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

Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

681
Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
681
Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

4.2K
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...
4.2K
Collisions in Multiple Dimensions: Introduction01:05

Collisions in Multiple Dimensions: Introduction

5.4K
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...
5.4K
Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

28
Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
28
Dimensional Analysis02:19

Dimensional Analysis

15.1K
The concept of dimension is important because every mathematical equation linking physical quantities must be dimensionally consistent, implying that mathematical equations must meet the following two rules. The first rule is that, in an equation, the expressions on each side of the equal sign must have the same dimensions. This is fairly intuitive since we can only add or subtract quantities of the same type (dimension). The second rule states that, in an equation, the arguments of any of the...
15.1K
Manipulation and Analysis01:21

Manipulation and Analysis

28
GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
28

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

Designing Metaphoric 3D Glyph Atlases for Web-Based Information Landscapes.

IEEE computer graphics and applications·2026
Same author

Data-Driven Polyoxometalate Chemistry.

Chemistry (Weinheim an der Bergstrasse, Germany)·2025
Same author

TraSculptor: Visual Analytics for Enhanced Decision-Making in Road Traffic Planning.

IEEE transactions on visualization and computer graphics·2025
Same author

A Large-Scale Sensitivity Analysis on Latent Embeddings and Dimensionality Reductions for Text Spatializations.

IEEE transactions on visualization and computer graphics·2024
Same author

A Survey on Non-Photorealistic Rendering Approaches for Point cloud Visualization.

IEEE transactions on visualization and computer graphics·2024
Same author

Visual Exploration and Analysis of Simulation and Testing Data in Motor Engineering.

IEEE computer graphics and applications·2024

相关实验视频

Updated: Jul 12, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.5K

主题模型的大规模评估和2D文本空间化尺寸缩小方法.

Daniel Atzberger, Tim Cech, Matthias Trapp

    IEEE transactions on visualization and computer graphics
    |October 23, 2023
    PubMed
    概括
    此摘要是机器生成的。

    可解释的主题模型和t-SNE缩小维度可以创建高质量的文本空间化. 本基准评估提供了使用主题建模进行有效文本可视化设计的指导方针.

    更多相关视频

    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

    599
    Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
    07:05

    Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

    Published on: October 27, 2016

    9.3K

    相关实验视频

    Last Updated: Jul 12, 2025

    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
    08:12

    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

    Published on: March 1, 2022

    2.5K
    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

    599
    Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
    07:05

    Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

    Published on: October 27, 2016

    9.3K

    科学领域:

    • 计算语言学计算语言学
    • 数据可视化数据可视化
    • 机器学习是机器学习.

    背景情况:

    • 主题模型揭示了文本体中的语义结构.
    • 主题模型与缩小维度相结合,可以创建文档空间化.
    • 对于高质量的文本空间化的最佳组合是未知的.

    研究的目的:

    • 评估主题模型和缩小文本空间化的维度.
    • 确定有效的组合,以获得准确和感知有效的布局.
    • 为设计文本可视化提供准则.

    主要方法:

    • 使用基准数据集进行大规模计算评估.
    • 测试了主题模型和缩小维度算法的组合.
    • 使用局部/全球属性和感知有效性的指标量化布局质量.

    主要成果:

    • 产生了超过45,000个独特的布局和质量指标.
    • 可解释的主题模型显著改善了体结构的捕获.
    • 推使用t-分布式随机邻居嵌入 (t-SNE) 进行维度缩小.

    结论:

    • 提出了有效的文本空间化设计的指导方针.
    • 可解释的主题模型对于语义结构表示至关重要.
    • t-SNE 是一个非常有效的缩小维度的技术来完成这个任务.