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相关概念视频

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. 
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Boundary Layer Characteristics01:18

Boundary Layer Characteristics

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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...
172
Laminar Flow: Problem Solving01:24

Laminar Flow: Problem Solving

210
Laminar flow occurs when a fluid moves smoothly in parallel layers with minimal mixing and turbulence. In fluid mechanics, ensuring laminar flow within a pipe is essential for precise control of flow characteristics, especially in engineering applications. The key factor in determining whether flow remains laminar is the Reynolds number, a dimensionless quantity that depends on the fluid's velocity, density, viscosity, and the pipe's diameter. A Reynolds number of 2100 or lower...
210
Neural Control of Respiration01:18

Neural Control of Respiration

2.6K
The neural regulation of respiration is a meticulously coordinated process primarily controlled by the respiratory centers located within the brainstem. These centers, composed of specialized neurons, transmit nerve impulses that control the contraction and relaxation of our respiratory muscles.
Respiratory Centers in the Brainstem
Two primary areas comprise the respiratory center: the medullary respiratory center in the medulla oblongata and the pontine respiratory group in the pons. The...
2.6K
Precipitation Processes01:12

Precipitation Processes

484
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...
484
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

371
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...
371

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相关实验视频

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Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites
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Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites

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深度学习框架用于预测航行中的空域排放,考虑时空相关性.

Junqiang Wan1, Honghai Zhang2, Qiqian Zhang2

  • 1College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, PR China; School of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield, Bedford MK43 0AL, UK.

The Science of the total environment
|September 17, 2023
PubMed
概括

准确的空中交通排放预测对于减轻环境影响至关重要. 这项研究引入了一个深度学习模型,结合了图形卷积网络,封闭的循环单元,并关注改善航线空域排放预测.

关键词:
预测空中交通排放量注意力机制注意力机制深度学习是一种深度学习.图表 卷积网络 卷积网络时间空间的相关性.

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Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar
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科学领域:

  • 环境科学 环境科学
  • 计算机科学 计算机科学
  • 航空航天工程 航空航天工程

背景情况:

  • 对航空运输排放的准确预测对于环境影响评估和减缓战略至关重要.
  • 预测空中交通排放需要考虑历史数据中的时间模式和空间影响.
  • 现有的方法可能无法充分捕捉不断发展的航空运输系统中排放的复杂动态.

研究的目的:

  • 开发一种新的深度学习框架,用于准确预测航行中的空域排放.
  • 有效地整合空间和时间特征,以改善排放预测.
  • 为监测空中交通排放提供早期预警指标.

主要方法:

  • 一个三通道深度学习网络,结合了图形卷积网络 (GCN),封闭的反复单元 (GRU) 和注意力机制.
  • GCN提取空间动态,GRU捕捉时间依赖,注意力机制模拟全球时间趋势.
  • 在复杂的空域网络中使用真实世界的空中交通数据集进行评估.

主要成果:

  • 拟议的深度学习框架显著超过现有的最先进的基准.
  • 该模型在各种评估指标和预测视野中显示出卓越的性能.
  • 该框架成功地提取了空间,时间和全球时间动态,以准确预测排放.

结论:

  • 开发的深度学习模型为预测空中交通排放提供了强大而准确的替代方案.
  • 该框架有效地利用公开可用的交通流量数据进行排放预测.
  • 建议扩展指数作为一个有价值的早期预警工具,用于监测空中交通排放的利益相关者.