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

End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

325
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...
325
Prediction Intervals01:03

Prediction Intervals

2.3K
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. 
2.3K
Classification of Signals01:30

Classification of Signals

462
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
462
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

106
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
106
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

69
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
69
Force Classification01:22

Force Classification

1.2K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.2K

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

Machine learning-based geospatial assessment of forest structure characteristics and sequestration potential for informed carbon stocks inventories.

Scientific reports·2026
Same author

Packed Fruits and Vegetables Visual Classification and Segmentation Benchmark.

Scientific data·2025
Same author

Feedback between microscopic activity and macroscopic dynamics drives excitability and oscillations in mechanochemical matter.

Physical review. E·2025
Same author

Shape Switching and Tunable Oscillations of Adaptive Droplets.

Physical review letters·2025
Same author

Data-driven uncertainty-aware forecasting of sea ice conditions in the gulf of Ob based on satellite radar imagery.

Scientific reports·2025
Same author

On the practical applicability of DM21 neural-network DFT functional for chemical calculations: Focus on geometry optimization.

The Journal of chemical physics·2025

相关实验视频

Updated: Jul 4, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.4K

使用多式联运数据和深度神经网络方法预测野火的传播.

Dmitrii Shadrin1, Svetlana Illarionova2, Fedor Gubanov1,3

  • 1Skolkovo Institute of Science and Technology, Moscow, Russia, 121205.

Scientific reports
|January 31, 2024
PubMed
概括

这项研究使用人工智能 (AI) 和遥感数据来预测野火在15天内蔓延. 关键因素包括风向和土地覆盖,显示大规模火灾管理的希望.

更多相关视频

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

4.8K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.0K

相关实验视频

Last Updated: Jul 4, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.4K
Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

4.8K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.0K

科学领域:

  • 环境科学 环境科学
  • 地理空间分析是什么
  • 人工智能的人工智能

背景情况:

  • 对于大型国家来说,野火蔓延的预测至关重要,但地面监测是不切实际的.
  • 遥感数据为全球野火监测提供了可行的解决方案.
  • 现有的方法通常集中在使用无人机数据的短期预测上.

研究的目的:

  • 利用地理空间数据和机器学习开发一个有效的管道,用于大规模野火传播预测.
  • 预测火灾在1到5天的时间里蔓延.
  • 为了确定影响野火行为的重要特征.

主要方法:

  • 一个基于MA-Net架构的神经网络模型被训练.
  • 使用了包括空间分布在内的环境和气候数据.
  • 进行了特征重要性分析,以了解促成因素.

主要成果:

  • 该模型在15天内实现了0.640.68的F1得分,用于预测的烧伤区域.
  • 风向和土地覆盖参数被确定为最重要的特征.
  • 这项研究是在俄罗斯北部地区进行的.

结论:

  • 基于地理空间数据的AI方法可以有效地预测大规模野火的蔓延.
  • 马网模型对支持应急系统和决策充满希望.
  • 该方法可适应在其他地区使用.