Data-driven techniques for hardware and software synthesis for embedded systems
Data-driven techniques for hardware and software synthesis for embedded systems
New manufacturing modeling methodology: data driven design and simulation system based on XML
Proceedings of the 35th conference on Winter simulation: driving innovation
DDDAS approaches to wildland fire modeling and contaminant tracking
Proceedings of the 38th conference on Winter simulation
Introduction to the ICCS 2007 Workshop on Dynamic Data Driven Applications Systems
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007
Building a Dynamic Data Driven Application System for Hurricane Forecasting
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007
Towards applications of particle filters in wildfire spread simulation
Proceedings of the 40th Conference on Winter Simulation
Dynamic data driven application simulation of surface transportation systems
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part III
Towards a dynamic data driven system for structural and material health monitoring
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part III
Development of a computational paradigm for laser treatment of cancer
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part III
Monte Carlo filters for non-linear state estimation
Automatica (Journal of IFAC)
Data assimilation using sequential monte carlo methods in wildfire spread simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
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Wildfire spread simulation plays important roles in wildfire management. Existing wildfire simulations are largely decoupled from real wildfires by making little usage of real time data. In this paper, a dynamic data driven application system is presented to incorporate the real time data into the simulation model, thus to improve the simulation results. The developed dynamic data driven application system is based on the DEVS-FIRE model and employs the particle filtering algorithm to estimate the state of fire spread. We describe the overall structure of the dynamic data driven application system for wildfire spread simulation. The major issues and computation models of this work are presented and experiment results are provided.