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

Precipitation Processes01:12

Precipitation Processes

414
The experimental conditions in a gravimetric analysis should be optimized to maximize the particle size and purity of the obtained precipitate. Ideally, the concentration of the precipitating reagent should be low with effective stirring to maintain low relative supersaturation for the growth of large crystals. In homogeneous precipitation, the precipitant is slowly generated by a chemical reaction in the solution to avoid local reagent excesses. For example, urea decomposes gradually to...
414
Precipitation and Co-precipitation01:17

Precipitation and Co-precipitation

1.7K
Precipitation and coprecipitation methods can be used to separate a mixture of ions in a solution. In qualitative inorganic analysis, ions that form sparingly soluble precipitates with the same reagent are separated based on the differences in solubility products. For example, consider the separation of Cu(II) and Fe(II) ions by precipitation as insoluble sulfides. First, copper(II) sulfide is precipitated by the addition of acidic H2S, where the dissociation of H2S is suppressed. Adding H2S...
1.7K
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
Rapidly Varying Flow01:24

Rapidly Varying Flow

49
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...
49
Wind Turbine Machine Models01:24

Wind Turbine Machine Models

99
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...
99
What is Weather?01:07

What is Weather?

18.1K
Overview
18.1K

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

AI for atmosphere-ocean sciences: advancements, challenges and ways forward.

National science review·2026
Same author

Physics-informed deep-learning parameterization of ocean vertical mixing improves climate simulations.

National science review·2022
Same author

Deep-learning-based information mining from ocean remote-sensing imagery.

National science review·2021
Same author

Purely satellite data-driven deep learning forecast of complicated tropical instability waves.

Science advances·2020
Same author

Human and mouse studies establish TBX6 in Mendelian CAKUT and as a potential driver of kidney defects associated with the 16p11.2 microdeletion syndrome.

Kidney international·2020
Same author

A<sub>2A</sub> R inhibition in alleviating spatial recognition memory impairment after TBI is associated with improvement in autophagic flux in RSC.

Journal of cellular and molecular medicine·2020

相关实验视频

Updated: Jun 1, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.3K

提升预测能力:用于预测热带气旋快速加剧的对比学习模型.

Chong Wang1,2,3, Nan Yang1,2,3, Xiaofeng Li1,2,3

  • 1Key Laboratory of Ocean Observation and Forecasting, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266000, China.

Proceedings of the National Academy of Sciences of the United States of America
|January 21, 2025
PubMed
概括

预测快速加剧的热带气旋 (TCs) 通过新的对比学习模型得到了改进. 这种模型提高了预测准确度,并减少了这些危险天气事件的错误报警.

关键词:
深度学习是一种深度学习.快速加剧的强化.热带气旋是一个热带气旋.

更多相关视频

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.6K
Simulating Impacts of Ice Storms on Forest Ecosystems
06:27

Simulating Impacts of Ice Storms on Forest Ecosystems

Published on: June 30, 2020

6.9K

相关实验视频

Last Updated: Jun 1, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.3K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.6K
Simulating Impacts of Ice Storms on Forest Ecosystems
06:27

Simulating Impacts of Ice Storms on Forest Ecosystems

Published on: June 30, 2020

6.9K

科学领域:

  • 气象学和大气科学 气象学和大气科学
  • 地理空间数据科学数据科学
  • 地球科学中的人工智能 地球科学中的人工智能

背景情况:

  • 热带气旋 (TCs) 构成重大威胁,快速强化 (RI) 时期特别具有挑战性,以准确预测.
  • 预测RI TCs (在24小时内强化至少13 m/s) 的现有模型在检测概率 (POD) 和错误报警率 (FARate) 上有局限性.

研究的目的:

  • 开发和评估一种基于对比的新型模型,用于预测热带气旋的快速强化.
  • 通过使用集成数据源来提高RI TC预测的准确性并减少虚假警报.

主要方法:

  • 基于对比的RI TC预测模型 (RITCF-对比) 的开发.
  • 卫星红外图像与大气和海洋数据的整合.
  • 解决样本不平衡问题,并将TC的结构特征纳入模型.

主要成果:

  • 在西北太平洋的1,149个TC周期 (2020-2021) 中,RITCF-contrastive模型实现了92.3%的POD和8.9%的FARate.
  • 与现有的深度学习方法相比,POD有11.7%的改善,FARate减少了三倍.
  • 成功解决了样本不平衡,并纳入了关键的TC结构特征.

结论:

  • 该RITCF-contrastive模型显著提高了快速加剧的热带气旋的预测.
  • 这种方法提供了一种独特而有效的方法来预测危险的天气事件,改进了当前的深度学习技术.