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Handbook of Dynamic Data Driven Applications Systems

Volume 1

Editors:

(view affiliations)
  • 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 general paradigm

  • Identification of contemporary concepts using DDDAS such as UAVs, surveillance, and computing

  • 3168 Accesses

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  • ISBN: 978-3-030-74568-4
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Table of contents (32 chapters)

  1. Front Matter

    Pages i-x
  2. Introduction to the Dynamic Data Driven Applications Systems (DDDAS) Paradigm

    • Erik P. Blasch, Frederica Darema, Dennis Bernstein
    Pages 1-32
  3. Measurement-Aware: Data Assimilation, Uncertainty Quantification

    1. Front Matter

      Pages 33-33
    2. Dynamic Data-Driven Adaptive Observations in Data Assimilation for Multi-scale Systems

      • Hoong C. Yeong, Ryne Beeson, N. Sri Namachchivaya, Nicolas Perkowski, Peter W. Sauer
      Pages 53-79
    3. Dynamic Data-Driven Uncertainty Quantification via Polynomial Chaos for Space Situational Awareness

      • Richard Linares, Vivek Vittaldev, Humberto C. Godinez
      Pages 81-99
  4. Signals-Aware: Process Monitoring

    1. Front Matter

      Pages 101-101
    2. Towards Learning Spatio-Temporal Data Stream Relationships for Failure Detection in Avionics

      • Sida Chen, Shigeru Imai, Wennan Zhu, Carlos A. Varela
      Pages 103-127
  5. Structures-Aware: Health Modeling

    1. Front Matter

      Pages 161-161
    2. Dynamic Data-Driven Approach for Unmanned Aircraft Systems Aero-elastic Response Analysis

      • R. Kania, A. Kebbie-Anthony, X. Zhao, S. Azarm, B. Balachandran
      Pages 201-219
  6. Environment-Aware: Earth, Biological, and Space Systems

    1. Front Matter

      Pages 221-221
    2. Dynamic Data Driven Application Systems for Identification of Biomarkers in DNA Methylation

      • Haluk Damgacioglu, Emrah Celik, Chongli Yuan, Nurcin Celik
      Pages 241-261
    3. Photometric Stereopsis for 3D Reconstruction of Space Objects

      • Xue Iuan Wong, Manoranjan Majji, Puneet Singla
      Pages 263-300
  7. Situation Aware: Tracking Methods

    1. Front Matter

      Pages 301-301
    2. Aided Optimal Search: Data-Driven Target Pursuit from On-Demand Delayed Binary Observations

      • Luca Carlone, Allan Axelrod, Sertac Karaman, Girish Chowdhary
      Pages 303-343

About this book

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 maximize the quality and timeliness of information they provide.

                                            Kelvin Droegemeier, Regents’ Professor of Meteorology at the University of Oklahoma; former Director of the White House Office of Science and Technology Policy

                                          

We may well be entering the golden age of data science, as society in general has come to appreciate the possibilities for organizational strategies that harness massive streams of data. The challenges and opportunities are even greater when the data or the underlying system are dynamic - and DDDAS is the time-tested paradigm for realizing this potential.

                          Sangtae Kim, Distinguished Professor of Mechanical Engineering and Distinguished Professor of Chemical Engineering at Purdue University

Keywords

  • DDDAS
  • Controls
  • Instrumentation
  • Big Data
  • High performance computing
  • Cyber physical systems
  • UAVs
  • data fusion
  • feature fusion
  • decision fusion
  • information fusion
  • Environmental Modeling
  • Environmental Analysis
  • Architectures
  • Statistical modeling
  • data assimilation

Reviews

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 maximize the quality and timeliness of information they provide.

            Kelvin Droegemeier, Regents’ Professor of Meteorology at the University of Oklahoma; former Director of the White House Office of Science and Technology Policy

             We may well be entering the golden age of data science, as society in general has come to appreciate the possibilities for organizational strategies that harness massive streams of data. The challenges and opportunities are even greater when the data or the underlying system are dynamic - and DDDAS is the time-tested paradigm for realizing this potential.

