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Related Concept Videos

Temperature Measurement Sites01:14

Temperature Measurement Sites

A thermometer measures body temperature. The common sites for measuring body temperature are the oral cavity, axillary region, temporal artery, and skin surface, such as the forehead, abdomen, and axilla. True core body temperature is assessed in the rectum, tympanic membrane, pulmonary artery, esophagus, and urinary bladder.
Oral: When assessing oral temperature, the thermometer tip should be placed under the tongue in the posterior sublingual pocket. It offers accurate readings and can be...

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Temperature-Based Long-Term Stabilization of Photoacoustic Gas Sensors Using Machine Learning.

Pavel Borozdin1, Evgenii Erushin1, Artem Kozmin1

  • 1The Artificial Intelligence Research Center, Novosibirsk State University, Pirogova Str. 2, 630090 Novosibirsk, Russia.

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Summary
This summary is machine-generated.

This study developed a novel AI model to predict photoacoustic detector (PAD) gas cell resonance frequency shifts caused by temperature changes. The method enables real-time tracking, enhancing gas concentration accuracy and sensor stability.

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accuracylong short-term memory networksmachine learningmethaneneural networksoptical sensingphotoacoustic gas sensorphotoacoustic spectroscopysensitivity enhancement

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Area of Science:

  • Analytical Chemistry
  • Sensor Technology
  • Artificial Intelligence

Background:

  • Accurate gas concentration measurements are vital for environmental and industrial monitoring.
  • Photoacoustic detector (PAD) gas cell resonance frequency is sensitive to temperature variations, impacting measurement accuracy.
  • Existing methods for tracking resonance frequency can be intrusive and interrupt sensor operation.

Purpose of the Study:

  • To develop a non-intrusive, real-time method for estimating photoacoustic detector (PAD) gas cell resonance frequency shifts due to temperature.
  • To improve the accuracy and long-term stability of gas concentration measurements by compensating for temperature-induced frequency drifts.
  • To investigate the relationship between gas mixture temperature and resonance frequency in a PAD.

Main Methods:

  • A novel approach utilizing a long short-term memory (LSTM) network and a self-attention mechanism was employed to model resonance frequency shifts based on temperature data.
  • The photoacoustic detector (PAD) was modified with an internal temperature sensor to capture precise gas mixture temperature data.
  • Experiments involved multiple heating and cooling cycles with varying methane concentrations to generate a comprehensive dataset.

Main Results:

  • The proposed AI models demonstrated real-time prediction capabilities for resonance frequency shifts.
  • A mean absolute error of less than 1 Hz was achieved for frequency shifts exceeding 30 Hz over four-hour periods.
  • The method allows continuous tracking of resonance frequency without interrupting laser operation.

Conclusions:

  • The developed AI approach effectively manages temperature-induced frequency shifts in photoacoustic detectors (PADs).
  • This technique significantly enhances the accuracy and long-term stability of gas concentration measurements.
  • The method offers a valuable tool for practical applications requiring precise and stable gas sensing.