標(biāo)題: Titlebook: Handbook of Dynamic Data Driven Applications Systems; Volume 2 Frederica Darema,Erik P. Blasch,Alex J. Aved Book 2023 This is a U.S. govern [打印本頁(yè)] 作者: grateful 時(shí)間: 2025-3-21 17:29
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書(shū)目名稱(chēng)Handbook of Dynamic Data Driven Applications Systems被引頻次
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書(shū)目名稱(chēng)Handbook of Dynamic Data Driven Applications Systems讀者反饋
書(shū)目名稱(chēng)Handbook of Dynamic Data Driven Applications Systems讀者反饋學(xué)科排名
作者: Blazon 時(shí)間: 2025-3-21 21:30
https://doi.org/10.1007/978-3-031-27986-7DDDAS; Controls; Instrumentation; Big Data; Dynamic Systems; High performance computing; InfoSymbiotics; Cy作者: 我怕被刺穿 時(shí)間: 2025-3-22 01:51
Frederica Darema,Erik P. Blasch,Alex J. AvedExpands the scope of the methods and the application areas presented in the first volume.In-depth discussions reflecting the adoption of DDDAS paradigm from leading experts in various domain.Includes 作者: 壯麗的去 時(shí)間: 2025-3-22 08:18
http://image.papertrans.cn/h/image/421193.jpg作者: grounded 時(shí)間: 2025-3-22 10:12
The Dynamic Data Driven Applications Systems (DDDAS) Paradigm and Emerging Directions,understanding and exploiting the fundamentals of systems and processes at the nanoscale to the extra-terrascale. This book is the second volume of collected works that detail DDDAS methods and the increasing impact of DDDAS over the years. To update readers on the DDDAS approaches and techniques, th作者: Delectable 時(shí)間: 2025-3-22 16:12 作者: 殺蟲(chóng)劑 時(shí)間: 2025-3-22 18:46 作者: 破譯 時(shí)間: 2025-3-23 01:03 作者: Conjuction 時(shí)間: 2025-3-23 04:30 作者: Institution 時(shí)間: 2025-3-23 09:26 作者: Deject 時(shí)間: 2025-3-23 12:24
From Data to Decisions: A Real-Time Measurement–Inversion–Prediction–Steering Framework for Hazardouion of the task of sensor steering as an optimization problem that seeks to minimize prediction uncertainty, and a model reduction approach that facilitates execution of all of these steps in a real-time setting.作者: Ataxia 時(shí)間: 2025-3-23 16:31 作者: 團(tuán)結(jié) 時(shí)間: 2025-3-23 18:14 作者: Cosmopolitan 時(shí)間: 2025-3-23 22:35
Dynamic Data-Driven Application Systems for Reservoir Simulation-Based Optimization: Lessons Learnedrpose of this chapter is to review the fundamental components that have shaped reservoir-simulation-based optimization in the context of DDDAS. The foundations of each component will be systematically reviewed, followed by a discussion on current and future trends oriented to highlight the outstandi作者: adumbrate 時(shí)間: 2025-3-24 04:58 作者: Concomitant 時(shí)間: 2025-3-24 09:24
A Simulation-Based Online Dynamic Data-Driven Framework for Large-Scale Wind-Turbine Farm Systems Ope progresses. Online optimization is a suitable framework for problems involving data uncertainties that evolve over time, requiring important and cognizant system decisions to be made sequentially prior to observing the entire data stream. An important challenge in using online control is to optimi作者: Reclaim 時(shí)間: 2025-3-24 13:31
Toward Dynamic Data-Driven Systems for Rapid Adaptive Interdisciplinary Ocean Forecastingodels, and ocean current monitoring data assimilation schemes with innovative modeling and adaptive sampling methods. The legacy systems are encapsulated at the binary level using software component methodologies. Measurement models are utilized to link the observed data to the dynamical model varia作者: pantomime 時(shí)間: 2025-3-24 15:15 作者: NIL 時(shí)間: 2025-3-24 21:19 作者: Endemic 時(shí)間: 2025-3-25 00:29
