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:
  • Sensors
  • Gas detectors
  • Gases
  • Exposure levels
  • Risk factors
  • Hazards
  • Monitoring systems
  • Monitors
  • Environmental monitoring
  • Indoor air quality
  More +
• ISBN:
  9781467346429
• ISSN:
  1930-0395
• 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
• Download URL:
  https://stacks.cdc.gov/view/cdc/203358/cdc_203358_DS1.pdf
• File Type:
  [PDF - 689.34 KB]
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:50c5b7e2decb88d29613ec5d4569ab9f2436970d5f184fcc70bf8f384c0d3e5bd0379d9eea8d1c6d97d8e2a02bae8e9ef54dbeeee310b6e07c05266c60b2f2c5