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An improved algorithm for locating a gas source using inverse methods

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An improved algorithm for locating a gas source using inverse methods. / Thomson, L.C.; Hirst, B.; Gibson, G. et al.
In: Atmospheric Environment, Vol. 41, No. 6, 2007, p. 1128-1134.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Thomson, LC, Hirst, B, Gibson, G, Gillespie, S, Jonathan, P, Skeldon, KD & Padgett, MJ 2007, 'An improved algorithm for locating a gas source using inverse methods', Atmospheric Environment, vol. 41, no. 6, pp. 1128-1134. https://doi.org/10.1016/j.atmosenv.2006.10.003

APA

Thomson, L. C., Hirst, B., Gibson, G., Gillespie, S., Jonathan, P., Skeldon, K. D., & Padgett, M. J. (2007). An improved algorithm for locating a gas source using inverse methods. Atmospheric Environment, 41(6), 1128-1134. https://doi.org/10.1016/j.atmosenv.2006.10.003

Vancouver

Thomson LC, Hirst B, Gibson G, Gillespie S, Jonathan P, Skeldon KD et al. An improved algorithm for locating a gas source using inverse methods. Atmospheric Environment. 2007;41(6):1128-1134. doi: 10.1016/j.atmosenv.2006.10.003

Author

Thomson, L.C. ; Hirst, B. ; Gibson, G. et al. / An improved algorithm for locating a gas source using inverse methods. In: Atmospheric Environment. 2007 ; Vol. 41, No. 6. pp. 1128-1134.

Bibtex

@article{72336427f99e4c0a9b6ca6a67a82eb3e,
title = "An improved algorithm for locating a gas source using inverse methods",
abstract = "We apply an inverse problem approach to locating a known gas source in a desert setting from simultaneous measurements of gas concentration and wind data. We use a random search algorithm with simulated annealing to generate candidate distributions of source strengths and positions. These distributions are then assessed by means of a cost function, which quantifies the degree to which the postulated source distribution accounts for the measured gas concentrations. We present results from using three cost functions with differing regularisation terms. We assess the robustness of these and the differing regularisation terms by the progressive addition of random noise and systematic offsets to the concentration data. We show that for our application, the best reconstructions are obtained by using a multiplicative regularisation parameter defined to minimise the total gas emissions. {\textcopyright} 2006 Elsevier Ltd. All rights reserved.",
keywords = "Cost function, Gas dispersion, Inverse problem, Random search, Simulated annealing, Algorithms, Concentration (process), Inverse problems, Robustness (control systems), Spurious signal noise, Candidate distributions, Cost functions, Tracking (position), algorithm, concentration (composition), cost-benefit analysis, gas, measurement method, monitoring, simulated annealing, article, atmosphere, atmospheric dispersion, exhaust gas, noise, priority journal, problem solving, simulation, wind",
author = "L.C. Thomson and B. Hirst and G. Gibson and S. Gillespie and P. Jonathan and K.D. Skeldon and M.J. Padgett",
year = "2007",
doi = "10.1016/j.atmosenv.2006.10.003",
language = "English",
volume = "41",
pages = "1128--1134",
journal = "Atmospheric Environment",
issn = "1352-2310",
publisher = "PERGAMON-ELSEVIER SCIENCE LTD",
number = "6",

}

RIS

TY - JOUR

T1 - An improved algorithm for locating a gas source using inverse methods

AU - Thomson, L.C.

AU - Hirst, B.

AU - Gibson, G.

AU - Gillespie, S.

AU - Jonathan, P.

AU - Skeldon, K.D.

AU - Padgett, M.J.

PY - 2007

Y1 - 2007

N2 - We apply an inverse problem approach to locating a known gas source in a desert setting from simultaneous measurements of gas concentration and wind data. We use a random search algorithm with simulated annealing to generate candidate distributions of source strengths and positions. These distributions are then assessed by means of a cost function, which quantifies the degree to which the postulated source distribution accounts for the measured gas concentrations. We present results from using three cost functions with differing regularisation terms. We assess the robustness of these and the differing regularisation terms by the progressive addition of random noise and systematic offsets to the concentration data. We show that for our application, the best reconstructions are obtained by using a multiplicative regularisation parameter defined to minimise the total gas emissions. © 2006 Elsevier Ltd. All rights reserved.

AB - We apply an inverse problem approach to locating a known gas source in a desert setting from simultaneous measurements of gas concentration and wind data. We use a random search algorithm with simulated annealing to generate candidate distributions of source strengths and positions. These distributions are then assessed by means of a cost function, which quantifies the degree to which the postulated source distribution accounts for the measured gas concentrations. We present results from using three cost functions with differing regularisation terms. We assess the robustness of these and the differing regularisation terms by the progressive addition of random noise and systematic offsets to the concentration data. We show that for our application, the best reconstructions are obtained by using a multiplicative regularisation parameter defined to minimise the total gas emissions. © 2006 Elsevier Ltd. All rights reserved.

KW - Cost function

KW - Gas dispersion

KW - Inverse problem

KW - Random search

KW - Simulated annealing

KW - Algorithms

KW - Concentration (process)

KW - Inverse problems

KW - Robustness (control systems)

KW - Spurious signal noise

KW - Candidate distributions

KW - Cost functions

KW - Tracking (position)

KW - algorithm

KW - concentration (composition)

KW - cost-benefit analysis

KW - gas

KW - measurement method

KW - monitoring

KW - simulated annealing

KW - article

KW - atmosphere

KW - atmospheric dispersion

KW - exhaust gas

KW - noise

KW - priority journal

KW - problem solving

KW - simulation

KW - wind

U2 - 10.1016/j.atmosenv.2006.10.003

DO - 10.1016/j.atmosenv.2006.10.003

M3 - Journal article

VL - 41

SP - 1128

EP - 1134

JO - Atmospheric Environment

JF - Atmospheric Environment

SN - 1352-2310

IS - 6

ER -