Dynamic multivariate regression for on-field calibration of high speed air quality chemical multi-sensor systems

Proceedings of the 2015 18th AISEM Annual Conference, AISEM 2015

Volumn , Issue , 2015, Pages

  De Vito, S ^a   Delli Veneri, P ^a   Esposito, E ^a   Salvato, M ^a   Bright, V ^b   Jones, R L ^b   Popoola, O ^b  

^a L' Energia e Lo Sviluppo Economico Sostenibile   (Italy)

^b UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

air quality monitoring; dynamic neural networks; multivariate calibration

Indexed keywords

AIR QUALITY; CALIBRATION; MULTIVARIABLE SYSTEMS; REGRESSION ANALYSIS; STATISTICAL TESTS; STOCHASTIC SYSTEMS;

AIR QUALITY MONITORING; CROSS SENSITIVITY; DYNAMIC NEURAL NETWORKS; MULTI-SENSOR SYSTEMS; MULTIVARIATE CALIBRATION; MULTIVARIATE REGRESSION; NEW APPLICATIONS; ON-FIELD CALIBRATION;

NEURAL NETWORKS;

EID: 84937143030     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/AISEM.2015.7066794     Document Type: Conference Paper

References (5)

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3. S. De Vito et al., Gas concentration estimation in ternary mixture with room temperature operating sensor array using tapped delay neural networks, Sensors and Actuators B, 124, pp-309-316, 2007.
4. M.I. Mead et al., The use of electrochemical sensors for monitoring urban air quality in low-cost, high-density networks. Atmospheric Environment. 70, 186, (2013).
5. S. Sheick et al., Continuous Prediction in chemoresistive Gas Sensors Using Reservoir Computing, Proceedings of XXVIII Eurosensors 2014, Brixia, (2014)