1.2K

    科学领域:

    • 机器学习 机器学习
    • 人工智能的人工智能
    • 数据科学数据科学数据科学

    背景情况:

    • 多视图学习算法通常假定完整的数据,这在实践中是不现实的.
    • 部分多视图数据在建模相互视图依赖性以获得一致性和互补性方面存在挑战.
    • 现有的方法与不完整的多视图数据集的固有复杂性作斗争.

    研究的目的:

    • 为部分多视图数据提出深度高斯交叉视图生成模型 (PMvCG).
    • 通过解决一致性和互补原则来学习全面的数据表示.
    • 为了重建缺失的观点,并整合不确定性,以提高绩效.

    主要方法:

    • 开发了一个深度高斯交叉视图生成模型 (PMvCG).
    • 在模型训练中使用变异推理和代优化.
    • 重建了缺失的视图,并将估计的不确定性纳入了表示.

    主要成果:

    • 在部分多视图数据中,PMvCG有效地建模了依赖关系.
    • 重建的视图被用来进一步优化模型.
    • 该模型取得了有希望的结果,在聚类和分类任务中表现优于比较方法.

    结论:

    • 对于处理部分多视图数据,PMvCG提供了一个强大的解决方案.
    • 该模型通过利用视图共享和视图特定功能,成功地学习了全面的表示.
    • 实验验证证证实了PMvCG在各种环境中的有效性和优越性.