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

12.1K
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...
12.1K
Vector Components in the Cartesian Coordinate System01:29

Vector Components in the Cartesian Coordinate System

19.7K
Vectors are usually described in terms of their components in a coordinate system. Even in everyday life, we naturally invoke the concept of orthogonal projections in a rectangular coordinate system. For example, if someone gives you directions for a particular location, you will be told to go a few km in a direction like east, west, north, or south, along with the angle in which you are supposed to move. In a rectangular (Cartesian) xy-coordinate system in a plane, a point in a plane is...
19.7K
Vector Operations01:20

Vector Operations

1.3K
Vectors are physical quantities that have both magnitude and direction. The vector operations include addition, subtraction, and scalar multiplication.
A vector multiplied by a scalar value is called scalar multiplication. The result obtained is a new vector with a different magnitude. If the scalar is positive, the direction of the vector remains the same, but if it is negative, the direction of the vector is reversed. For example, the product of the mass and velocity yields the momentum.
1.3K
Vector Algebra: Method of Components01:08

Vector Algebra: Method of Components

13.9K
It is cumbersome to find the magnitudes of vectors using the parallelogram rule or using the graphical method to perform mathematical operations like addition, subtraction, and multiplication. There are two ways to circumvent this algebraic complexity. One way is to draw the vectors to scale, as in navigation, and read approximate vector lengths and angles (directions) from the graphs. The other way is to use the method of components.
In many applications, the magnitudes and directions of...
13.9K
Cartesian Vector Notation01:28

Cartesian Vector Notation

771
Cartesian vector notation is a valuable tool in mechanical engineering for representing vectors in three-dimensional space, performing vector operations such as determining the gradient, divergence, and curl, and expressing physical quantities such as the displacement, velocity, acceleration, and force. By using Cartesian vector notation, engineers can more easily analyze and solve problems in various areas of mechanical engineering, including dynamics, kinematics, and fluid mechanics. This...
771
Statgraphics01:10

Statgraphics

124
Statgraphics is a comprehensive statistical software suite designed for both basic and advanced data analysis. Originating in 1980 at Princeton University under Dr. Neil W. Polhemus, it was one of the pioneering tools for statistical computing on personal computers, with its public release in 1982 marking an early milestone in data science software. Over the years, it has evolved into a robust platform for data science, offering tools for regression analysis, ANOVA, multivariate statistics,...
124

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

UD-Gaussian: Uncertainty-Driven Gaussian Modeling for Occluded Person Re-Identification.

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

ActPrompt: In-Domain Feature Adaptation via Action Cues for Video Temporal Grounding.

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

Active Dataset Distillation via Dual-Space Informative Matching.

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

ASDTracker: Adaptively Sparse Detection With Attention-Guided Refinement for Efficient Multi-Object Tracking.

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

Paramagnetic Properties of Rare Earth Hydroxides, Oxalates, and Dibutyl Phosphates.

ACS omega·2026
Same author

Reliable Pseudo-Supervision for Unsupervised Domain Adaptive Person Search.

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

Relation DETR+: Exploring Explicit Position Relation Prior for Dense Prediction.

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

RBF++: Quantifying and Optimizing Reasoning Boundaries across Measurable and Unmeasurable Capabilities for Chain-of-Thought Reasoning.

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

CAFE: Cross-View Adaptive Fusion and Cluster Center Enhancement for Robust Multi-View Clustering.

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

DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Driving.

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

Ethics-Aware Safe Reinforcement Learning for Rare-Event Risk Control in Interactive Urban Driving.

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

Learning Shape Anchors for Holistic Indoor Scene Understanding.

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 27, 2025

Author Spotlight: Unveiling Plankton Response to Climate Change Through Time-Series Data and Artistic Expression
08:15

Author Spotlight: Unveiling Plankton Response to Climate Change Through Time-Series Data and Artistic Expression

Published on: July 28, 2023

1.2K

层次识别矢量图形和一个新的基于图表的矢量图形数据集.

Shuguang Dou, Xinyang Jiang, Lu Liu

    IEEE transactions on pattern analysis and machine intelligence
    |April 26, 2024
    PubMed
    概括
    此摘要是机器生成的。

    本研究介绍了YOLaT++,一种使用矢量图形进行对象识别的新方法,其性能优于传统的形图形方法. 它有效地分析结构信息,以提高图像识别任务的准确性和效率.

    更多相关视频

    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
    07:35

    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

    Published on: October 11, 2018

    7.5K
    Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
    10:58

    Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

    Published on: January 2, 2011

    10.1K

    相关实验视频

    Last Updated: Jun 27, 2025

    Author Spotlight: Unveiling Plankton Response to Climate Change Through Time-Series Data and Artistic Expression
    08:15

    Author Spotlight: Unveiling Plankton Response to Climate Change Through Time-Series Data and Artistic Expression

    Published on: July 28, 2023

    1.2K
    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
    07:35

    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

    Published on: October 11, 2018

    7.5K
    Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
    10:58

    Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

    Published on: January 2, 2011

    10.1K

    科学领域:

    • 计算机视觉 计算机视觉
    • 机器学习 机器学习
    • 数据科学数据科学数据科学

    背景情况:

    • 传统的图像识别依赖于形图形,这些图形在缩放过程中面临着别名和信息丢失等局限性.
    • 现有的方法与矢量图形中存在的固有结构和层次信息作斗争.

    研究的目的:

    • 提出一种新的对象检测和分类方法,利用矢量图形数据.
    • 通过结合多级特征学习来增强现有的矢量图形识别方法.
    • 引入一个全面的数据集,用于矢量图形的检测和理解.

    主要方法:

    • 开发了YOLaT (You Only Look at Text),一种使用图形神经网络 (GNN) 处理矢量图形的文本表示的方法.
    • 介绍了YOLaT++用于多级抽象特征学习,分析原始的形状,曲线和点.
    • 创建了VG-DCU数据集,包括基于图表的矢量图形,格对应物和注释.

    主要成果:

    • 与基于矢量图形和形图形的对象检测方法相比,YOLaT系列的性能优越.
    • 在具有挑战性的VG-DCU数据集上实现了更高的准确性和效率.
    • 验证了多级特征学习在矢量图形识别中的有效性.

    结论:

    • 矢量图形为推进图像识别任务提供了巨大的潜力.
    • YOLaT++为使用矢量图形进行对象检测和分类提供了一个强大的框架.
    • VG-DCU数据集促进了对矢量图形理解的进一步研究.