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During leveling, the Earth's curvature and atmospheric refraction introduce deviations in the line of sight from a true horizontal reference. When the line of sight is leveled, it remains perpendicular to the plumb line only at a single point. Beyond this, it deviates due to the Earth’s curvature, represented by the correction C. For a sight distance D, the deviation can be derived using the relationship:This relationship shows that the deviation increases quadratically with distance. Over a...
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To achieve precise distance measurements, especially in surveying and construction, certain corrections must be applied to account for potential sources of error like the standardization errors, temperature variations, and slope adjustments.Standardization error emerges when measurement equipment undergoes changes, such as wear, repairs, or weather impacts. To address this, surveyors compare the equipment’s readings to a standard. This process identifies any deviation that might lead to...
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If acceleration as a function of time is known, then velocity and position functions can be derived using integral calculus. For constant acceleration, the integral equations refer to the first and second kinematic equations for velocity and position functions, respectively.
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风场校正方法的研究 整合位置信息和代理分歧.

Jianhong Gan1,2,3,4, Mengjia Zhang1,2,3, Cen Gao5

  • 1College of Software Engineering, Chengdu University of Information Technology, Chengdu 610225, China.

Biomimetics (Basel, Switzerland)
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PubMed
概括
此摘要是机器生成的。

我们开发了PPWNet,这是一种深度学习模型,使用稀疏的观测数据进行精确的风场校正. 与传统方法相比,这种基于物理学的方法显著改善了风速和风向预测.

关键词:
注意力机制注意力机制超参数优化超参数优化物理的一致性.点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网位置信息 位置信息风场纠正风场的纠正

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科学领域:

  • 大气科学与气象学
  • 人工智能和机器学习

背景情况:

  • 数字天气预报的准确性受到稀疏的地面风场观测的限制.
  • 当前的校正模型通常依赖于插入的重新分析数据 (例如,ERA5),引入不准确性.
  • 需要使用直接观测数据进行高精度的风场校正.

研究的目的:

  • 提出PPWNet,一个新的深度学习模型,用于准确的风场校正.
  • 为了利用稀疏,离散的观测数据作为基本事实.
  • 将位置信息和物理一致性整合到模型中.

主要方法:

  • PPWNet编码观测点位置,并在损失函数中使用观测值.
  • 一个平行双分支的DenseInception网络提取了多个规模的网格特征.
  • 一个受到PointNet启发的注意力机制,有效地处理稀疏,不规则的观测数据.
  • 基于物理学的方法将学习的物理一致性术语纳入损失函数中.
  • 超参数调整是使用贝叶斯的TPE算法进行的.

主要成果:

  • PPWNet显著优于传统和现有的深度学习方法.
  • 平均绝对误差 (MAE) 减少了38.65%,根平均平方误差 (RMSE) 减少了28.93%.
  • 修正后的风场显示出与观察到的风速和风向的高度一致.

结论:

  • 通过直接利用稀疏的观测数据,PPWNet有效地纠正风场.
  • 整合位置信息和物理定律可以提高基于深度学习的风场校正精度.
  • 该模型为改善数值天气模型初始条件提供了有希望的进步.