LEADER 07473nam 22007455 450 001 996550557203316 005 20230914142501.0 010 $a3-031-27986-7 024 7 $a10.1007/978-3-031-27986-7 035 $a(MiAaPQ)EBC30745211 035 $a(Au-PeEL)EBL30745211 035 $a(DE-He213)978-3-031-27986-7 035 $a(PPN)272739251 035 $a(EXLCZ)9928225225700041 100 $a20230914d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aHandbook of Dynamic Data Driven Applications Systems$b[electronic resource] $eVolume 2 /$fedited by Frederica Darema, Erik P. Blasch, Sai Ravela, Alex J. Aved 205 $a1st ed. 2023. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2023. 215 $a1 online resource (937 pages) 311 08$aPrint version: Darema, Frederica Handbook of Dynamic Data Driven Applications Systems Cham : Springer International Publishing AG,c2023 9783031279850 327 $aThe Dynamic Data Driven Applications Systems (DDDAS) Paradigm and Emerging Directions -- Dynamic Data Driven Applications Systems and Information-Inference Couplings -- Polynomial Chaos Expansion Based Nonlinear Filtering for Dynamic State Estimation -- Measure-Invariant Symbolic Systems for Pattern Recognition and Anomaly Detection -- Equation?Free Computations as DDDAS Protocols for Bifurcation Studies: A Granular Chain Example -- A Stochastic Dynamic Data-Driven Framework for Real-time Prediction of Materials Damage in Composites -- Dynamic Data-Driven Monitoring of Nanoparticle Self Assembly Processes -- From Data to Decisions: A Real-Time Measurement -Inversion-Prediction-Steering Framework for Hazardous Events and Structural Health Monitoring -- Bayesian Computational Sensor Networks: Small-Scale Structural Health Monitoring -- A Dynamic Data Driven Sensor Tasking with Application of Aerospace Systems -- Dynamic Data-Driven Application Systems for Reservoir Simulation-Based Optimization: Lessons Learned and Future Trends -- DDDAS within the Oil and Gas Industry -- A Simulation-Based Online Dynamic Data-Driven Framework for Large-Scale Wind-turbine Farm Systems Operation -- Towards Dynamic Data Driven Systems for Rapid Adaptive Interdisciplinary Ocean Forecasting -- Towards Cyber-Eco Systems: Networked Sensing, Inference and Control for Ecological and Agricultural Systems -- An Energy-Aware Airborne Dynamic Data-Driven Application System for Persistent Sampling and Surveillance -- Using Dynamic Data Driven Cyberinfrastructure for Next Generation Wildland Fire Intelligence -- Autonomous Monitoring of Wildfires with Vision-Equipped UAS and Temperature Sensors via Evidential Reasoning -- Airborne Fire Detection and Modeling using Unmanned Aerial Vehicles Imagery: Datasets and Approaches -- DDDAS-based Remote Sensing -- Advances in Domain Adaptation for Aerial Imagery -- Retrospective Cost Parameter Estimation with Application to Space Weather Modeling -- A Dynamic Data Driven Approach to Space Situational Awareness -- Data driven cancer research with digital microscopy and Pathomics -- Robust Data Driven Region of Interest Segmentation for Breast Thermography -- Adaptive Data Stream Mining (DSM) Systems -- Deception Detection in Videos using Robust Facial Features with Attention Feedback -- Manufacturing the Future via Dynamic Data Driven Applications Systems (DDDAS) -- DDDAS in the Social Sciences -- Anomaly-Detection Defense against Test-Time Evasion Attacks on Robust DNNs -- Dynamic Data-Driven Approach for Cyber Resilient and Secure Critical Energy Systems -- Dynamic Network-centric Multi-cloud Platform for Real-Time and Data-Intensive Science Workflows -- INDICES: Applying DDDAS Principles for Performance Interference?aware Cloud?to?Fog Application Migration -- Adaptive Routing for Hybrid Photonic-Plasmonic (HyPPI) Interconnection Network for Manycore Processors using DDDAS on the Chip. 330 $aThis Second Volume in the series Handbook of Dynamic Data Driven Applications Systems (DDDAS) expands the scope of the methods and the application areas presented in the first Volume and aims to provide additional and extended content of the increasing set of science and engineering advances for new capabilities enabled through DDDAS. The methods and examples of breakthroughs presented in the book series capture the DDDAS paradigm and its scientific and technological impact and benefits. The DDDAS paradigm and the ensuing DDDAS-based frameworks for systems? analysis and design have been shown to engender new and advanced capabilities for understanding, analysis, and management of engineered, natural, and societal systems (?applications systems?), and for the commensurate wide set of scientific and engineering fields and applications, as well as foundational areas. The DDDAS book series aims to be a reference source of many of the important research and development efforts conducted under the rubric of DDDAS through the examples and case studies presented, either within their own field or other fields of study. DDDAS has proven to be a transformative technology as data has become the third part of the triad, with theory and computation as the other two parts. Initially DDDAS was part of control theory but as data have become ubiquitous there has been a paradigm shift, initiated by DDDAS, from simulation to effectively using data for prediction. John Cherniavsky, retired, as Division Director, NSF - US National Science Foundation The DDDAS paradigm integrates and enhances the deepest, most well-grounded foundations and tools - both from expert based methods and from learning-based methods, building well beyond more popular and limited forms of AI. This book provides a unique breadth and depth of scope across many science and technology fields, showing how DDDAS is an overarching concept that connects and unifies the wide diversity across these fields. Paul Werbos, retired, as Program Director, NSF - US National Science Foundation . 606 $aComputer simulation 606 $aBig data 606 $aComputers, Special purpose 606 $aDynamics 606 $aNonlinear theories 606 $aDynamical systems 606 $aQuantitative research 606 $aComputer Modelling 606 $aBig Data 606 $aSpecial Purpose and Application-Based Systems 606 $aApplied Dynamical Systems 606 $aDynamical Systems 606 $aData Analysis and Big Data 615 0$aComputer simulation. 615 0$aBig data. 615 0$aComputers, Special purpose. 615 0$aDynamics. 615 0$aNonlinear theories. 615 0$aDynamical systems. 615 0$aQuantitative research. 615 14$aComputer Modelling. 615 24$aBig Data. 615 24$aSpecial Purpose and Application-Based Systems. 615 24$aApplied Dynamical Systems. 615 24$aDynamical Systems. 615 24$aData Analysis and Big Data. 676 $a003.3 700 $aDarema$b Frederica$01427867 701 $aBlasch$b Erik P$01427868 701 $aRavela$b Sai$01427869 701 $aAved$b Alex J$01427870 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996550557203316 996 $aHandbook of Dynamic Data Driven Applications Systems$93562777 997 $aUNISA LEADER 02274nam0 22004813i 450 001 TO01031515 005 20251003044409.0 100 $a20030221d2001 ||||0itac50 ba 101 | $aita 102 $ait 181 1$6z01$ai $bxxxe 182 1$6z01$an 183 1$6z01$anc$2RDAcarrier 200 1 $aˆIl ‰trasporto internazionale di container, la portualita italiana, la logistica$fCNEL, Consiglio nazionale dell'economia e del lavoro 210 $aRoma$cCNEL$d2001 215 $a104 p.$d24 cm 225 | $aRapporto 300 $aIn cop.: Rapporto 300 $aAtti di convegno. 410 0$1001TO01107204$12001 $aRapporto 606 $aECONOMIA MARITTIMA$2FIR$3MILC125652$9I 606 $aTrasporti$xItalia$2FIR$3CFIC030579$9E 676 $a387.510945$9TRASPORTI MARITTIMI. 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