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

Application of Integration: Problem Solving01:30

Application of Integration: Problem Solving

30
The process of breathing involves the periodic intake and expulsion of air, known as the respiratory cycle, which typically lasts about five seconds. Modeling the volume of air inhaled into the lungs as a function of time provides insight into both the dynamics and efficiency of pulmonary ventilation. This volume is determined by integrating the airflow rate over time, which captures the cumulative effect of air entering the lungs.Sinusoidal Model of AirflowAirflow during respiration is not...
30
Factors Affecting Pulmonary Ventilation01:19

Factors Affecting Pulmonary Ventilation

2.8K
Besides the pressure difference between the external environment and the lungs, the airflow rate and ease of pulmonary ventilation are also influenced by three other factors: surface tension of the fluid in the alveoli, compliance of the lungs, and airway resistance.
Alveolar Surface Tension
The alveolar fluid lines the luminal surface of the alveoli and exerts a force called surface tension. This force is caused by the polar water molecules in the liquid being more strongly attracted to each...
2.8K
Prediction Intervals01:03

Prediction Intervals

3.2K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
3.2K
Effects of feedback01:24

Effects of feedback

984
Feedback in control systems plays a critical role in shaping various operational parameters, extending beyond simple error reduction to influence stability, bandwidth, gain, impedance, and sensitivity. Understanding these effects requires examining a basic feedback system characterized by defined input, output, error, and feedback signals.
Feedback significantly modifies the gain of a control system. The gain of a system without feedback is altered by a factor of one plus GH, where G represents...
984
Variation of Atmospheric Pressure01:18

Variation of Atmospheric Pressure

3.9K
Change in atmospheric pressure with height is particularly interesting. The decrease in atmospheric pressure with increasing altitude is due to the decreasing gravitational force per unit area as we move away from the surface of the earth.
Assuming the air temperature is constant at a given altitude and that the ideal gas law of thermodynamics describes the atmosphere to a good approximation, one can find the variation of atmospheric pressure with height.
Let p(y) be the atmospheric pressure at...
3.9K

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

Solving multi-depot closed-path multiple traveling salesman problem using k-means++ hierarchical clustering and neural combinatorial networks.

Scientific reports·2026
Same author

Machine Learning for Predicting Coronary Heart Disease Risk in Patients with Hypertension: An Ensemble Modeling Approach.

Healthcare informatics research·2026
Same author

MixKNet: A Modified U-shaped Network with Hybrid Channel Convolution for Medical Image Segmentation.

Journal of visualized experiments : JoVE·2025
Same author

A transformer-based structure-aware model for tackling the traveling salesman problem.

PloS one·2025
Same author

Leveraging transfer learning with deep learning for crime prediction.

PloS one·2024
Same author

Promotion and sustainable development of beef cattle farming industry in agro-pasture ecotone areas, Inner Mongolia of China: A comparison between two fattening systems.

Heliyon·2023

相关实验视频

Updated: Jan 12, 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

909

DF-OSELM:一个动态反功能学习模型,用于在线空气质量预测.

Yujie Liu1, Fadratul Hafinaz Hassan2, Li-Pei Wong1

  • 1School of Computer Sciences, Universiti Sains Malaysia, 11800, Gelugor, Pulau Pinang, Malaysia.

Environmental monitoring and assessment
|October 31, 2025
PubMed
概括

这项研究引入了一个新的动态反功能学习在线序列极端学习机器 (DF-OSELM),用于准确的实时空气质量预测. 该模型显著提高了PM2.5.5等污染物的预测性能和效率.

关键词:
空气污染 大气污染空气质量数据预测极端学习机器自编码器自编码器在线连续极端学习机器.经常性的神经网络.

相关实验视频

Last Updated: Jan 12, 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

909

科学领域:

  • 环境科学 环境科学
  • 数据科学数据科学数据科学
  • 机器学习 机器学习

背景情况:

  • 准确的空气质量预测对于公共卫生和污染风险减轻至关重要.
  • 现有的模型在适应性,计算速度和解释性方面扎.

研究的目的:

  • 开发一个先进的在线顺序极端学习机器,用于实时空气质量预测.
  • 提高模型的适应性,效率和可解释性.

主要方法:

  • 提出了一个动态反功能学习在线顺序极端学习机器 (DF-OSELM).
  • 集成的双重极端学习机器自动编码器 (ELM-AEs),一个规范化层和一个反复反机制.
  • 在线训练模型使用每小时10,000个空气质量样本 (PM2,PM10,SO2,NO2).

主要成果:

  • DF-OSELM实现了卓越的预测性能 (NRMSE < 0.1,R2 > 0.99),表现优于基线模型.
  • 废除研究证实了正常化和双自编码机制的重要性.
  • 不确定性量化提供了可靠的置信区间,SHAP分析确定了关键预测因素.

结论:

  • DF-OSELM为实时空气质量监测提供了精度,效率 (更新时间<3ms) 和可解释性的平衡方法.
  • 该模型适用于大型环境平台和风险评估.