Space Invariant Independent Component Analysis and ENose for Detection of Selective Chemicals in an Unknown Environment

Tuan A. Duong; Margaret A. Ryan; Vu A. Duong

doi:10.20965/jaciii.2007.p1197

single-jc.php

« previous

JACIII Vol.11 No.10 pp. 1197-1203

doi: 10.20965/jaciii.2007.p1197

(2007)

Paper:

Views over last 60 days: 619

Space Invariant Independent Component Analysis and ENose for Detection of Selective Chemicals in an Unknown Environment

Tuan A. Duong, Margaret A. Ryan, and Vu A. Duong

Jet Propulsion Laboratory/California Institute of Technology, 4800 Oak Grove Dr. Pasadena, CA 91109

Received:

October 27, 2006

Accepted:

September 5, 2007

Published:

December 20, 2007

Keywords:

space invariant independent component analysis, ENose, chemical detection

Abstract

In this paper, we present a space invariant architecture to enable the Independent Component Analysis (ICA) to solve chemical detection from two unknown mixing chemical sources. The two sets of unknown paired mixture sources are collected via JPL 16-ENose sensor array in the unknown environment with, at most, 12 samples data collected. Our space invariant architecture along with the maximum entropy information technique by Bell and Sejnowski and natural gradient descent by Amari has demonstrated that it is effective to separate the two mixing unknown chemical sources with unknown mixing levels to the array of two original sources under insufficient sampled data. From separated sources, they can be identified by projecting them on the 11 known chemical sources to find the best match for detection. We also present the results of our simulations. These simulations have shown that 100% correct detection could be achieved under the two cases: a) under-completed case where the number of input (mixtures) is larger than number of original chemical sources; and b) regular case where the number of input is as the same as the number of sources while the time invariant architecture approach may face the obstacles: overcomplete case, insufficient data and cumbersome architecture.

Cite this article as:

T. Duong, M. Ryan, and V. Duong, “Space Invariant Independent Component Analysis and ENose for Detection of Selective Chemicals in an Unknown Environment,” J. Adv. Comput. Intell. Intell. Inform., Vol.11 No.10, pp. 1197-1203, 2007.

Data files:

References

[1] M. A. Ryan, M. L. Homer, M. G. Buehler, K. S. Manatt, F. Zee, and J. Graf, “Monitoring the Air Quality in a Closed Chamber Using an Electronic Nose,” Proc. of the 27th Int. Conf. on Environmental Systems, Society of Automotive Engineers 1997, 97-ES84.
[2] M. A. Ryan, M. L. Homer, M. G. Buehler, K. S. Manatt, B. Lau, D. Karmon, and S. Jackson, “Monitoring Space Shuttle Air for Selected Contaminants Using an Electronic Nose,” Proc. of the 28th Int. Conf. on Environmental Systems, Society of Automotive Engineers 1998, 98156.
[3] M. A. Ryan, M. L. Homer, H. Zhou, K. S. Manatt, V. S. Ryan, and S. P. Jackson, “Operation of an Electronic Nose Aboard the Space Shuttle and Directions for Research for a Second Generation Device,” Proc. of the 30th Int. Conf. on Environmental Systems, Society of Automotive Engineers 2000, 00ICES-259.
[4] “Spacecraft Maximum Allowable Concentrations for Selected Airborne Contaminants,” National Academy Press: Washington, DC, Vols.1 and 2, 1994.
[5] J. T. James, T. F. Limero, H. J. Leano, J. F. Boyd, and P. A. Covington, “Volatile Organic Contaminants Found in the Habitable Environment of the Space Shuttle: STS-26 to STS-55.,” Aviation Space Environmental Medicine, Vol.65, No.9, pp. 851-857, 1994.
[6] D. E. Rumelhart and J. L. McClelland, “Parallel Distributed Processing: Explorations in the Microstructure of Cognition,” Vol.1: Foundation, MIT Press, Cambridge, MA, 1986.
[7] S. E. Fahlman and C. Lebiere, “The Cascade Correlation learning architecture, Advances in Neural Information Processing Systems II,” D. Touretzky, M. Kaufmann, and S. Mateo (Eds.), CA, pp. 524-532, 1990.
[8] M. Cohen and S. Grossberg, “Absolute stability of global pattern formation and parallel memory stage by competitive neural networks,” IEEE Trans. Systems, Man, Cybernetics, Vol.SMC-13, pp. 815-826, 1983.
[9] B. Widrow, “Generalization and information storage in networks of ADALINE neurons,” G. T. Yovitt (Ed.), Self-Organizing Systems, Spartan Books, Washington DC, 1962.
[10] T. A. Duong and A. R. Stubberud, “Convergence Analysis Of Cascade Error Projection: An Efficient hardware learning algorithm,” International Journal of Neural System, Vol.10, No.3, pp. 199-210, June, 2000.
[11] J. Herault and C. Jutten, “Space or time adaptive signal processing by neural network models,” J. S. Denker (Ed.), Neural network for computing: AIP Conference proceedings 151, New York, American Institute for Physics, 1986.
[12] C. Jutten and J. Herault, “Blind separation of the sources, part I: an adaptive algorithm based on neuromimetic architecture,” Signal Processing, Vol.24, pp. 1-10, 1991.
[13] P. Common, “Independent component Analysis a new concepts?” Signal Processing, Vol.36, Issue 3, pp. 287-314, 1994.
[14] D.-T. Pham, P. Garrat, and C. Jutten, “Separation of a mixtures of independent sources through a maximum likelihood approach,” EUSIPCO, pp. 771-774, 1992.
[15] J. Cardoso, “High-order contrast for independent component analysis,” Neural Computation, 1998.
[16] A. J. Bell and T. J. Sejnowski, “An Information-Maximization Approach to Blind Separation and Blind Deconvolution,” Neural Computation, Vol.7, pp. 1129-1159, 1995.
[17] V. A. Duong, “Independent Component Analysis for Space-Time and Nonlinear Mixtures,” Ph.D. Thesis UCI, 2004.
[18] S. I. Amari, “Natural gradient works efficiently in learning,” Neural Computation, 1998.
[19] A. Bell, “Information Theory, Independent-Component Analysis and Applications, Unsupervised Adaptive Filtering,” S. Haykin (Ed.), Vol.1: Blind Source Separation, Chapter 6, John Wiley and Son Inc., 2000.

