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相关概念视频

Weighted Mean00:57

Weighted Mean

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While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
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Distance Measurements by Taping01:18

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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...
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Difference from Background: Limit of Detection01:05

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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.
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Calibration Curves: Linear Least Squares01:20

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A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
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In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
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Apparent Weight01:09

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True weight is the measure of the gravitational force acting on an object. However, if the object accelerates, its measured weight is different from its true weight. Similar observations can be made when the object is submerged in water. An object's weight in water is its apparent weight, which is equal to the difference between its true weight and the buoyant forces.
Consider a person standing on a bathroom scale inside an elevator. If the scale is accurate at rest, its reading equals the...
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相关实验视频

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A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
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基于改进的YOLOv5的轻量计指针识别方法.

Chi Zhang1, Kai Wang1, Jie Zhang2

  • 1School of Artificial Intelligence and Big Data, Hefei University, Hefei 230601, China.

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

这项研究介绍了YOLOv5-MRL,一种轻量级的物体检测模型,用于变电站的自动计量器读取. 它实现了高精度和速度,克服了实时监控传统模型的局限性.

关键词:
深度学习是一种深度学习.计量器的读数可以读取.对象检测检测对象检测对象检测变电站的巡逻队正在巡逻.

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Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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科学领域:

  • 计算机视觉 计算机视觉
  • 电气工程 电气工程
  • 人工智能的人工智能

背景情况:

  • 经典的物体检测模型对于变电站监控硬件来说太大,速度太慢.
  • 现有的轻量级模型难以平衡计量器读数的准确性和实时性能.
  • 变电站自动计量器读数需要高效准确的物体检测.

研究的目的:

  • 开发一个轻量级的物体检测算法 (YOLOv5-MRL) 用于变电站计数读数.
  • 提高检测速度和准确性,以便部署在变电站监控设备上.
  • 通过新的外部圆圈配件和圆角算法来促进表盘读取.

主要方法:

  • 通过提高YOLOv5的速度和精度,构建了一个轻量级的YOLOv5-MRL算法.
  • 应用卷积内核通道软修剪,以减少YOLOv5-MRL模型参数.
  • 开发了一种表盘外圈装配方法,用于表盘读取的圆形角度算法.

主要成果:

  • YOLOv5-MRL的平均精度达到了96.9%.
  • 该模型显示了5ms/frame的快速检测速度.
  • 模型的重量大小减少到5.5 MB,优于其他拨号读取模型.

结论:

  • YOLOv5-MRL为变电站计数读数,平衡精度和实时需求提供了卓越的解决方案.
  • 修剪和拨号读取算法可以在资源有限的硬件上高效地部署.
  • 这种方法显著提高了电力变电站的自动计量器读数能力.