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Related Concept Videos

Instrument Calibration01:12

Instrument Calibration

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Instrument calibration is essential for ensuring that instruments produce accurate and consistent results. It is vital in manufacturing, healthcare, testing laboratories, and scientific research. Calibration processes are specific to each instrument and help enhance data accuracy. Each instrument has a unique calibration process tailored to its design and function to improve data accuracy.
Analytical Balance Calibration
An analytical balance measures mass and requires regular calibration to...
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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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Additive Manufacturing-Enabled Low-Cost Particle Detector
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Using Machine Learning for the Calibration of Airborne Particulate Sensors.

Lakitha O H Wijeratne1, Daniel R Kiv1, Adam R Aker1

  • 1University of Texas at Dallas, 800 W, Campbell Rd, Richardson, TX 75080, USA.

Sensors (Basel, Switzerland)
|December 28, 2019
PubMed
Summary
This summary is machine-generated.

This study demonstrates machine learning effectively calibrates low-cost optical particle counters for monitoring airborne particulates. Environmental monitoring can be expanded with affordable sensors, improving air quality data collection.

Keywords:
airborne particulatesmachine learningoptical particle counter

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Area of Science:

  • Environmental science
  • Atmospheric chemistry
  • Sensor technology

Background:

  • Airborne particulates significantly impact human health, atmospheric radiative transfer, and chemistry.
  • Current particulate matter monitoring relies on expensive instruments, limiting sensor deployment.
  • There is a need for cost-effective methods to enhance air quality monitoring networks.

Purpose of the Study:

  • To investigate the efficacy of machine learning for calibrating low-cost optical particle counters.
  • To determine the feasibility of using affordable sensors for accurate particulate matter measurements.
  • To improve the scalability of atmospheric particulate monitoring.

Main Methods:

  • Utilized machine learning algorithms to calibrate optical particle counters.
  • Collected data concurrently with atmospheric pressure, humidity, and temperature measurements.
  • Validated the performance of the calibrated low-cost sensors against established methods.

Main Results:

  • Machine learning successfully calibrated lower-cost optical particle counters.
  • The calibration accuracy was dependent on simultaneous measurements of atmospheric pressure, humidity, and temperature.
  • The findings suggest a viable method for widespread deployment of affordable particulate sensors.

Conclusions:

  • Machine learning offers a cost-effective solution for calibrating optical particle counters.
  • Enhanced calibration enables the use of lower-cost sensors for environmental monitoring.
  • This approach can significantly expand the capacity for collecting vital air quality data.