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] M. A. Ryan, M. L. Homer, M. G. Buehler, K. S. Manatt, F. Zee, and J. Graf, “Monitoring the Air Quality in a Closed Chamber Using an Electronic Nose,” Proc. of the 27th Int. Conf. on Environmental Systems, Society of Automotive Engineers 1997, 97-ES84.

[2] [2] M. A. Ryan, M. L. Homer, M. G. Buehler, K. S. Manatt, B. Lau, D. Karmon, and S. Jackson, “Monitoring Space Shuttle Air for Selected Contaminants Using an Electronic Nose,” Proc. of the 28th Int. Conf. on Environmental Systems, Society of Automotive Engineers 1998, 98156.

[3] [3] M. A. Ryan, M. L. Homer, H. Zhou, K. S. Manatt, V. S. Ryan, and S. P. Jackson, “Operation of an Electronic Nose Aboard the Space Shuttle and Directions for Research for a Second Generation Device,” Proc. of the 30th Int. Conf. on Environmental Systems, Society of Automotive Engineers 2000, 00ICES-259.

[4] [4] “Spacecraft Maximum Allowable Concentrations for Selected Airborne Contaminants,” National Academy Press: Washington, DC, Vols.1 and 2, 1994.

[5] [5] J. T. James, T. F. Limero, H. J. Leano, J. F. Boyd, and P. A. Covington, “Volatile Organic Contaminants Found in the Habitable Environment of the Space Shuttle: STS-26 to STS-55.,” Aviation Space Environmental Medicine, Vol.65, No.9, pp. 851-857, 1994.

[6] [6] D. E. Rumelhart and J. L. McClelland, “Parallel Distributed Processing: Explorations in the Microstructure of Cognition,” Vol.1: Foundation, MIT Press, Cambridge, MA, 1986.

[7] [7] S. E. Fahlman and C. Lebiere, “The Cascade Correlation learning architecture, Advances in Neural Information Processing Systems II,” D. Touretzky, M. Kaufmann, and S. Mateo (Eds.), CA, pp. 524-532, 1990.

[8] [8] M. Cohen and S. Grossberg, “Absolute stability of global pattern formation and parallel memory stage by competitive neural networks,” IEEE Trans. Systems, Man, Cybernetics, Vol.SMC-13, pp. 815-826, 1983.

[9] [9] B. Widrow, “Generalization and information storage in networks of ADALINE neurons,” G. T. Yovitt (Ed.), Self-Organizing Systems, Spartan Books, Washington DC, 1962.

[10] [10] T. A. Duong and A. R. Stubberud, “Convergence Analysis Of Cascade Error Projection: An Efficient hardware learning algorithm,” International Journal of Neural System, Vol.10, No.3, pp. 199-210, June, 2000.

[11] [11] J. Herault and C. Jutten, “Space or time adaptive signal processing by neural network models,” J. S. Denker (Ed.), Neural network for computing: AIP Conference proceedings 151, New York, American Institute for Physics, 1986.

[12] [12] C. Jutten and J. Herault, “Blind separation of the sources, part I: an adaptive algorithm based on neuromimetic architecture,” Signal Processing, Vol.24, pp. 1-10, 1991.

[13] [13] P. Common, “Independent component Analysis a new concepts?” Signal Processing, Vol.36, Issue 3, pp. 287-314, 1994.

[14] [14] D.-T. Pham, P. Garrat, and C. Jutten, “Separation of a mixtures of independent sources through a maximum likelihood approach,” EUSIPCO, pp. 771-774, 1992.

[15] [15] J. Cardoso, “High-order contrast for independent component analysis,” Neural Computation, 1998.

[16] [16] A. J. Bell and T. J. Sejnowski, “An Information-Maximization Approach to Blind Separation and Blind Deconvolution,” Neural Computation, Vol.7, pp. 1129-1159, 1995.

[17] [17] V. A. Duong, “Independent Component Analysis for Space-Time and Nonlinear Mixtures,” Ph.D. Thesis UCI, 2004.

[18] [18] S. I. Amari, “Natural gradient works efficiently in learning,” Neural Computation, 1998.

[19] [19] A. Bell, “Information Theory, Independent-Component Analysis and Applications, Unsupervised Adaptive Filtering,” S. Haykin (Ed.), Vol.1: Blind Source Separation, Chapter 6, John Wiley and Son Inc., 2000.