Berechnung der Kegelradgeometrie,or ballistic trajectory estimation, where both the dynamic model and the angle-only measurement model are nonlinear. Numerical results show that the proposed PC-SrEnKF filter outperforms some previous popular nonlinear estimation methods, such as the extended Kalman filter (EKF), the unscented Kalma作者: Astigmatism 時(shí)間: 2025-3-25 03:25
https://doi.org/10.1007/978-3-663-02759-1 a random fashion. Therefore, fatigue damage is a stochastic process, for which early detection is required for condition-based maintenance and service life extension. This chapter addresses the problem of statistical pattern recognition, anomaly detection, and decision-making for control, operation作者: eucalyptus 時(shí)間: 2025-3-25 11:00 作者: 不持續(xù)就爆 時(shí)間: 2025-3-25 13:53 作者: 分離 時(shí)間: 2025-3-25 19:08 作者: PALL 時(shí)間: 2025-3-25 21:40
https://doi.org/10.1007/978-3-662-41223-7ion of the task of sensor steering as an optimization problem that seeks to minimize prediction uncertainty, and a model reduction approach that facilitates execution of all of these steps in a real-time setting.作者: Employee 時(shí)間: 2025-3-26 00:25
https://doi.org/10.1007/978-3-663-04496-3mation (locations, orientations, etc.), and second, the map model is updated by the material damage estimates, and in turn the uncertainty in the mapping data determines where new ultrasound data will be acquired. This approach is validated in a set of physical experiments.作者: Osteoarthritis 時(shí)間: 2025-3-26 05:36
https://doi.org/10.1007/978-3-663-07464-9ncreasing number of targets and sensors. Appropriate suboptimal approximations are presented to alleviate this computational complexity of the sensor-tasking problem. The submodular property of the mutual information measure is utilized to provide guarantees on the optimality of different approximat作者: chassis 時(shí)間: 2025-3-26 08:56 作者: Hyperopia 時(shí)間: 2025-3-26 13:19
https://doi.org/10.1007/978-3-663-07473-1hed by combining smart sensors and data analytics. This chapter highlights the shale revolution – through sustainable and environmental-friendly energy resources, illustrating how DDDAS helps the light hydrocarbons processing. While the early development of DDDAS in O&G has already been witnessed, t作者: Enzyme 時(shí)間: 2025-3-26 20:37 作者: Ingest 時(shí)間: 2025-3-26 23:11
https://doi.org/10.1007/978-3-662-32620-6odels, and ocean current monitoring data assimilation schemes with innovative modeling and adaptive sampling methods. The legacy systems are encapsulated at the binary level using software component methodologies. Measurement models are utilized to link the observed data to the dynamical model varia作者: 仔細(xì)閱讀 時(shí)間: 2025-3-27 02:43 作者: Deference 時(shí)間: 2025-3-27 06:06
Handbook of Dynamic Data Driven Applications Systems978-3-031-27986-7作者: 飲料 時(shí)間: 2025-3-27 12:27
Book 2023es aims to be a .reference source. of many of the important research and development efforts conducted under the rubric of DDDAS, and to also inspire the broader communities of researchers and developers about the potential in their respective areas of interest, of the application and the exploitati作者: 記憶 時(shí)間: 2025-3-27 16:28
https://doi.org/10.1007/978-3-662-29534-2ve, first-principles, and high-dimensional models of systems with corresponding instrumentation of these systems, be they natural, engineered, or societal. The application of the DDDAS paradigm has demonstrated that it can create more accurate and efficient modeling methods as well as more effective作者: Efflorescent 時(shí)間: 2025-3-27 18:21 作者: 狗舍 時(shí)間: 2025-3-27 23:28
Berechnung der Kegelradgeometrie,le (SrEn) Kalman filter (KF). The PC-SrEnKF method combines two techniques, the polynomial chaos expansion and the square root ensemble filter, which utilizes the Dynamic Data Driven Applications Systems (DDDAS) framework of data estimation and measurement-updated model enhancement. The PC-SrEnKF co作者: CURB 時(shí)間: 2025-3-28 04:46
