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

Methods of Obtaining Topography01:25

Methods of Obtaining Topography

63
Topography involves measuring and mapping land elevations, natural features, and artificial structures to create accurate representations of the terrain. Topographic surveying relies on traditional and modern methods, each with distinct advantages and limitations.Traditional Surveying Methods:Transit stadia surveys and plane table surveys were widely used traditional surveying methods. These techniques relied on instruments like theodolites and stadia rods for measuring distances and angles,...
63
Common Leveling Mistakes and Errors01:17

Common Leveling Mistakes and Errors

72
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...
72
Topographic Surveying and Contours01:29

Topographic Surveying and Contours

91
Topographic surveying is critical for documenting the Earth's surface, focusing on capturing elevations, slopes, and natural and man-made features. It is essential in construction planning, water resource management, and land-use analysis. The primary outcome of such surveys is a topographic map, which uses contour lines to visually represent the shape and slope of the terrain, providing valuable insights into the landscape's characteristics.Contour lines are fundamental to understanding the...
91
Atomic Force Microscopy01:08

Atomic Force Microscopy

3.4K
Atomic force microscopy (AFM) is a type of scanning probe microscopy that can analyze topographic details of various specimens like ceramics, glass, polymers, and biological samples. AFM offers over 1000 times more resolution than the optical imaging system. Images generated from AFM are three-dimensional surface profiles, offering an advantage over the flat, two-dimensional images from other imaging techniques.
The AFM Probe
The probe is regarded as the heart of any AFM setup and comprises the...
3.4K
Plotting of Topographic Maps01:29

Plotting of Topographic Maps

44
Topographic maps represent the Earth's surface features using contour lines, which connect points of equal elevation to create a two-dimensional representation of three-dimensional terrain. Creating a topographic map requires a systematic approach.Begin by plotting a scaled grid and marking intersections corresponding to the survey's elevation data points. Assign elevation values at these intersections to build the base map. Next, determine contour levels using a consistent contour interval,...
44
Errors and Mistakes in Surveying01:19

Errors and Mistakes in Surveying

76
Errors and mistakes in surveying refer to inaccuracies in measurements and data recording. The errors are deviations from the actual value caused by human sensory limitations, equipment flaws, or environmental effects. These errors are typically unintentional and can result from the inherent imperfections in the instruments used, atmospheric conditions, or the observer’s inability to perceive exact measurements. On the other hand, mistakes are caused by the surveyor's lack of...
76

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

Viscosity Characterization of PDMS and Its Influence on the Performance of a Torsional Vibration Viscous Damper Under Forced Hydrodynamic Loading.

Materials (Basel, Switzerland)·2026
Same author

Epoxy Adhesive Materials as Protective Coatings: Strength Property Analysis Using Machine Learning Algorithms.

Materials (Basel, Switzerland)·2025
Same author

Measurement Technique Comparison in the Entire Fracture Surface Topography Assessment for Additively Manufactured Materials.

Materials (Basel, Switzerland)·2025
Same author

Machine Learning-Driven Prediction of Composite Materials Properties Based on Experimental Testing Data.

Polymers·2025
Same author

Application of Machine Learning to the Prediction of Surface Roughness in the Milling Process on the Basis of Sensor Signals.

Materials (Basel, Switzerland)·2025
Same author

Analytical Approach for Forecasting the Load Capacity of the EN AW-7075-T6 Aluminum Alloy Joints Created Using RFSSW Technology.

Materials (Basel, Switzerland)·2024
Same journal

Correction: Yang et al. Microstructural Characteristics of High-Pressure Die Casting with High Strength-Ductility Synergy Properties: A Review. <i>Materials</i> 2023, <i>16</i>, 1954.

Materials (Basel, Switzerland)·2026
Same journal

Effect of La and Ce Microalloying on the Corrosion Resistance of 0.4Sb Low-Alloy Steel in a Harsh Marine Atmospheric Environment.

Materials (Basel, Switzerland)·2026
Same journal

High-Temperature Properties of Magnesium Ammonium Phosphate Cement Modified with Gold Tailings.

Materials (Basel, Switzerland)·2026
Same journal

A Study on the Evolution of Intermetallic Phase Microstructure and High-Temperature Creep Behavior in Mg-8.0Al-1.0Nd-1.5Gd-Mn Alloys.

