標(biāo)題: Titlebook: Handbook of Dynamic Data Driven Applications Systems; Erik Blasch,Sai Ravela,Alex Aved Book 20181st edition Springer Nature Switzerland AG [打印本頁(yè)] 作者: rupture 時(shí)間: 2025-3-21 16:26
書目名稱Handbook of Dynamic Data Driven Applications Systems影響因子(影響力)
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書目名稱Handbook of Dynamic Data Driven Applications Systems網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Handbook of Dynamic Data Driven Applications Systems被引頻次
書目名稱Handbook of Dynamic Data Driven Applications Systems被引頻次學(xué)科排名
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書目名稱Handbook of Dynamic Data Driven Applications Systems讀者反饋
書目名稱Handbook of Dynamic Data Driven Applications Systems讀者反饋學(xué)科排名
作者: 自傳 時(shí)間: 2025-3-21 21:35
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作者: ascetic 時(shí)間: 2025-3-22 03:14 作者: 含沙射影 時(shí)間: 2025-3-22 08:09 作者: Frequency 時(shí)間: 2025-3-22 09:34
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作者: Ossification 時(shí)間: 2025-3-22 16:45
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作者: Eeg332 時(shí)間: 2025-3-22 20:20
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作者: 軟弱 時(shí)間: 2025-3-22 23:16 作者: 越自我 時(shí)間: 2025-3-23 01:34 作者: 關(guān)節(jié)炎 時(shí)間: 2025-3-23 07:07
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作者: EVADE 時(shí)間: 2025-3-23 09:55 作者: 卵石 時(shí)間: 2025-3-23 17:53 作者: 小隔間 時(shí)間: 2025-3-23 21:52 作者: Crayon 時(shí)間: 2025-3-23 22:17 作者: 晚來(lái)的提名 時(shí)間: 2025-3-24 05:41
Strahlung der neuen radioaktiven Substanzen,models, run-time measurements, statistical methods, and computation architectures. One of the foremost applications of DDDAS successes was environmental assessment of natural disasters such as wild fire monitoring and volcanic plume detection. Monitoring the atmosphere with DDDAS principles has evol作者: obviate 時(shí)間: 2025-3-24 07:44 作者: 箴言 時(shí)間: 2025-3-24 14:33 作者: 首創(chuàng)精神 時(shí)間: 2025-3-24 18:33 作者: reception 時(shí)間: 2025-3-24 20:08
https://doi.org/10.1007/978-3-662-24787-7e 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作者: 悲觀 時(shí)間: 2025-3-25 01:44 作者: Exposition 時(shí)間: 2025-3-25 04:49
W. Jawtusch,G. Schuster,R. Jaeckelucture. 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作者: 秘方藥 時(shí)間: 2025-3-25 08:48 作者: corpuscle 時(shí)間: 2025-3-25 13:30 作者: 旅行路線 時(shí)間: 2025-3-25 15:53 作者: 舊石器 時(shí)間: 2025-3-25 20:19
https://doi.org/10.1007/978-3-663-05030-8acteristics and condition, and weather, notably wind. Recent advances include the Coupled Atmosphere-Wildland Fire Environment (CAWFE) modeling system, which couples a . optimized for modeling fine-scale airflows in complex terrain with fire behavior algorithms, capturing how the fire “creates its o作者: 玷污 時(shí)間: 2025-3-26 03:16
Zusammenfassung der Ergebnisse,DNA) sequence. Epigenetic modifications play a crucial role in development and differentiation of various diseases including cancer. The specific epigenetic alteration that has garnered a great deal of attention is DNA methylation, i.e., the addition of a methyl-group to cytosine. Recent studies hav作者: 流眼淚 時(shí)間: 2025-3-26 04:54
https://doi.org/10.1007/978-3-663-20276-9hms and methods for shape estimation form an integral element of a Dynamic Data Driven Application System (DDDAS) for enhancing space situational awareness, where, sensor tasking and scheduling operations are carried out based upon the RSO orbital and geometric attributes, as estimated from terrestr作者: abreast 時(shí)間: 2025-3-26 08:50 作者: 心胸開闊 時(shí)間: 2025-3-26 15:32
Book 20181st edition 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..? ? ?.作者: 最高峰 時(shí)間: 2025-3-26 20:14
,Aufbau und Eigenschaften der Prüfst?nde, 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.作者: 針葉類的樹 時(shí)間: 2025-3-26 23:52 作者: Dealing 時(shí)間: 2025-3-27 01:08 作者: Living-Will 時(shí)間: 2025-3-27 07:28 作者: Cumulus 時(shí)間: 2025-3-27 10:29 作者: Physiatrist 時(shí)間: 2025-3-27 14:54
