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

Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

3.3K
Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...
3.3K
Frustration and Conflict: Approach-Approach, Approach-Avoidance01:20

Frustration and Conflict: Approach-Approach, Approach-Avoidance

537
Frustration occurs when people are obstructed or prevented from achieving a desired goal or fulfilling a perceived need. For example, when someone's input is ignored in a discussion, it can lead to feelings of frustration. Conflict, however, arises from opposing interests, goals, or actions. Conflicts can take various forms based on the nature of these opposing desires or goals.
One common type of conflict is the Approach–Approach Conflict. In this case, a person faces two desirable...
537
Convenience Sampling Method00:55

Convenience Sampling Method

11.6K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population.
Convenience sampling is a non-random method of sample selection; this method selects individuals that are easily accessible and may result in biased data. For example, a marketing...
11.6K
Sampling Methods: Overview01:06

Sampling Methods: Overview

3.4K
A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
3.4K
Systematic Sampling Method01:17

Systematic Sampling Method

13.3K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
Systematic sampling is one of the simplest methods...
13.3K
Stratified Sampling Method01:16

Stratified Sampling Method

15.4K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
15.4K

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

Accounting for under-reporting in wildlife-vehicle collision hotspot identification using copulas and Bayesian model averaging.

Accident; analysis and prevention·2026
Same author

Preparation of cellulose/zinc oxide/graphene oxide ternary hybrid aerogel to photodestruction of anionic dye from aqueous environment.

Scientific reports·2026
Same author

How long is long enough for traffic conflict observation: An investigation using extreme value theory approaches.

Accident; analysis and prevention·2025
Same author

The effect of data transformation on the severe event prediction in road traffic using extreme value theory.

Accident; analysis and prevention·2025
Same author

Artificial intelligence in endoscopy and colonoscopy: a comprehensive bibliometric analysis of global research trends.

Frontiers in medicine·2025
Same author

Investigating the influence of socioeconomic factors on the relationships between road characteristics and traffic crash frequency and severity-- A hybrid structural equation modelling - artificial neural networks approach.

Accident; analysis and prevention·2025

相关实验视频

Updated: Feb 5, 2026

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

1.0K

哪种方法更好地样本极端的交通冲突? 基于传统与机器学习的采样方法.

Maryam Hasanpour1, Zhankun Chen2, Carmelo D'Agostino2

  • 1Department of Civil Engineering, Toronto Metropolitan University, 350 Victoria Street, Toronto, ON M5B 2K3, Canada.

Accident; analysis and prevention
|February 3, 2026
PubMed
概括

机器学习方法通过更好地识别极端行人与车辆冲突来提高交通安全. 与传统技术相比,这种方法可以提高撞车风险估计.

关键词:
自动编码神经网络的自动编码器.极端价值理论是一个极端价值理论.孤立森林的孤立森林采样技术 采样技术 采样技术交通冲突 交通冲突

更多相关视频

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.5K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

8.1K

相关实验视频

Last Updated: Feb 5, 2026

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

1.0K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.5K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

8.1K

科学领域:

  • 运输工程 运输工程
  • 交通安全分析 交通安全分析
  • 机器学习应用 机器学习应用

背景情况:

  • 极端价值理论用于通过采样极端交通冲突来估计碰撞风险.
  • 目前的方法缺乏标准化程序,用于值选择和严重程度调整评估.

研究的目的:

  • 为了解决传统极端价值理论对碰撞风险采样的局限性.
  • 评估机器学习模型,以改善极端交通冲突的采样.
  • 为了比较基于机器学习的采样与常规方法的严重程度对齐.

主要方法:

  • 研究了自编码神经网络和隔离森林机器学习模型.
  • 利用城市信号交叉路口的车辆与行人冲突数据库.
  • 机器学习采样与传统基线技术进行比较.

主要成果:

  • 机器学习方法产生了极端冲突,这些冲突更好地与概念严重程度水平保持一致.
  • 隔离森林证明了经验尾部分布特征的优越保存.
  • 基于机器学习的采样为极端价值分布建模提供了改进的上下文表示.

结论:

  • 机器学习采样方法,特别是隔离森林,提高了极端交通冲突识别的准确性.
  • 与传统方法相比,这些先进的技术为事故风险评估提供了更强大的方法.
  • 这些发现支持将机器学习整合到交通安全分析中,以进行积极的风险管理.