Materials (Basel, Switzerland)·2026
Same journal

Material-Driven Clinical Complications in Mechanical Circulatory Support: From Blood-Material Interactions to Device-Related Adverse Events.

Materials (Basel, Switzerland)·2026
Same journal

Influence of Final Irrigation on Calcium Silicate-Based Sealer Dentinal Tubular Penetration: A Systematic Review.

Materials (Basel, Switzerland)·2026
查看所有相关文章

相关实验视频

Updated: Jun 28, 2025

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
09:44

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Published on: October 16, 2018

10.2K

从使用机器学习方法转向表面拓数据评估高频测量错误.

Przemysław Podulka1, Monika Kulisz2, Katarzyna Antosz1

  • 1Faculty of Mechanical Engineering and Aeronautics, Rzeszow University of Technology, 35-959 Rzeszow, Poland.

Materials (Basel, Switzerland)
|April 13, 2024
PubMed
概括
此摘要是机器生成的。

机器学习优化了无接触表面粗度测量的过. 支持矢量机器 (SVM) 准确地识别出最好的过器,在转向表面分析中减少高频噪声.

关键词:
在SVM中,SVM是SVM.人工神经网络的人工神经网络决策树 决策树是一个决定树.高频错误的高频错误是什么机械加工 机械加工 机械加工测量噪声的测量噪声.粗性 粗性 粗性的表面地形图表 表面地形图表转动 转动 转动 转动

更多相关视频

Characterization of Surface Modifications by White Light Interferometry: Applications in Ion Sputtering, Laser Ablation, and Tribology Experiments
11:47

Characterization of Surface Modifications by White Light Interferometry: Applications in Ion Sputtering, Laser Ablation, and Tribology Experiments

Published on: February 27, 2013

15.6K
Quantitative Hardness Measurement by Instrumented AFM-indentation
08:21

Quantitative Hardness Measurement by Instrumented AFM-indentation

Published on: November 22, 2016

9.6K

相关实验视频

Last Updated: Jun 28, 2025

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
09:44

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Published on: October 16, 2018

10.2K
Characterization of Surface Modifications by White Light Interferometry: Applications in Ion Sputtering, Laser Ablation, and Tribology Experiments
11:47

Characterization of Surface Modifications by White Light Interferometry: Applications in Ion Sputtering, Laser Ablation, and Tribology Experiments

Published on: February 27, 2013

15.6K
Quantitative Hardness Measurement by Instrumented AFM-indentation
08:21

Quantitative Hardness Measurement by Instrumented AFM-indentation

Published on: November 22, 2016

9.6K

科学领域:

  • 制造业 工程 制造工程
  • 计量学 计量学 计量学
  • 数据科学数据科学数据科学

背景情况:

  • 表面地形评估对于工业制造控制至关重要.
  • 无接触式测量技术提供速度,但容易受到环境噪音的影响,特别是高频振动.
  • 准确的表面粗度评估对于产品质量和性能至关重要.

研究的目的:

  • 研究机器学习方法,以减少无接触地表地形测量中的高频噪声.
  • 优化数字过技术,以提高翻转表面的表面粗度评估.
  • 为了比较各种机器学习模型在预测最佳过参数方面的有效性.

主要方法:

  • 数字过器 (高斯回归,spline) 的应用,用于无接触地测量转面的粗度.
  • 利用机器学习模型,包括神经网络,支持矢量机 (SVM) 和决策树.
  • 在特定的加工条件下分析表面地形数据,以确定降噪策略.

主要成果:

  • 高斯回归过器和线过器被认为在22.5μm的切断界面对降低噪声最有效.
  • 支持矢量机器 (SVM) 在预测最佳过方法方面表现出卓越的性能.
  • 该研究在确定有效的降噪技术方面取得了高准确度和灵敏度.

结论:

  • 机器学习,特别是SVM,可以通过优化过来显著提高无接触表面粗度测量的准确性.
  • 这些发现为减轻工业表面计量学中振动引起的错误提供了坚实的框架.
  • 使用ML进行优化过,提高了用于制造过程控制的表面地形数据的可靠性.