https://doi.org/10.1007/978-3-662-40055-5moplastic healing agent performs the healing process. For this purpose, double-cantilever beam (DCB) tests were carried out to quantify the healing efficiency in terms of Mode-I interlaminar fracture toughness (..) following the ASTM D5528-13 testing protocol and the healing efficiencies were calcul作者: 無(wú)彈性 時(shí)間: 2025-3-27 19:52
https://doi.org/10.1007/978-3-663-04499-4t. In this phase, with the aeroelastic simulator, preliminary stability envelopes are constructed to determine the flutter boundary of the aircraft with damage and without damage to the aircraft. By using available simulation results, an initial meta-model is trained offline. During the online phase作者: 痛打 時(shí)間: 2025-3-27 22:25 作者: 恫嚇 時(shí)間: 2025-3-28 02:14
Zusammenfassung der Ergebnisse, this goal, we propose a novel high dimensional learning framework inspired by the dynamic data driven application systems paradigm to identify the biomarkers, determine the outlier(s) and improve the quality of the resultant disease detection. The proposed framework starts with a principal componen作者: 開花期女 時(shí)間: 2025-3-28 06:30 作者: Abominate 時(shí)間: 2025-3-28 12:57
P. H?lemann,R. Hasselmann,G. Dixe the belief on the trajectory of the target, and the searcher actively steers the measurement process to improve its knowledge about the location of the target. In particular, we make two main contributions. The first regards the target trajectory estimation. We show how to perform optimal Bayesian作者: BLANK 時(shí)間: 2025-3-28 14:59
ication 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..? ? ?.978-3-319-95504-9作者: 確定方向 時(shí)間: 2025-3-28 22:27
Handbook of Dynamic Data Driven Applications Systems作者: 托人看管 時(shí)間: 2025-3-29 02:10 作者: 責(zé)怪 時(shí)間: 2025-3-29 06:34
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.作者: Substance-Abuse 時(shí)間: 2025-3-29 07:22
Dynamic Data-Driven Uncertainty Quantification via Polynomial Chaos for Space Situational Awarenessmine the likelihood of satellite collisions in space..The main focus of this chapter is the application of a new Polynomial Chaos based Uncertainty Quantification (UQ) approach for Space Situational Awareness (SSA). The challenge of applying UQ to SSA is the long-term integration problem, where simu作者: 胰臟 時(shí)間: 2025-3-29 12:11
Towards Learning Spatio-Temporal Data Stream Relationships for Failure Detection in Avionics to minimize least squares error for given training data. The Bayesian approach classifies operating modes according to supervised offline training and can discover new statistically significant modes online. As shown in Tuninter 1153 simulation result, dynamic Bayes classifier finds discrete error 作者: Pedagogy 時(shí)間: 2025-3-29 18:06 作者: Interlocking 時(shí)間: 2025-3-29 20:45
A Computational Steering Framework for Large-Scale Composite Structuresctures, such as wind turbine blades and towers. The proposed DISCERN framework continuously and dynamically integrates the SHM data into the FSI analysis of these structures. This capability allows one to: (1) Shelter the structures from excessive stress levels during operation; (2) Make informed de作者: 細(xì)胞學(xué) 時(shí)間: 2025-3-30 01:28 作者: 支架 時(shí)間: 2025-3-30 05:20
Dynamic Data-Driven Approach for Unmanned Aircraft Systems and Aeroelastic Response Analysist. In this phase, with the aeroelastic simulator, preliminary stability envelopes are constructed to determine the flutter boundary of the aircraft with damage and without damage to the aircraft. By using available simulation results, an initial meta-model is trained offline. During the online phase作者: chronicle 時(shí)間: 2025-3-30 08:12 作者: 磨碎 時(shí)間: 2025-3-30 14:23
Dynamic Data Driven Application Systems for Identification of Biomarkers in DNA Methylation this goal, we propose a novel high dimensional learning framework inspired by the dynamic data driven application systems paradigm to identify the biomarkers, determine the outlier(s) and improve the quality of the resultant disease detection. The proposed framework starts with a principal componen作者: CURT 時(shí)間: 2025-3-30 17:58 作者: Ornithologist 時(shí)間: 2025-3-31 00:25
