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相关概念视频

Types of Global Positioning System Surveys01:30

Types of Global Positioning System Surveys

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GPS surveying methods vary in application, accuracy, and data collection techniques, catering to diverse surveying and mapping needs. Static GPS, kinematic GPS, and real-time kinematic (RTK) surveying are widely used. Each technique offers distinct advantages.Static GPS involves placing one receiver at a known reference point and another at the target point. It collects exact positional data by observing multiple satellite ranges over an extended period, achieving centimeter-level accuracy for...
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Distance Measurements by Taping01:18

Distance Measurements by Taping

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Tapes are essential in surveying for accurate, durable, and short-distance measurements. Made from lightweight, nylon-coated steel, they offer flexibility and strength for rugged outdoor use. The nylon coating protects against rust and wear, extending the tape's life. Standard lengths, around 30 meters, are marked in meters and millimeters for precision.Surveyors select tapes based on site conditions and accuracy needs. Lightweight, nylon-coated tapes are commonly used for ease of handling and...
97
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

168
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...
168
Cluster Sampling Method01:20

Cluster Sampling Method

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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Field Application of Global Positioning System01:28

Field Application of Global Positioning System

88
The Global Positioning System (GPS) has become an indispensable tool in fieldwork, offering unparalleled precision and efficiency for surveying, navigation, and infrastructure development. By harnessing signals from a constellation of satellites, GPS receivers determine the location of objects with remarkable speed and accuracy, often completing calculations within a second.Advantages of Modern GPS TechnologyContemporary GPS receivers are designed to meet the practical demands of field...
88
Stratified Sampling Method01:16

Stratified Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
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相关实验视频

Updated: Sep 10, 2025

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
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一个基于团队的真相发现框架通过移动人群传感数据流

Bayan Hashr Saeed Alamri1,2, Muhammad Mostafa Monowar2, Suhair Alshehri2

  • 1Faculty of Cyber Security and Forensic Computing, Prince Mugrin University, Al-Madinah, Saudi Arabia.

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概括

本研究为移动人群传感 (MCS) 数据流引入了一种新的基于两层组的真相发现 (TGTD) 框架. 通过改进真相发现,TGTD提高了基于组的MCS应用中的数据准确性.

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科学领域:

  • 计算机科学
  • 数据科学
  • 移动计算

背景情况:

  • 移动人群传感 (MCS) 面临着由于数据来源不同而导致的数据不一致性挑战.
  • 群体行为和社区层面的参与正在越来越多地影响MCS应用.
  • 现有的真相发现 (TD) 方法难以应对MCS数据流的规模和实时需求.

研究的目的:

  • 为MCS数据流提出一个新的雾辅助集团式真相发现框架.
  • 解决实时MCS应用中的数据不一致性和组动态.
  • 在大规模,动态的MCS环境中提高真相发现的准确性和效率.

主要方法:

  • 开发了一个基于两层组的真相发现 (TGTD) 机制.
  • 使用可信度级别进行初始化参与者权重.
  • 整合了雾辅助架构,用于实时处理MCS数据流.

主要成果:

  • 与现有的流动 TD 方法相比,TGTD 框架显示出更高的真相发现准确性.
  • 提出的框架保持了合理的运行时间,证明了它的效率.
  • 通过对合成和现实数据集的广泛实验, 验证了该框架的有效性.

结论:

  • 基于团队的真相发现框架为MCS中的数据不一致提供了有效的解决方案.
  • TGTD成功地处理了群组动态和实时处理要求.
  • 为未来的工作建议对流动过程的雾架构模拟进行进一步的研究.