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Titlebook: Handbook of Dynamic Data Driven Applications Systems; Erik Blasch,Sai Ravela,Alex Aved Book 20181st edition Springer Nature Switzerland AG

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發(fā)表于 2025-3-21 16:26:15 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Handbook of Dynamic Data Driven Applications Systems
編輯Erik Blasch,Sai Ravela,Alex Aved
視頻videohttp://file.papertrans.cn/422/421192/421192.mp4
概述Peer-reviewed contributions that focus on the use of DDDAS for various applications:.Benefit: Future readers can quickly see the areas of contribution in a single (hardback volume) which can be availa
圖書封面Titlebook: Handbook of Dynamic Data Driven Applications Systems;  Erik Blasch,Sai Ravela,Alex Aved Book 20181st edition Springer Nature Switzerland AG
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 in10 application areas to enable an accurate understanding, analysis, and control of complex systems; be they natural, engineered, or societal:.Earth and Space Data Assimilation.Aircraft Systems Processing.Structures Health Monitoring.Biological Data Assessment.Object and Activity Tracking.Embedded Control and Coordination.Energy-Aware Optimization.Image and Video Computing.Security and Policy Coding.Systems Design. .?The authorsexplain 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..? ? ?.
出版日期Book 20181st edition
關(guān)鍵詞DDDAS; Controls; Instrumentation; Big Data; High performance computing; Cyber physical systems; UAVs; data
版次1
doihttps://doi.org/10.1007/978-3-319-95504-9
isbn_ebook978-3-319-95504-9
copyrightSpringer Nature Switzerland AG 2018
The information of publication is updating

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Tractable Non-Gaussian Representations in Dynamic Data Driven Coherent Fluid Mappingous small unmanned aircraft. The application and and its underlying system dynamics and optimization are presented along with three key ideas. The first is that of a dynamically deformable reduced model, which enables efficacious prediction by solving non-Gaussian problems associated with coherent f
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Towards Learning Spatio-Temporal Data Stream Relationships for Failure Detection in Avionicse weight it carries, it also depends on many other factors. Some of these factors are controllable such as engine inputs or the airframe’s angle of attack, while others contextual, such as air density, or turbulence. It is therefore critical to develop failure models that can help recognize errors i
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Markov Modeling of Time Series via Spectral Analysis for Detection of Combustion Instabilities models are often used to capture temporal patterns in sequential data for statistical learning applications. This chapter presents a methodology for reduced-order Markov modeling of time-series data based has been used on spectral properties of stochastic matrix and clustering of directed graphs. I
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Dynamic Space-Time Model for Syndromic Surveillance with Particle Filters and Dirichlet Processucture. To resolve these issues, we propose a novel Dirichlet process particle filter (DPPF) model. The Dirichlet process models a set of stochastic functions as probability distributions for dimension reduction, and the particle filter is used to solve the nonlinear filtering problem with sequentia
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Dynamic Data-Driven Approach for Unmanned Aircraft Systems and Aeroelastic Response Analysis the system. Our approach is illustrated in the context of an unmanned aerial vehicle, such as the joined wing SensorCraft. It will be shown as to how DDDAS can be used to enhance the performance envelope as well as avoid aeroelastic instabilities, while reducing the need for user input. The DDDAS m
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