             Sangtae Kim, Distinguished Professor of Mechanical Engineering and Distinguished Professor of Chemical Engineering at Purdue University

 

Editors and Affiliations

  • Air Force Office of Scientific Research, Arlington, USA

    Erik P. Blasch

  • InfoSymbiotics Systems Society, Boston, USA

    Frederica Darema

  • Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, USA

    Sai Ravela

  • Air Force Research Lab, Rome, USA

    Alex J. Aved

About the editors

Erik P. Blasch is a program officer with the Air Force Office of Scientific Research. His focus areas are in multi-domain (space, air, ground) data fusion, target tracking, pattern recognition, and robotics. He has authored 750+ scientific papers, 22 patents, 30 tutorials, and 5 books. His recognitions include the Military Sensing Society Mignogna Leadership in Data Fusion Award, IEEE Aerospace and Electronics Systems Society Mimno Best Magazine Paper Award, and IEEE Russ Bioengineering Award. He was also a founding member of the International Society of Information Fusion (ISIF). His previous appointments include adjunct associate professor at Wright State University, exchange scientist at Defense Research and Development Canada, and officer in the Air Force Research Laboratory. Dr. Blasch is an associate fellow of American Institute of Aeronautics and Astronautics (AIAA), fellow of the Society of Photo-Optical and Instrumentation Engineers (SPIE) and fellow of the Institute of Electrical and Electronics Engineers (IEEE).

Dr. Frederica Darema: retired as Senior Executive Service (SES) member and Director of the Air Force Office of Scientific Research, in Arlington, Virginia, where she led the entire basic research investment for the AF and served as Research Director in the Air Force’s Chief Data Office, and as Associate Deputy Assistant Secretary the Air Force Office for Science, Technology and Engineering.  Prior career history includes: research staff positions at the University of Pittsburgh, Brookhaven National Laboratory, and Schlumberger-Doll; and management and executive-level positions at: the T.J.Watson IBM Research Center and the IBM Corporate Strategy Group; the National Science Foundation and the Defense Advanced Research Projects Agency; and Director of the AFOSR Directorate for Information, Math, and Life Sciences. Dr. Darema, PhD in Nuclear Physics, is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), among other professional recognitions. She pioneered the DDDAS paradigm and since 2000 has organized and led research initiatives, programs, workshops, conferences, and other forums, to foster and promote DDDAS-based science and technology advances.

Dr. Ravela, Ph.D. 2002, directs the Earth Signals and Systems Group (ESSG) in the Earth Atmospheric and Planetary Sciences (EAPS) department at the Massachusetts Institute of Technology. His primary interests are in statistical pattern recognition, stochastic nonlinear systems, and computational intelligence with application to earth, planets, climate, and life. Dr. Ravela has pioneered dynamic data driven observing systems for wildlife and fluids, the latter with application from the laboratory to localized atmospheric phenomena. He has advanced several DDDAS topics with new methods for application to coherent fluid dynamical regimes. Dr. Ravela proposed and co-organized the Dynamic Data Driven Environmental Systems Science Conference (DyDESS 2014, Cambridge), and then co-organized the first, second, and third general DDDAS conferences (2016 Hartford, 2017 Cambridge, 2020 MIT/Online). Dr. Ravela also teaches Machine Learning with System Dynamics and Optimization, which introduces the informative approach, a key DDDAS concept, to design Learning and Hybrid Stochastic Systems and solve inverse problems and inference.

Alex J. Aved is a senior researcher with the Air Force Research Laboratory, Information Directorate, Rome, NY, USA. His research interests include multimedia databases, stream processing (via CPU, GPU, or coprocessor), and dynamically executing models with feedback loops incorporating measurement and error data to improve the accuracy of the model. He has published over 50 papers and given numerous invited lectures. Previously he was a programmer at the University of Central Florida and database administrator and programmer at Anderson University.

Bibliographic Information

  • Book Title: Handbook of Dynamic Data Driven Applications Systems

  • Book Subtitle: Volume 1

  • Editors: Erik P. Blasch, Frederica Darema, Sai Ravela, Alex J. Aved

  • DOI: https://doi.org/10.1007/978-3-030-74568-4

  • Publisher: Springer Cham

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2022

  • Hardcover ISBN: 978-3-030-74567-7

  • eBook ISBN: 978-3-030-74568-4

  • Edition Number: 2

  • Number of Pages: X, 766

  • Number of Illustrations: 41 b/w illustrations, 228 illustrations in colour

  • Topics: Computer Modelling, Computer and Information Systems Applications

Buying options

eBook
USD 219.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-74568-4
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Hardcover Book
USD 279.99
Price excludes VAT (USA)