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

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
Types of Collisions - II01:19

Types of Collisions - II

7.9K
When two or more objects collide with each other, they can stick together to form one single composite object (after collision). The total mass of the object after the collision is the sum of the masses of the original objects, and it moves with a velocity dictated by the conservation of momentum. Although the system's total momentum remains constant, the kinetic energy decreases, and thus such a collision is an inelastic collision. Most of the collisions between objects in daily life are...
7.9K
Elastic Collisions: Case Study01:15

Elastic Collisions: Case Study

14.1K
Elastic collision of a system demands conservation of both momentum and kinetic energy. To solve problems involving one-dimensional elastic collisions between two objects, the equations for conservation of momentum and conservation of internal kinetic energy can be used. For the two objects, the sum of momentum before the collision equals the total momentum after the collision. An elastic collision conserves internal kinetic energy, and so the sum of kinetic energies before the collision equals...
14.1K
Types Of Collisions - I01:04

Types Of Collisions - I

7.3K
When two objects come in direct contact with each other, it is called a collision. During a collision, two or more objects exert forces on each other in a relatively short amount of time. A collision can be categorized as either an elastic or inelastic collision. If two or more objects approach each other, collide and then bounce off, moving away from each other with the same relative speed at which they approached each other, the total kinetic energy of the system is said to be conserved. This...
7.3K
Elastic Collisions: Introduction01:00

Elastic Collisions: Introduction

12.9K
An elastic collision is one that conserves both internal kinetic energy and momentum. Internal kinetic energy is the sum of the kinetic energies of the objects in a system. Truly elastic collisions can only be achieved with subatomic particles, such as electrons striking nuclei. Macroscopic collisions can be very nearly, but not quite, elastic, as some kinetic energy is always converted into other forms of energy such as heat transfer due to friction and sound. An example of a nearly...
12.9K

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

Model-Centric or Data-Centric Approach? A Case Study on the Classification of Surface Defects in Steel Hot Rolling Using Convolutional Neural Networks.

Sensors (Basel, Switzerland)·2026
Same author

Expanding Domain-Specific Datasets with Stable Diffusion Generative Models for Simulating Myocardial Infarction.

International journal of neural systems·2025
Same author

Distinguishing Patient Profiles of Suicidal Ideation and Behavior: The Influence of Repetitive Negative Thinking, Internal and External Entrapment, and Defeat within the Integrated Motivational-Volitional Model in a Suicide Prevention Program.

The Psychiatric quarterly·2025
Same author

Understanding Robot Gesture Perception in Children with Autism Spectrum Disorder during Human-Robot Interaction.

International journal of neural systems·2025
Same author

A ROS2-Based Gateway for Modular Hardware Usage in Heterogeneous Environments.

Sensors (Basel, Switzerland)·2024
Same author

Editorial for the Special Issue on "Feature Papers in Section AI in Imaging".

Journal of imaging·2024
Same journal

Human-AI Interaction in Interventional Radiology: A Narrative Review of Current Applications, Challenges, and Future Directions.

Journal of imaging·2026
Same journal

Coronary Artery Anomalies and Anatomical Variants: Cross-Sectional Diagnostic Imaging and Clinical Background.

Journal of imaging·2026
Same journal

YoLeTooth: A Unified Framework for Joint Tooth Segmentation and Periapical Lesion Detection in Panoramic Radiographs.

Journal of imaging·2026
Same journal

Radiomics-Guided Multi-Sequence Learning for Pathological Complete Response Prediction from Breast MRI with Missing Auxiliary Sequences.

Journal of imaging·2026
Same journal

Cutaneous Thermography in Arthropathies: Quantitative Imaging, Machine Learning, and Clinical Translation.

Journal of imaging·2026
Same journal

Two-Stage Dynamic Synergistic Segmentation Method for Myocardial Pathology.

