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関連する概念動画

Types of Global Positioning System Surveys01:30

Types of Global Positioning System Surveys

119
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...
119
Distance Measurements by Taping01:18

Distance Measurements by Taping

97
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...
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Stratified Sampling Method01:16

Stratified Sampling Method

12.8K
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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モバイルクラウド上で,グループベースの真実発見フレームワークで,データストリームを感知

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

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

PloS one
|August 26, 2025
PubMed
まとめ

この研究は,モバイルクラウドセンシング (MCS) データストリームのための新しい2層グループベースの真実発見 (TGTD) フレームワークを導入します. TGTDは,真実の発見を改善することによって,グループベースのMCSアプリケーションのデータ精度を高めます.

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科学分野:

  • コンピュータ科学
  • データサイエンス
  • モバイル・コンピューティング

背景:

  • モバイルクラウドセンシング (MCS) は,さまざまなデータソースによるデータ不一致の問題に直面しています.
  • グループ行動とコミュニティレベルの参加は,MCSアプリケーションにますます影響を与えている.
  • 現存する真実発見 (TD) 方法は,MCSデータストリームのスケールとリアルタイムの要求と戦っています.

研究 の 目的:

  • MCSデータストリームのための新しいフォグ支援グループベースの真実発見フレームワークを提案する.
  • リアルタイム MCS アプリケーションのデータ不一致とグループダイナミクスを解決します.
  • 大規模でダイナミックなMCS環境で真実の発見の正確性と効率性を高める.

主な方法:

  • 2層グループベースの真実発見 (TGTD) メカニズムを開発しました.
  • 信頼性のレベルを使用して参加者の重みを初期化します.
  • MCSのデータストリームをリアルタイムで処理するための霧支援アーキテクチャを統合した.

主要な成果:

  • TGTDフレームワークは,既存のストリーミングTDアプローチと比較して,より優れた真実発見の精度を示しました.
  • 提案された枠組みは合理的な実行期間を維持し,その効率性を証明しました.
  • このフレームワークの有効性は 合成データと実世界のデータセットでの 広範な実験によって検証されました

結論:

  • フォグ支援のグループベースの真実発見フレームワークは,MCSのデータ不一致に対する効果的な解決策を提供します.
  • TGTDはグループダイナミクスとリアルタイム処理の要求をうまく処理します.
  • ストリーミングプロセスの霧アーキテクチャシミュレーションに関するさらなる調査は,将来の作業のために推奨されます.