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: Method of Components01:08

Vector Algebra: Method of Components

13.4K
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...
13.4K
Vector Representation of Complex Numbers01:16

Vector Representation of Complex Numbers

94
Complex numbers, represented in Cartesian coordinates, can also be visualized as vectors. These vectors can be expressed in polar form, emphasizing their magnitude and angle. When a complex number is input into a function, the output is another complex number, highlighting the function's zero point from which the vector representation can originate.
Consider a function defined as the product of the complex factors in the numerator divided by the product of the complex factors in the...
94
Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

11.5K
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...
11.5K
Vector Components in the Cartesian Coordinate System01:29

Vector Components in the Cartesian Coordinate System

18.7K
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...
18.7K
Divergence and Curl of Electric Field01:25

Divergence and Curl of Electric Field

5.2K
The divergence of a vector is a measure of how much the vector spreads out (diverges) from a point. For example, an electric field vector diverges from the positive charge and converges at the negative charge. The divergence of an electric field is derived using Gauss's law and is equal to the charge density divided by the permittivity of space. Mathematically, it is expressed as
5.2K
Cartesian Form for Vector Formulation01:26

Cartesian Form for Vector Formulation

556
The Cartesian form for vector formulation is a process to calculate  the moment of force using the position and force vectors. The moment of force is defined as the cross-product of these vectors, making it a vector quantity. The Cartesian form of the position and force vectors involves unit vectors, which can be used to express the cross-product in determinant form.
556

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

Medical Referring Image Segmentation via Next-Token Mask Prediction.

IEEE transactions on medical imaging·2026
Same author

A Prospective Trial on Operability and Safety of a Simplified Robot-Assisted System (FASTER) for Colorectal Endoscopic Submucosal Dissection.

Surgical laparoscopy, endoscopy & percutaneous techniques·2026
Same author

GPR15-guided CD8<sup>+</sup> T regulatory cells control intestinal inflammation.

Nature·2026
Same author

Dependencies in heterogeneous, lineage plastic patient-derived prostate cancer organoids revealed through integrated single-cell multiomics and CRISPR screening.

bioRxiv : the preprint server for biology·2026
Same author

CRA5 a high-fidelity compressed reanalysis atmospheric dataset for weather and climate research.

Scientific data·2026
Same author

Comment on "Association of symptoms of neuropsychological long COVID with imaging and plasma biomarkers".

Journal of the neurological sciences·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: May 16, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

466

神经向量场:通过代码书和零曲线规则化对距离向量场进行概括.

Xianghui Yang, Guosheng Lin, Zhenghao Chen

    IEEE transactions on pattern analysis and machine intelligence
    |April 4, 2025
    PubMed
    概括
    此摘要是机器生成的。

    本研究介绍了神经向量场 (NVF),这是一种新的3D表示,将显式网状操纵与隐式无符号距离函数相结合,用于高级表面重建. 此外,NVF可以实现无差异化方向场计算,改善分辨率和拓处理.

    更多相关视频

    Meso-Scale Particle Image Velocimetry Studies of Neurovascular Flows In Vitro
    08:00

    Meso-Scale Particle Image Velocimetry Studies of Neurovascular Flows In Vitro

    Published on: December 3, 2018

    8.3K
    High-speed Particle Image Velocimetry Near Surfaces
    11:59

    High-speed Particle Image Velocimetry Near Surfaces

    Published on: June 24, 2013

    32.9K

    相关实验视频

    Last Updated: May 16, 2025

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
    03:14

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

    Published on: December 6, 2024

    466
    Meso-Scale Particle Image Velocimetry Studies of Neurovascular Flows In Vitro
    08:00

    Meso-Scale Particle Image Velocimetry Studies of Neurovascular Flows In Vitro

    Published on: December 3, 2018

    8.3K
    High-speed Particle Image Velocimetry Near Surfaces
    11:59

    High-speed Particle Image Velocimetry Near Surfaces

    Published on: June 24, 2013

    32.9K

    科学领域:

    • 计算机视觉 计算机视觉
    • 计算机图形 计算机图形
    • 机器学习 机器学习

    背景情况:

    • 基于神经网络的3D表面重建方法通常属于显式模板曲或隐式表面表示类别.
    • 现有的方法通常面临着分辨率,拓学的局限性,并且需要复杂的表面提取过程.

    研究的目的:

    • 提出一种新的3D表示,神经向量场 (NVF),它集成了明确和隐性表面重建技术的优势.
    • 开发一种在3D表面重建中克服分辨率和拓障碍的方法.
    • 引入一种无差异化的方法来计算方向场,简化表面提取.

    主要方法:

    • 介绍了神经向量场 (NVF),通过直接预测来自表面查询的位移,并将形状建模为向量场.
    • 开发了NVF (Lite和Ultra),结合了形状代码书,通过编码交叉对象先验来进行跨类别的重建.
    • 提出了基于NVF的零曲线属性的新型规范化,在NVF (Ultra) 的完全可差分框架中实现.

    主要成果:

    • NVF能够直接预测位移和模拟形状作为矢量场,绕过网络差异化以获得方向场的需求.
    • 拟议的方法有效地编码了距离和方向场,绕过了复杂的表面提取步骤.
    • 在各种场景上评估NVF,包括防水/非防水形状,类别不可知/看不见的重建,类别特定和跨域重建.

    结论:

    • 神经向量场为表面重建提供了强大的和通用的3D表示,结合了明确和隐含的学习优势.
    • 无差异化方向场计算和形状代码集成提高了各种场景的重建能力.
    • 零曲线规则化进一步完善了NVF性能,展示了高级3D形状建模和重建的巨大潜力.