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Citation: Uri Lerner and Brooks Moses and Maricia Scott and Sheila A. McIlraith and Daphne Koller. (2002) Monitoring a Complex Physical System using a Hybrid Dynamic Bayes Net. In Proceedings of the Eighteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI-2002), August,2002.
| Publication inproceedings ( Edit )
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| type | InProceedings
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| bibtype | inproceedings
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| Bibtex basics
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| author | Uri Lerner and Brooks Moses and Maricia Scott and Sheila A. McIlraith and Daphne Koller
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| title | Monitoring a Complex Physical System using a Hybrid Dynamic Bayes Net
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| booktitle | Proceedings of the Eighteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI-2002)
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| address | Edmonton, Canada
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| year | 2002
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| month | August
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| abstract | The Reverse Water Gas Shift system (RWGS) is a complex physical system designed to produce oxygen from the carbon dioxide atmosphere on Mars. If sent to Mars, it would operate without human supervision,thus requiring a reliable automated system formonitoring and control. The RWGS presents many challenges typical of real-world systems, including:noisy and biased sensors, nonlinear behavior, effects that are manifested over different time granularities,and unobservability of many important quantities. In this paper we model the RWGS using a hybrid (discrete/continuous) Dynamic Bayesian Network (DBN),where the state at each time slice contains 33 discrete and 184 continuous variables. We show how the system state can be tracked using probabilistic inference over the model. We discuss how to deal with the various challenges presented by the RWGS, providing a suite of techniques that are likely to be useful in a wide range of applications. In particular, we describe a general framework for dealing with nonlinear behavior using numerical integration techniques, extending the successful Unscented Filter. We also show how to use a fixed-point computation to deal with effects that develop at different time scales, specifically rapid changes occurring during slowly changing processes.We test our model using real data collected from the RWGS, demonstrating the feasibility of hybrid DBNs for monitoring complex real-world physical systems.
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| KSL Technical Report ID: KSL-02-08
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Facts about Monitoring a Complex Physical System using a Hybrid Dynamic Bayes NetRDF feed
| Abstract | The Reverse Water Gas Shift system (RWGS) … The Reverse Water Gas Shift system (RWGS) is a complex physical system designed to produce oxygen from the carbon dioxide atmosphere on Mars. If sent to Mars, it would operate without human supervision,thus requiring a reliable automated system formonitoring and control. The RWGS presents many challenges typical of real-world systems, including:noisy and biased sensors, nonlinear behavior, effects that are manifested over different time granularities,and unobservability of many important quantities. In this paper we model the RWGS using a hybrid (discrete/continuous) Dynamic Bayesian Network (DBN),where the state at each time slice contains 33 discrete and 184 continuous variables. We show how the system state can be tracked using probabilistic inference over the model. We discuss how to deal with the various challenges presented by the RWGS, providing a suite of techniques that are likely to be useful in a wide range of applications. In particular, we describe a general framework for dealing with nonlinear behavior using numerical integration techniques, extending the successful Unscented Filter. We also show how to use a fixed-point computation to deal with effects that develop at different time scales, specifically rapid changes occurring during slowly changing processes.We test our model using real data collected from the RWGS, demonstrating the feasibility of hybrid DBNs for monitoring complex real-world physical systems. oring complex real-world physical systems. |
| Address | Edmonton, Canada + |
| Author | Uri Lerner and Brooks Moses and Maricia Scott and Sheila A. McIlraith and Daphne Koller + |
| Bibtype | inproceedings + |
| Booktitle | Proceedings of the Eighteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI-2002) + |
| Has author | Uri Lerner and Brooks Moses and Maricia Scott and Sheila A. McIlraith and Daphne Koller + |
| Has identifier | KSL-02-08 + |
| Has publishing details | August,2002 + |
| Has title | Monitoring a Complex Physical System using a Hybrid Dynamic Bayes Net + |
| Has where published | Proceedings of the Eighteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI-2002) + |
| Has year | 2002 + |
| Ksl tr id | KSL-02-08 + |
| Month | August + |
| Process note | NO + |
| Title | Monitoring a Complex Physical System using a Hybrid Dynamic Bayes Net + |
| Year | 2002 + |
