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相关概念视频

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

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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...
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Field Application of Global Positioning System01:28

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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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改善移动传感数据收集的设计指南:前性混合方法研究研究

Christopher Slade1,2, Roberto M Benzo3,4, Peter Washington2

  • 1Computer Science Department, Brigham Young University-Hawaii, Laie, HI, United States.

Journal of medical Internet research
|November 18, 2024
PubMed
概括
此摘要是机器生成的。

移动传感应用程序可以改善机器学习模型的数据收集. 对背景事件来说,上下文提示更好,而对生态瞬间评估 (EMA) 建议设置提示.

关键词:
积极收集数据,积极收集数据.学院学院学院学院学院学院学院学院学院学院学院数据的一致性数据的一致性生态瞬间评估 环境瞬间评估移动健康 移动健康 移动健康 移动健康机器学习是机器学习.混合方法是一种混合方法.移动数据移动数据移动健康传感传感器手机电话 手机电话手机电话通过被动收集数据来收集数据.现实世界的设置设置.学生学生的学生学生的学生用户数据用户数据

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科学领域:

  • 移动传感器是一种移动传感器.
  • 机器学习 机器学习
  • 数据收集 数据收集

背景情况:

  • 机器学习模型利用传感器数据流通过生态瞬间评估 (EMA) 进行预测.
  • 在移动数据收集方面仍然存在挑战,特别是获得背景任务和通知的授权.

研究的目的:

  • 调查移动传感应用的挑战,并制定设计准则.
  • 为了比较设置与用于主动数据收集 (EMA) 的上下文提示.
  • 评估计划后台任务与被动数据收集的持续提醒.

主要方法:

  • 开发了iOS和Android移动传感应用程序,与145名大学生进行了为期30天的研究.
  • 通过每日EMA问题测试了积极的数据收集.
  • 通过背景位置事件,持续提醒和计划后台任务评估被动数据收集.

主要成果:

  • 设置和上下文提示在EMA合规性方面没有显著差异 (23.4/30评估完成).
  • 在授权背景事件方面,上下文提示更有效55.5%.
  • 持续的提醒,尽管用户最初的抵抗,完成了226.5%更多的背景会议比计划任务.

结论:

  • 在被动感知中,上下文提示更有效地授权背景事件.
  • 由于在Android上提供更一致的通知,建议EMA使用设置提示.
  • 持续的提醒提供了一种可行的方法来增强用于适应性干预的传感器和用户数据收集.