https://doi.org/10.1007/978-3-663-02759-1 fast computation and actuation are required for monitoring and active control. An example is mitigation of thermoacoustic instabilities (TAI) in combustion systems; TAI may lead to severe damage in mechanical structures if the frequency of resulting pressure oscillations matches one of the natural 作者: 逃避系列單詞 時(shí)間: 2025-3-28 08:32
https://doi.org/10.1007/978-3-662-40054-8tocols for the computer-assisted study of the bifurcation structure of complex dynamical systems, such as those that arise in biology (neuronal networks, cell populations), multiscale systems in physics, chemistry, and engineering, and system modeling in the social sciences. An illustrative example 作者: Amenable 時(shí)間: 2025-3-28 13:43
,Die Lipide anderer Getreidest?rken,me monitoring and prediction of the behavior of complex physical systems. The developed DDDAS framework for materials damage prediction enables harnessing imaging data acquired in real-time to train physics-based models of system evolution, allowing computational prediction and dynamic control of th作者: PET-scan 時(shí)間: 2025-3-28 15:52
https://doi.org/10.1007/978-3-663-04412-3mbly processes. The proposed Nanoparticle Self-assembly Process Control and Tracking (NSPECT) methodology combines two different instrumentation technology of complementing capabilities: a dynamic light scattering (DLS) machine which can operate almost instantaneously at a high temporal resolution b作者: 細(xì)胞 時(shí)間: 2025-3-28 22:01 作者: 怎樣才咆哮 時(shí)間: 2025-3-29 02:05
https://doi.org/10.1007/978-3-663-04496-3 ultrasound sensors, maps small-scale structures for damage (e.g., holes, cracks) by localizing itself and the damage on a map. The combination of vision and ultrasound reduces the uncertainty in damage localization. The data storage and analysis take place exploiting cloud computing mechanisms, and作者: hedonic 時(shí)間: 2025-3-29 05:21
https://doi.org/10.1007/978-3-663-07464-9twork of static and dynamic sensors to accurately characterize complex dynamical environments. Examples of sensor-network management include assigning a set of sensors for surveillance and tracking multiple ground/aerial/marine targets. Further examples include adaptive sensing of large-scale spatia作者: ATRIA 時(shí)間: 2025-3-29 08:34 作者: Anticlimax 時(shí)間: 2025-3-29 14:22 作者: 到婚嫁年齡 時(shí)間: 2025-3-29 18:27
https://doi.org/10.1007/978-3-663-07477-9 and in reverse, the model controls the instrumentation to target measurements or apply coordinated actuation, and do so by combining the use of computer models, mathematical algorithms, and measurements systems to work with dynamic systems. Large-scale dynamic systems experience stochastic forces o作者: Acetaminophen 時(shí)間: 2025-3-29 19:54 作者: BROW 時(shí)間: 2025-3-30 00:53
The Dynamic Data Driven Applications Systems (DDDAS) Paradigm and Emerging Directions,ve, first-principles, and high-dimensional models of systems with corresponding instrumentation of these systems, be they natural, engineered, or societal. The application of the DDDAS paradigm has demonstrated that it can create more accurate and efficient modeling methods as well as more effective作者: transient-pain 時(shí)間: 2025-3-30 08:02
Dynamic Data-Driven Applications Systems and Information-Inference Couplingsiple physical or other models of a system and, in reverse, uses said models to control the instrumentation. This chapter is a synopsis of the DDDAS paradigm narrated through the use of a prototypical ., wherein a closed systems dynamics and optimization (SDO) cycle couples prediction, uncertainty qu