Journal of imaging·2026
查看所有相关文章

相关实验视频

Updated: Jul 12, 2025

A Standardized Obstacle Course for Assessment of Visual Function in Ultra Low Vision and Artificial Vision
09:29

A Standardized Obstacle Course for Assessment of Visual Function in Ultra Low Vision and Artificial Vision

Published on: February 11, 2014

13.1K

基于视觉的飞行障碍探测,以避免空中碰撞:系统审查

Daniel Vera-Yanez1, António Pereira2,3, Nuno Rodrigues2

  • 1Albacete Research Institute of Informatics, Universidad de Castilla-La Mancha, 02071 Albacete, Spain.

Journal of imaging
|October 27, 2023
PubMed
概括
此摘要是机器生成的。

本文审查了基于计算机视觉的飞行障碍物检测,以避免空中碰撞. 研究表明,由于无人机的可访问性和改进的计算能力,人们越来越感兴趣,但现实世界的测试仍然有限.

关键词:
计算机视觉 计算机视觉在空中碰撞碰撞.障碍物检测 障碍物检测 障碍物检测 障碍物检测系统性审查 系统性审查

更多相关视频

Low-Cost Automated Flight Intercept Trap for the Temporal Sub-Sampling of Flying Insects Attracted to Artificial Light at Night
06:19

Low-Cost Automated Flight Intercept Trap for the Temporal Sub-Sampling of Flying Insects Attracted to Artificial Light at Night

Published on: December 29, 2021

2.6K
Evaluating Flight Performance and Eye Movement Patterns Using Virtual Reality Flight Simulator
03:49

Evaluating Flight Performance and Eye Movement Patterns Using Virtual Reality Flight Simulator

Published on: May 19, 2023

976

相关实验视频

Last Updated: Jul 12, 2025

A Standardized Obstacle Course for Assessment of Visual Function in Ultra Low Vision and Artificial Vision
09:29

A Standardized Obstacle Course for Assessment of Visual Function in Ultra Low Vision and Artificial Vision

Published on: February 11, 2014

13.1K
Low-Cost Automated Flight Intercept Trap for the Temporal Sub-Sampling of Flying Insects Attracted to Artificial Light at Night
06:19

Low-Cost Automated Flight Intercept Trap for the Temporal Sub-Sampling of Flying Insects Attracted to Artificial Light at Night

Published on: December 29, 2021

2.6K
Evaluating Flight Performance and Eye Movement Patterns Using Virtual Reality Flight Simulator
03:49

Evaluating Flight Performance and Eye Movement Patterns Using Virtual Reality Flight Simulator

Published on: May 19, 2023

976

科学领域:

  • 机器人和人工智能 机器人和人工智能
  • 计算机视觉 计算机视觉
  • 航空航天工程 航空航天工程

背景情况:

  • 空中碰撞对航空安全构成重大风险,特别是随着无人机系统的扩散.
  • 有效地检测飞行障碍物对于开发强大的避免碰撞系统至关重要.
  • 计算机视觉为实时障碍物检测提供了一个有前途的传感器模式.

研究的目的:

  • 系统地审查和分析关于基于计算机视觉的飞行障碍物检测的现有文献.
  • 确定飞行器在空中避免碰撞领域的趋势,挑战和研究缺口.
  • 了解促进这一领域研究增长的因素.

主要方法:

  • 在主要的科学数据库 (Scopus,IEEE,ACM,MDPI,Web of Science) 进行了系统的文献搜索,涵盖到2022年的出版物.
  • 选了647个出版物的初始池,其中85个被选择进行深入分析.
  • 审查的重点是与基于计算机视觉的飞行障碍物检测和空中碰撞避免相关的文章.

主要成果:

  • 观察到关于使用计算机视觉检测和跟踪飞行障碍物的出版物显著增加.
  • 这种增长的假设驱动因素包括商用无人机的可用性和单板计算机和计算机视觉库的进步.
  • 大多数审查的算法都在模拟环境中进行了评估,只有26%的人报告了对物理飞行器的测试.

结论:

  • 未来的研究应该优先提高威胁检测算法的成功率.
  • 需要在复杂的现实场景中更多地测试拟议的解决方案,使用物理平台.
  • 解决这些差距对于实际实施有效的空中避免碰撞系统至关重要.