Power-error analysis of sensor array regression algorithms for gas mixture quantification in low-power microsystems

Details

• Personal Author:
  Jinfeng Y ; Mason AJ ; Rong J ; Yang Y
• Description:
  Reliable gas sensors are highly desired for many applications, but their typically poor specificity requires arrays of cross-sensitive sensors to predict identity and concentrations of gas mixtures. A relationship between sensor outputs and gas concentrations can be formulated using regression models. This paper presents a detailed analysis of regression models generated using different algorithms. The analysis incorporates a variety of sensor parameters as well as the power consumption of each model when implemented within a low-power microcontroller. The results provide new insight into the effects of sensor array parameters on prediction errors and the tradeoffs between prediction errors and power for different regression models. [Description provided by NIOSH]
• Subjects:
  Air Pollution, Indoor Environmental Exposure Environmental Monitoring Occupational Health Risk Assessment Risk Factors Safety Workplace
• Keywords:
  Environmental Monitoring Exposure Levels Gas Detectors Gases Hazards Health Effects IAQ Indoor Air Quality Measurement Equipment Models Monitoring Systems Monitors Risk Factors Sensors Work Environment Workers

  More +
• ISBN:
  9781467346429
• ISSN:
  1930-0395
• Publisher:
  Institute of Electrical and Electronics Engineer
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  Michigan ; OSHA Region 5
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• NIOSHTIC Number:
  nn:20048650
• Citation:
  IEEE Sensors 2013, November 3-6, 2013, Baltimore, Maryland. New York: Institute of Electrical and Electronics Engineers, 2013 Nov; :6688580
• Contact Point Address:
  Andrew J. Mason, Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan, USA
• Email:
  mason@msu.edu
• Federal Fiscal Year:
  2014
• Performing Organization:
  Michigan State University
• Peer Reviewed:
  True
• Start Date:
  20100901
• Source Full Name:
  IEEE Sensors 2013, November 3-6, 2013, Baltimore, Maryland
• End Date:
  20150831
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:50c5b7e2decb88d29613ec5d4569ab9f2436970d5f184fcc70bf8f384c0d3e5bd0379d9eea8d1c6d97d8e2a02bae8e9ef54dbeeee310b6e07c05266c60b2f2c5
• Download URL:
  https://stacks.cdc.gov/view/cdc/203358/cdc_203358_DS1.pdf
• File Type:
  [PDF - 689.34 KB ]