Non-linear carbon dioxide determination using infrared gas sensors and neural networks with Bayesian regularization

Sensors and Actuators, B: Chemical

Volumn 136, Issue 1, 2009, Pages 242-247

  Lau, King Tong ^a   Guo, Weimin ^a   Kiernan, Breda ^a   Slater, Conor ^a   Diamond, Dermot ^a  

^a DUBLIN CITY UNIVERSITY   (Ireland)

Author keywords

Bayesian regularization; Carbon dioxide; Infrared gas sensors; Levenberg Marquardt algorithm; Neural networks

Indexed keywords

ARTIFICIAL INTELLIGENCE; BACKPROPAGATION ALGORITHMS; BAYESIAN NETWORKS; CARBON DIOXIDE; CHEMICAL SENSORS; GAS DETECTORS; GASES; IMAGE ENHANCEMENT; METROPOLITAN AREA NETWORKS; OXIDES; SENSOR NETWORKS; SENSORS; VEGETATION;

BACK PROPAGATION NEURAL NETWORKS; BAYESIAN; BAYESIAN REGULARIZATION; CARBON DIOXIDE GASSES; CLASSICAL METHODS; GENERALIZATION CAPABILITIES; HIDDEN LAYERS; INFRARED GAS SENSORS; INFRARED SENSORS; INPUT AND OUTPUTS; LEVENBERG-MARQUARDT ALGORITHM; LINEAR OUTPUTS; NETWORK MODELS; OPTIMAL ARCHITECTURES; POWERFUL TOOLS;

NEURAL NETWORKS;

EID: 58249131218     PISSN: 09254005     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.snb.2008.11.030     Document Type: Article

References (23)

1. Lombardi L., Carnevale E., and Corti A. Greenhouse effect reduction and energy recovery from waste landfill. Energy 31 (2006) 3208-3219
2. Mosher B.W., Czepiel P.M., Harriss R.C., Shorter J.H., Kolb C.E., McManus J.B., Allwine E., and Lamb B.K. Methane emissions at nine landfill sites in the northeastern United States. Environ. Sci. Technol. 33 (1999) 2088-2094
3. Costi P., Minciardi R., Robba M., Rovatti M., and Sacile R. An environmentally sustainable decision model for urban solid waste management. Waste Manage. 24 (2004) 277-295
4. Squire S., and Ramsey M.H. Inter-organisational sampling trials for the uncertainty estimation of landfill gas measurements. J. Environ. Monitor. 3 (2001) 288-294
5. 2 flux at the air/water interface in lakes using flow injection analysis. J. Environ. Monitor. 3 (2001) 317-321
6. Spokas K., Bogner J., Chanton J.P., Morcet M., Aran C., Graff C., Moreau-Le Golvan Y., and Hebe I. Methane mass balance at three landfill sites: what is the efficiency of capture by gas collection systems?. Waste Manage. 26 (2006) 516-525
7. Diamond D. Internet-scale sensing. Anal. Chem. 76 (2004) 278A-286A
8. 2 sensor. J. Environ. Monitor. 2 (2000) 556-560
9. Kiernan B., Guo W.M., Slater C., Hayes J., and Diamond D. Autonomous monitoring of landfill gas migration at borehole wells on landfill sites using wireless technology. Proceedings of the 10th International Conference on Environmental Science and Technology, A (2007) 679-685
10. Kanu A.B., Hill H.H., Gribb M.M., and Waters R.N. A small subsurface ion mobility spectrometer sensor for detecting environmental soil-gas contaminants. J. Environ. Monitor. 9 (2007) 51-60
11. Hanrahan G., Patil D.G., and Wang J. Electrochemical sensors for environmental monitoring: design, development and applications. J. Environ. Monitor. 6 (2004) 657-664
12. Dickens J., and Sepaniak M. Modular separation-based fiber-optic sensors for remote in situ monitoring. J. Environ. Monitor. 2 (2000) 11-16
13. Mulrooney J., Clifford J., Fitzpatrick C., and Lewis E. Detection of carbon dioxide emissions from a diesel engine using a mid-infrared optical fiber based sensor. Sensors Actuat. A: Phys. 136 (2007) 104-110
14. Henkel K., and Schmeisser D. Back-propagation-based neural network with a two sensor system for monitoring carbon dioxide and relative humidity. Anal. Bioanal. Chem. 374 (2002) 329-337
15. Mackay D.J.C. A practical Bayesian framework for backpropagation. Networks: Neural Comput. 4 (1992) 448-472
16. Yin C.S., Guo W.M., Liu S.S., Shen Y., Pan Z.X., and Wang L.S. Simultaneous determination of vitamin B complex using wavelet neural network. Chin. J. Chem. 19 (2001) 836-841
17. Corrado D.N., Fabrizio D., and Arnaldo D.A. Pattern recognition in gas sensing: well-stated techniques and advances. Sensors Actuat. B: Chem. 23 (1995) 111-118
18. Li C.Y., Heinemann P., and Sherry R. Neural network and Bayesian network fusion models to fuse electronic nose and surface acoustic wave sensor data for apple defect detection. Sensors Actuat. B: Chem. 125 (2007) 301-310
19. Moreno-Baron L., Cartas R., Merkoçi A., Alegret S., del Valle M., Leija L., Hernandez P.R., and Munoz R. Application of the wavelet transform coupled with artificial neural networks for quantification purposes in a voltammetric electronic tongue. Sensors Actuat. B: Chem. 113 (2006) 487-499
20. Gallardo J., Alegret S., and del Valle M. A flow-injection electronic tongue based on potentiometric sensors for the determination of nitrate in the presence of chloride. Sensors Actuat. B: Chem. 101 (2004) 72-80
21. Lampinen J., and Vehtari A. Bayesian approach for neural networks: a review and case studies. Networks 14 (2001) 257-274
22. Mackay D.J.C. Probable networks and plausible predictions: a review of practical Bayesian methods for supervised neural networks. Network: Comput. Neural Systems 6 (1995) 469-505
23. Hagan M.T., and Menhaj M. Training multilayer networks with the Marquardt algorithm. IEEE Trans. Neural Networks 5 (1994) 989-993