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Titlebook: Handbook of Dynamic Data Driven Applications Systems; Volume 1 Erik P. Blasch,Frederica Darema,Alex J. Aved Book 2022Latest edition This is

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書目名稱Handbook of Dynamic Data Driven Applications Systems
副標(biāo)題Volume 1
編輯Erik P. Blasch,Frederica Darema,Alex J. Aved
視頻videohttp://file.papertrans.cn/422/421194/421194.mp4
概述Peer-reviewed contributions that focus on the use of DDDAS for various applications into one volume.Contributions from leading experts in various domains to reflect individual applications to the more
圖書封面Titlebook: Handbook of Dynamic Data Driven Applications Systems; Volume 1 Erik P. Blasch,Frederica Darema,Alex J. Aved Book 2022Latest edition This is
描述.The Handbook of Dynamic Data Driven Applications Systems establishes an authoritative reference of DDDAS, pioneered by Dr. Darema and the co-authors for researchers and practitioners developing DDDAS technologies...Beginning with general concepts and history of the paradigm, the text provides 32 chapters by leading experts in ten application areas to enable an accurate understanding, analysis, and control of complex systems; be they natural, engineered, or societal:..The authors explain how DDDAS unifies the computational and instrumentation aspects of an application system, extends the notion of Smart Computing to span from the high-end to the real-time data acquisition and control, and manages Big Data exploitation with high-dimensional model coordination...The Dynamically Data Driven Applications Systems (DDDAS) paradigm inspired research regarding the prediction of severe storms.? Specifically, the DDDAS concept allows atmospheric observing systems, computer forecast models, and cyberinfrastructure to dynamically configure themselves in optimal ways in direct response to current or anticipated weather conditions.? In so doing, all resources are used in an optimal manner to max
出版日期Book 2022Latest edition
關(guān)鍵詞DDDAS; Controls; Instrumentation; Big Data; High performance computing; Cyber physical systems; UAVs; data
版次2
doihttps://doi.org/10.1007/978-3-030-74568-4
isbn_softcover978-3-030-74570-7
isbn_ebook978-3-030-74568-4
copyrightThis is a U.S. government work and not under copyright protection in the U.S.; foreign copyright pro
The information of publication is updating

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https://doi.org/10.1007/978-3-663-07484-7ike and respiratory illness (from 2008 to 2010) from the Indiana Public Health Emergency Surveillance System. The DPPF develops a dynamic data-driven applications system (DDDAS) methodology for disease outbreak detection. Numerical results show that our model significantly improves the outbreak detection performance in real data analysis.
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Dynamic Space-Time Model for Syndromic Surveillance with Particle Filters and Dirichlet Processike and respiratory illness (from 2008 to 2010) from the Indiana Public Health Emergency Surveillance System. The DPPF develops a dynamic data-driven applications system (DDDAS) methodology for disease outbreak detection. Numerical results show that our model significantly improves the outbreak detection performance in real data analysis.
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https://doi.org/10.1007/978-3-322-88462-6the use of tractable variational information theoretic inference in estimation that also requires minimal resampling and allows for gradient-based inferences for non-Gaussian high-dimensional problems with few samples.
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els, and cyberinfrastructure to dynamically configure themselves in optimal ways in direct response to current or anticipated weather conditions.? In so doing, all resources are used in an optimal manner to max978-3-030-74570-7978-3-030-74568-4
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Dynamic Data-Driven Adaptive Observations in Data Assimilation for Multi-scale Systems from the expected uncertainty minimization criterion, for dynamic sensor selection in filtering problems. It is compared with a strategy based on finite-time Lyapunov exponents of the dynamical system, which provide insight into error growth due to signal dynamics.
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