Dynamic neural network architectures for on field stochastic calibration of indicative low cost air quality sensing systems

Sensors and Actuators, B: Chemical

Volumn 231, Issue , 2016, Pages 701-713

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

^a ENEA   (Italy)

^b UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

Air quality monitoring; Dynamic neural networks; Machine learning; Multivariate calibration

Indexed keywords

AIR QUALITY; ARTIFICIAL INTELLIGENCE; CALIBRATION; CONTROL EQUIPMENT; LEARNING SYSTEMS; MULTIVARIABLE SYSTEMS; NETWORK ARCHITECTURE; NEURAL NETWORKS; POLLUTION; QUALITY CONTROL; STATISTICAL TESTS; STOCHASTIC SYSTEMS;

AIR QUALITY MONITORING; CONCENTRATION CHANGE; CROSS SENSITIVITY; DYNAMIC NEURAL NETWORKS; MULTIVARIATE CALIBRATION; PERFORMANCE ANALYSIS; STATIC CALIBRATION; STATIC NEURAL NETWORKS;

MEMORY ARCHITECTURE;

EID: 84962252411     PISSN: 09254005     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.snb.2016.03.038     Document Type: Article

References (43)

1. EuNETAir COST, Action TD1105 Monitoring Progress Reports, 2012-2015, Available on www.eunetair.it action website.
2. T.J. Lyons, and W.D. Scott Principles of Air Pollution Meteorology 1990 Belhaven Press London
3. B.J. Alloway, and D.C. Ayres Chemical Principles of Environmental Pollution 1993 Blackie Academic & Professional London
4. P. Bloomfield, J.A. Royle, L.J. Steiberg, and Q. Yang Accounting for meteorological effects in measuring urban ozone levels and trends Atmos. Environ. 30 1996 3067 3077
5. S. Marco, and A. Gutiérrez-Gálvez Signal and data processing for machine olfaction and chemical sensing: A review IEEE Sens. J. 12 11 2012 3189 3214
6. x urban pollution monitoring with on-field calibrated electronic nose by automatic bayesian regularization Sens. Actuators B 143 2008 182 191
7. F. Röck, N. Barsan, and U. Weimar Electronic nose: Current status and future trends Chem. Rev. 108 2 2008 705 725
8. L. Spinelle, M. Gerboles, M.G. Villani, M. Aleixandre, and F. Bonavitacola Field calibration of a cluster of low-cost available sensors for air quality monitoring. Part A: Ozone and nitrogen dioxide Sens. Actuators B 2016 2014 SI: EUROSENSOR
9. M. Schweizer-Berberich, J. Goppert, A. Hielermann, J. Mitrovics, U. Weimar, W. Rosenstiel, and W. Gopel Application of neural network systems to the dynamic response of polymer-based sensor arrays Sens. Actuators B 26 27 1995 232 236
10. J.G. Monroy, A. Lilienthal, J.L. Blanco, J. González-Jimenez, and Trincavelli Calibration of MOX gas sensors in open sampling systems based on Gaussian processes Sens. IEEE 2012 1 4
11. M. Trincavelli, S. Coradeschi, and A. Loutfi Odour classification system for continuous monitoring applications Sens. Actuators B: Chem. 139 2 2009 265 273
12. A. Perera, T. Yamanaka, A. Gutiérrez-Gálvez, B. Raman, and R. Gutiérrez-Osuna A dimensionality-reduction technique inspired by receptor convergence in the olfactory system Sens. Actuators B: Chem. 116 1 2006 17 22
13. E. Llobet, O. Gualdrón, M. Vinaixa, N. El-Barbri, J. Brezmes, X. Vilanova, B. Bouchikhi, R. Gómez, J. Carrasco, and X. Correig Efficient feature selection for mass spectrometry based electronic nose applications Chemom. Intell. Lab. Syst. 85 2 2007 253 261
14. I. Rodriguez-Lujan, R. Huerta, C. Elkan, and C.S. Cruz Quadratic programming feature selection J. Mach. Learn. Res. 11 2010 1491 1516
15. J.S. Murguía, A. Vergara, C. Vargas-Olmos, T.J. Wong, J. Fonollosa, and R. Huerta Two-dimensional wavelet transform feature extraction for porous silicon chemical sensors Anal. Chim. Acta 785 2013 1 15
16. M.K. Muezzinoglu, A. Vergara, R. Huerta, N. Rulkov, M.I. Rabinovich, A. Selverston, and H.D. Abarbanel Acceleration of chemo-sensory information processing using transient features Sens. Actuators B: Chem. 137 2 2009 507 512
17. R. Gutierrez-Osuna Pattern analysis for machine olfaction: A review IEEE Sens. J. 2 3 2002 189 202
18. S.J. Chen, D.C. Hovde, K.A. Peterson, and A.W. Marshall Fire detection using smoke and gas sensors Fire Saf. J. 42 8 2007 507 515
19. A. Pardo, S. Marco, and J. Samitier Nonlinear inverse dynamic models of gas sensing systems based on chemical sensor arrays for quantitative measurements IEEE Trans. Instrum. Meas. 47 3 1998 644 651
