Abstract
We describe how we plan to convert a traditional data collection sensor and ocean model into a DDDAS enabled system for identifying contaminants and then reacting with different models, simulations, and sensing strategies in a symbiotic manner. The sensor is just as useful in water as it would be on Mars for material identification. A successful terrestrial application of the sensor will lead to many new applications of the device and possible technology transfer to the private sector.
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Douglas, C.C. et al. (2006). Dynamic Contaminant Identification in Water. In: Alexandrov, V.N., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds) Computational Science – ICCS 2006. ICCS 2006. Lecture Notes in Computer Science, vol 3993. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11758532_53
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DOI: https://doi.org/10.1007/11758532_53
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34383-7
Online ISBN: 978-3-540-34384-4
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Keywords
- Markov Chain Monte Carlo
- Hyperspectral Imaging
- Spectral Element
- Spectral Element Method
- Kalman Filter Approach
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
