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相关概念视频

Taping Over Different Ground Profiles01:12

Taping Over Different Ground Profiles

27
Taping over varying ground profiles requires careful adaptation to achieve accurate measurements. On smooth, level ground with minimal vegetation, the tape can rest directly on the ground. Here, the taping team, typically consisting of a head and a rear tapeman, coordinates their positions with clear communication. The rear tapeman holds the tape at the starting point and guides the head tapeman toward a range pole placed beyond the endpoint, using hand or voice signals to ensure alignment.On...
27
Common Leveling Mistakes and Errors01:17

Common Leveling Mistakes and Errors

75
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...
75
Distance Corrections01:15

Distance Corrections

28
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...
28
Adjusting a Traverse01:12

Adjusting a Traverse

60
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...
60
Design Example: Traverse Angle Computations01:25

Design Example: Traverse Angle Computations

82
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...
82
Influence of Earth's Curvature and Atmospheric Refraction on Leveling01:26

Influence of Earth's Curvature and Atmospheric Refraction on Leveling

105
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.
105

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相关实验视频

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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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基于时间序列的管道地面透雷达校准角度预测算法研究.

Maoxuan Xu1, Feng Yang1, Yuanjin Fang1

  • 1School of Mechanical Electronic and Information Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China.

Sensors (Basel, Switzerland)
|January 23, 2024
PubMed
概括

使用长短期记忆 (LSTM) 的深度学习模型有效地预测了管道机器人的引导轮角度和扭矩. 这提高了探测地下管道缺陷的地面透雷达准确性.

关键词:
偏斜角度预测 偏斜角度预测智能偏斜校正 智能偏斜校正长期短期记忆神经网络的神经网络穿透管道的雷达机器人可以穿透管道.地下空间的安全.

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相关实验视频

Last Updated: Jul 5, 2025

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科学领域:

  • 地质物理学 地质物理学
  • 机器人技术 机器人技术 机器人技术
  • 人工智能的人工智能

背景情况:

  • 地下基础设施的安全依赖于管道内的缺陷的准确检测.
  • 导航地下排水系统的管道机器人由于各种因素而经历姿势偏差.
  • 精确的地面透雷达 (GPR) 天线的空间定位对于缺陷检测至关重要.

研究的目的:

  • 开发一个智能控制系统来纠正管道机器人的姿势.
  • 提出基于时间序列的算法,用于预测导轮校正角度和扭矩.
  • 利用深度学习来提高GPR缺陷定位的准确性.

主要方法:

  • 配备有指导轮的轮式管道机器人被用于姿势纠正.
  • 使用长短期记忆 (LSTM) 深度学习模型来预测校正角度和扭矩.
  • 将LSTM模型的性能与自回归集成移动平均线 (ARIMA) 模型进行了比较.

主要成果:

  • 在LSTM模型中,对角度的平均绝对误差 (MAE) 降低了4.11°,对扭矩降低了8.25N·m.
  • 预测角度和扭矩的平均平方误差 (MSE) 分别下降了10.66%和7.27%.
  • 实验结果显示,平均校正速度为5秒,角误差为±1°.

结论:

  • 基于LSTM的预测模型有效地以高准确度实时纠正管道机器人的姿势.
  • 这种智能态度校正显著提高了GPR天线在定位管道缺陷方面的精度.
  • 拟议的方法提供了一个强大的解决方案,通过先进的机器人GPR系统来改善地下空间安全.