20. M. Pardo, G. Faglia, G. Sberveglieri, M. Corte, F. Masulli, and M. Riani A time delay neural network for estimation of gas concentrations in a mixture Sens. Actuators B: Chem. 65 1 2000 267 269
21. S. De Vito, A. Castaldo, F. Loffredo, E. Massera, T. Polichetti, I. Nasti, and G. Di Francia Gas concentration estimation in ternary mixture with room temperature operating sensor array using tapped delay neural networks Sens. Actuators B 124 2007 309 316
22. S. De Vito, E. Massera, M. Piga, L. Martinotto, and G. Di Francia On field calibration of an electronic nose for benzene estimation in an urban pollution monitoring scenario Sens. Actuators B: Chem. 129 2 2008 750 757
23. C. Di Natale, S. Marco, F. Davide, and A. D'Amico Sensor-array calibration time reduction by dynamic modelling Sens. Actuators B: Chem. 25 1 1995 578 583
24. S. Sheik, and et al. Continuous prediction in chemoresistive gas sensors using reservoir computing Procedia Eng. 87 2014 843 846
25. J. Fonollosa, S. Sheik, R. Huerta, and S. Marco Reservoir computing compensates slow response of chemosensor arrays exposed to fast varying gas concentrations in continuous monitoring Sens. Actuators B: Chem. 215 2015 618 629
26. M.I. Mead, O.A.M. Popoola, G.B. Stewart, P. Landshoff, M. Calleja, M. Hayes, and A. Lewis The use of electrochemical sensors for monitoring urban air quality in low-cost, high-density networks Atmos. Environ. 70 2013 186 203
27. O3B4 Ozone Sensor 4-Electrode, data sheet, http://www.alphasense.com/WEB1213/wp-content/uploads/2013/11/O3B4.pdf.
28. NO2B4 Nitrogen Dioxide, 4-Electrode, Data sheet, http://www.alphasense.com/WEB1213/wp-content/uploads/2013/11/NO2B4.pdf.
29. C.M. Bishop Pattern Recognition and Machine Learning 2006 Springer Science ISBN 0-387-31073-8
30. L. Medsker, and L.C. Jain Recurrent Neural Networks: Design and Applications 1999 CRC Press
31. J. Fonollosa, L. Fernández, R. Huerta, A. Gutiérrez-Gálvez, and S. Marco Temperature optimization of metal oxide sensor arrays using mutual information Sens. Actuators B: Chem. 187 2013 331 339
32. J. Fonollosa, I. Rodriguez-Lujan, A.V. Shevade, M.L. Homer, M.A. Ryan, and R. Huerta Human activity monitoring using gas sensor arrays Sens. Actuators B: Chem. 199 2014 398 402
33. F.A. Davide, C.D. Natale, A. D'Amico, A. Hierlemann, J. Mitrovics, M. Schweizer, U. Weimar, W. Göpel, S. Marco, and A. Pardo Dynamic calibration of QMB polymer-coated sensors by Wiener kernel estimation Sens. Actuators B: Chem. 27 1 1995 275 285
34. S. Marco, A. Pardo, F.A. Davide, C.D. Natale, A. D'Amico, A. Hierlemann, J. Mitrovics, M. Schweizer, U. Weimar, and W. Göpel Different strategies for the identification of gas sensing systems Sens. Actuators B: Chem. 34 1 1996 213 223
35. J. Samitier, J. Lopez-Villegas, S. Marco, L. Camara, A. Pardo, O. Ruiz, and J. Morante A new method to analyse signal transients in chemical sensors Sens. Actuators B: Chem. 18 1 1994 308 312
36. E. Llobet, J. Brezmes, X. Vilanova, J.E. Sueiras, and X. Correig Qualitative and quantitative analysis of volatile organic compounds using transient and steady-state responses of a thick-film tin oxide gas sensor array Sens. Actuators B: Chem. 41 1 1997 13 21
37. R. Gutierrez-Osuna Pattern analysis for machine olfaction: A review IEEE Sens. J. 2 3 2002 189 202
38. P. Althainz, J. Goschnick, S. Ehrmann, and H. Ache Multisensor microsystem for contaminants in air Sens. Actuators B: Chem. 33 1 1996 72 76
39. L. Zhang, F. Tian, S. Liu, J. Guo, B. Hu, Q. Ye, L. Dang, X. Peng, C. Kadri, and J. Feng Chaos based neural network optimization for concentration estimation of indoor air contaminants by an electronic nose Sens. Actuators A: Phys. 189 2013 161 167
40. M.T. Hagan, and M.B. Menhaj Training feedforward networks with the marquardt algorithm IEEE Trans. Neural Networks 5 1994 989 993
41. J.G. Monroy, A.J. Lilienthal, J.L. Blanco, J. Gonzalez-Jimenez, and M. Trincavelli Probabilistic gas quantification with MOX sensors in open sampling systems - A gaussian process approach Sens. Actuators B: Chem. 188 2013 298 312
42. S. Vembu, A. Vergara, M.K. Muezzinoglu, and R. Huerta On time series features and kernels for machine olfaction Sens. Actuators B 174 November 2012 535 546 ISSN 0925-4005 http://dx.doi.org/10.1016/j.snb.2012.06.070
43. J. Quinonero-Candela, C.E. Rasmussen, F. Sinz, O. Bousquet, and B. Scholkopf Evaluating predictive uncertainty challenge Lect. Notes Comput. Sci. 3944 2006 1 27