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

Unsoundness of Aggregate due to Volume Change01:26

Unsoundness of Aggregate due to Volume Change

359
Unsoundness in aggregates due to volume changes is primarily caused by the physical alterations aggregates undergo, such as freezing and thawing, thermal changes, and wetting and drying. Unsound aggregates, when subjected to these changes, result in volume change upon disintegration. This, in turn, contributes to the deterioration of concrete, including scaling, pop-outs, and cracking. Particular types of aggregates, such as porous flints, cherts, and those containing clay minerals, are...
359
Random Error01:04

Random Error

8.5K
Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
8.5K
Echo01:06

Echo

889
The human ear cannot distinguish between two sources of sound if they happen to reach within a specific time interval, typically 0.1 seconds apart. More than this, and they are perceived as separate sources.
Imagine the sound is reflected back to the ears. Assuming that the source is very close to the human, the difference between hearing the two sounds—the emitted sound and the reflected sound—may be more than the minimum time for perceiving distinct sounds. If this is the case,...
889
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

8.0K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
8.0K
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

1.8K
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
1.8K
Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

99.9K
Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
99.9K

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

A Sampling-Based Approach for Achieving Desired Patterns of Probabilistic Coverage with Distributed Sensor Networks.

Sensors (Basel, Switzerland)·2023
查看所有相关文章

相关实验视频

Updated: Jan 18, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.1K

聚合对最佳传感器网络配置的影响,与依赖于距离的噪声.

Russell Costa1, Thomas A Wettergren1

  • 1Naval Undersea Warfare Center, Newport, RI 02841, USA.

Sensors (Basel, Switzerland)
|September 13, 2025
PubMed
概括

优化传感器放置以准确定位源是关键. 环境噪声和聚合方法显著影响传感器配置,独特地影响不同的传感器类型.

科学领域:

  • 传感器网络是一个传感器网络.
  • 信号处理 信号处理
  • 优化优化 优化优化

背景情况:

  • 在传感器网络中准确地定位源至关重要.
  • 处理源位置不确定性通常涉及聚合函数.
  • 环境噪音和聚合对传感器放置的综合影响还没有得到充分研究.

研究的目的:

  • 研究环境噪声模型和聚合函数在传感器位置优化中的相互作用.
  • 分析这些因素如何影响不同类型传感器的最佳传感器配置.

主要方法:

  • 集成的以距离为依赖的环境噪声模型.
  • 使用仅轴承和仅距离传感器分析传感器配置.
  • 开发了计算策略,以实现最佳的传感器放置.

主要成果:

  • 最佳的传感器配置在很大程度上取决于在考虑噪声时的聚合方法.
  • 这种依赖性在不同的传感器类型之间有很大的差异 (例如,仅轴承与仅距离).

结论:

  • 环境复杂性和聚合方法对于强大的传感器本地化至关重要.
  • 要设计有效的传感器网络,必须仔细考虑这两个因素.
关键词:
D-优化度是最好的聚合方式 聚合方式 聚合方式分布式传感器网络是一个分布式传感器网络.环境依赖性 环境依赖性最优的规划最优的规划传感器配置 传感器配置源代码本地化 源代码本地化

更多相关视频

Data Acquisition Protocol for Determining Embedded Sensitivity Functions
07:46

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Published on: April 20, 2016

6.5K
Fiber Optic Distributed Sensors for High-resolution Temperature Field Mapping
09:48

Fiber Optic Distributed Sensors for High-resolution Temperature Field Mapping

Published on: November 7, 2016

12.4K

相关实验视频

Last Updated: Jan 18, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.1K
Data Acquisition Protocol for Determining Embedded Sensitivity Functions
07:46

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Published on: April 20, 2016

6.5K
Fiber Optic Distributed Sensors for High-resolution Temperature Field Mapping
09:48

Fiber Optic Distributed Sensors for High-resolution Temperature Field Mapping

Published on: November 7, 2016

12.4K