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相关概念视频

Design Example: Traverse Angle Computations01:25

Design Example: Traverse Angle Computations

101
Traverse angle computations are a critical component of surveying, used to compute the internal angles within a closed traverse. A traverse consists of a series of connected lines forming a closed loop, often used for land boundary delineation or mapping. Calculating the internal angles ensures accuracy in the traverse geometry and is essential for checking survey data integrity.The process begins with known azimuths and bearings of the traverse sides. Internal angles at each vertex are...
101
Adjusting a Traverse01:12

Adjusting a Traverse

80
In the site survey of a four-sided traverse, internal angles are essential to ensure geometric accuracy. The survey revealed that the sum of the measured internal angles was 359 degrees and 48 minutes, which is 12 minutes less than the expected 360 degrees. This discrepancy signals an error likely arising from measurement inaccuracies during the fieldwork.To rectify this error, the adjustment process involved distributing the 12-minute shortfall equally across the four internal angles. By...
80
Azimuths and Bearings01:19

Azimuths and Bearings

154
Azimuths and bearings are essential concepts in surveying, providing methods to express the direction of a line relative to a meridian. Azimuths refer to the clockwise angle measured from the north end of a reference meridian to the given line, ranging from zero to 360 degrees. This method gives a comprehensive directional reference within a full 360-degree circle, making it a straightforward way to communicate direction in various fields, including navigation, cartography, and...
154
Common Leveling Mistakes and Errors01:17

Common Leveling Mistakes and Errors

96
A survey team is tasked with determining the elevation difference between points Point A and Point B, separated by uneven terrain. They use a leveling instrument and a leveling rod.Common MistakesMisreading the Rod: During a backsight reading at Point A, the instrumentman observes the rod partially obscured by tall grass. Instead of reading 1.135 m, they mistakenly record 1.735 m due to the misalignment of the crosshair with the wrong graduation. This error adds 0.600 m to all subsequent...
96
Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device01:30

Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device

78
Surveyors use Global Positioning System (GPS) technology to measure the precise location and elevation of points on Earth. In a recent survey, GPS receivers were used to determine the coordinates and elevations of two park monuments. The process involved careful mission planning, data collection, and correction to ensure accuracy. The survey began with mission planning to identify optimal satellite visibility and minimize Position Dilution of Precision (PDOP). A geodetic control point...
78
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

421
Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
421

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相关实验视频

Updated: Jul 18, 2025

Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands
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使用基于决策树的方法同时估计阿齐木斯和高度角.

Anabel Reyes Carballeira1, Felipe A P de Figueiredo1, Jose Marcos C Brito1

  • 1National Institute of Telecommunications INATEL, Av. João de Camargo, 510-Centro, Santa Rita do Sapucaí 37540-000, MG, Brazil.

Sensors (Basel, Switzerland)
|August 26, 2023
PubMed
概括
此摘要是机器生成的。

本研究引入了一个决策树 (DT) 模型,用于使用机器学习 (ML) 精确的到达方向 (DOA) 估计. 与传统方法相比,DT模型显著减少了预测错误和时间.

关键词:
相关性矩阵是一个相关性矩阵.决策树是一个决策树.到达的方向是到达的方向.机器学习是机器学习.音乐 音乐 音乐 音乐

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相关实验视频

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科学领域:

  • 信号处理 信号处理
  • 机器学习 机器学习
  • 天线理论天线理论

背景情况:

  • 对天线阵列系统来说,准确估计信号角度至关重要.
  • 现有的方法,如多重信号分类 (MUSIC),在速度和准确性方面存在局限性.
  • 机器学习为改进到达方向 (DOA) 预测提供了潜力.

研究的目的:

  • 开发和评估一种基于机器学习的方法,用于预测传入信号的度和高度角度.
  • 在不同的条件下评估拟议模型的概括能力.
  • 将拟议模型的性能与最先进的MUSIC算法进行比较.

主要方法:

  • 通过天线阵列接收的信号信息来训练一个决策树 (DT) 模型.
  • 该DT模型的设计是为了同时估计度和高度的角度.
  • 进行了模拟,以测试模型的稳定性和通用性.

主要成果:

  • 基于DT的方法证明了强大的DOA估计,很好地概括到未见的条件.
  • 与MUSIC.相比,拟议的DT模型实现了超过90%的预测误差减少.
  • 使用基于DT的方法,预测时间减少了约50%.

结论:

  • 决策树 (DTs) 为信号接收中DOA估计提供了具有竞争力和高效的替代方案.
  • 提出的基于机器学习的方法在准确性和速度上明显优于MUSIC算法.
  • 这项研究强调了ML在提高天线阵列系统性能方面的潜力.