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

Wind Turbine Machine Models01:24

Wind Turbine Machine Models

109
In the growing field of wind energy, incorporating wind turbine models into transient stability analysis is essential. Induction and synchronous machines are the primary models used, with induction machines being prevalent due to their simplicity and reliability.
Induction machines interact through the rotating magnetic field generated by the stator and the rotor. The key parameter is slip, which is the difference between synchronous speed and rotor speed relative to synchronous speed. Slip is...
109
Prediction Intervals01:03

Prediction Intervals

2.2K
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. 
2.2K
Boundary Layer Characteristics01:18

Boundary Layer Characteristics

57
When a fluid encounters a solid surface, a boundary layer forms due to the interaction between the fluid's motion and the stationary surface. This phenomenon is characterized by a thin region adjacent to the surface where viscous forces dominate, influencing the fluid's velocity profile. The development of the boundary layer begins at the leading edge of the surface and evolves as the fluid moves downstream.As the fluid flows over the surface, friction between the fluid and the wall slows down...
57
Rapidly Varying Flow01:24

Rapidly Varying Flow

53
Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
53
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

285
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
285
Relative Motion Analysis - Velocity01:24

Relative Motion Analysis - Velocity

342
A stroke engine has a slider-crank mechanism that converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider.
When an external force is exerted, it sets the crank into a rotational movement. This, in turn, instigates the motion of the connecting rod, leading to what is referred to as a general plane motion. This process involves two key points - point A on the connecting rod...
342

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

Novel Insights into the Causal Effects of Plasma Protein-to-protein Ratios on Aneurysms and the Mediating Effects of Cardiometabolic Traits.

Current medicinal chemistry·2026
Same author

Association between fluid administration and 28-day mortality in sepsis: a retrospective cohort study.

Scientific reports·2026
Same author

Efficacy and Safety of Omadacycline in Patients with <i>Mycoplasma Pneumoniae</i> Harboring the 23S rRNA A2063G Mutation.

Infection and drug resistance·2026
Same author

Development and validation of a clinical nomogram for predicting 30-day in-hospital mortality in children with moderate-to-severe traumatic brain injury.

Frontiers in pediatrics·2026
Same author

Differentiating Ischemic From Nonischemic T-Wave Inversion Using a Multimodal Vision-Language Model With Reinforcement Learning (ECG-R1): Development and Validation Study.

JMIR medical informatics·2026
Same author

Bidirectional causal relationships between plasma proteins, neuroimaging metrics and risk of Alzheimer's disease.

The journal of prevention of Alzheimer's disease·2026

相关实验视频

Updated: Jun 10, 2025

Measurements of Waves in a Wind-wave Tank Under Steady and Time-varying Wind Forcing
08:54

Measurements of Waves in a Wind-wave Tank Under Steady and Time-varying Wind Forcing

Published on: February 13, 2018

8.6K

基于深度学习的时间相关水平风速预测.

Lintong Li1, Jose Escribano-Macias1, Mingwei Zhang2

  • 1Centre for Transport Engineering and Modelling, Imperial College London, London SW7 2AZ, UK.

Sensors (Basel, Switzerland)
|October 16, 2024
PubMed
概括

使用先进的质量指标 (QI) 和双向长短期记忆 (BiLSTM) 模型进行准确的风速预测,提高了航空安全和清洁能源效率. 新的QA显著提高了比传统方法更准确的预测准确性.

关键词:
这是一个双LSTM.这是LSTM的LSTM.横向的风速预测预测质量指标质量指标时间相关性 时间相关性

更多相关视频

Exploring the Effects of Atmospheric Forcings on Evaporation: Experimental Integration of the Atmospheric Boundary Layer and Shallow Subsurface
13:27

Exploring the Effects of Atmospheric Forcings on Evaporation: Experimental Integration of the Atmospheric Boundary Layer and Shallow Subsurface

Published on: June 8, 2015

8.7K
Investigating the Relationship between Sea Surface Chlorophyll and Major Features of the South China Sea with Satellite Information
10:28

Investigating the Relationship between Sea Surface Chlorophyll and Major Features of the South China Sea with Satellite Information

Published on: June 13, 2020

5.8K

相关实验视频

Last Updated: Jun 10, 2025

Measurements of Waves in a Wind-wave Tank Under Steady and Time-varying Wind Forcing
08:54

Measurements of Waves in a Wind-wave Tank Under Steady and Time-varying Wind Forcing

Published on: February 13, 2018

8.6K
Exploring the Effects of Atmospheric Forcings on Evaporation: Experimental Integration of the Atmospheric Boundary Layer and Shallow Subsurface
13:27

Exploring the Effects of Atmospheric Forcings on Evaporation: Experimental Integration of the Atmospheric Boundary Layer and Shallow Subsurface

Published on: June 8, 2015

8.7K
Investigating the Relationship between Sea Surface Chlorophyll and Major Features of the South China Sea with Satellite Information
10:28

Investigating the Relationship between Sea Surface Chlorophyll and Major Features of the South China Sea with Satellite Information

Published on: June 13, 2020

5.8K

科学领域:

  • 气象学和大气科学 气象学和大气科学
  • 数据科学和机器学习
  • 航空航天工程 航空航天工程
  • 可再生能源系统可再生能源系统

背景情况:

  • 风速是影响航空安全,运营效率和可再生能源生产的关键因素.
  • 准确的风速预测对于减轻事故等风险和优化能源发电至关重要.
  • 现有的风速预测方法通常依赖于传统的质量指标 (QI) 和点智能模型.

研究的目的:

  • 综合审查质量指标 (QI) 与风速的定义,特征,测量传感器和关系.
  • 评估各种QA在预测水平风速方面的特征重要性.
  • 将传统机器学习模型的预测性能与深度学习模型进行比较,特别是双向长期短期记忆 (BiLSTM).

主要方法:

  • 对风速测量和预测相关的质量指标 (QI) 的详细概述.
  • 在风速预测的背景下,对每个QI进行了特征重要性分析.
  • 传统的点式机器学习模型与时间相关的深度学习模型的比较,包括BiLSTM.

主要成果:

  • 双向长短期记忆 (BiLSTM) 神经网络在三个关键指标上在风速预测方面表现出卓越的准确性.
  • 最近提出的一套质量指标 (QI) 在预测任务中显著优于以前使用的质量指标.
  • 与传统模型相比,深度学习模型,特别是BiLSTM,在捕捉时间相关性方面表现更好.

结论:

  • 该研究证实了先进的质量指标 (QI) 和像BiLSTM这样的深度学习模型的有效性,用于准确预测风速.
  • 结果表明,改进的QA和BiLSTM网络可以提高航空安全和风能生产的效率.
  • 这项研究为开发更强大的风速预测系统提供了基础.