Using neural network techniques in environmental sensing and measurement systems to compensate for the effects of influence quantities

Multiple influence quantities affect environmental sensing and measurement (ESM) systems. Accounting for their variations over time promotes metrological comparability and traceability of measurement results. Using suitable data processing techniques allows identification of the main influence quant...

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Bibliographic Details
Published in:IEEE instrumentation & measurement magazine 2014-12, Vol.17 (6), p.26-56
Main Authors: Dias Pereira, J. Miguel, Postolache, Octavian Adrian, Silva Girao, Pedro M. B.
Format: Magazinearticle
Language:English
Subjects:
Accounting
Artificial neural networks
Compensation
Data processing
Detection
Environmental factors
Instrumentation
Measurement uncertainty
Metrology
Neural networks
Pollution measurement
Temperature measurement
Time measurement
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Summary:Multiple influence quantities affect environmental sensing and measurement (ESM) systems. Accounting for their variations over time promotes metrological comparability and traceability of measurement results. Using suitable data processing techniques allows identification of the main influence quantities that affect the measurement result of a given quantity and allows evaluation of the associated compensation coefficients. This paper presents several concerns related to implementation of ESM systems, paying particular attention to the impact of the multiple of measuring and influence quantities on system performance. A review of different data processing techniques to compensate for the effects of influence quantities is presented. A case study based on water conductivity measurements is used to illustrate the capability of artificial neural network (ANN) based techniques and to cope with errors due to a low number of measurement values and due to collinear effects between influence quantities.
ISSN:1094-6969
1941-0123
DOI:10.1109/MIM.2014.6968927