Handbook of Dynamic Data Driven Applications Systems

1 Introduction to Dynamic Data Driven Applications Systems.- 2 Tractable Non-Gaussian Representation in Dynamic Data Driven Coherent Fluid Mapping.- 3 Dynamic Data-Driven Adaptive Observations in Data Assimilation for Multi-scale Systems.- 4 Dynamic Data-Driven Uncertainty Quantification via Polynomial Chaos for Space Situational Awareness.- 5 Towards Learning Spatio-Temporal Data Stream Relationships for Failure Detection in Avionics.- 6 Markov Modeling of Time Series via Spectral Analysis for Detection of Combustion Instabilities.- 7 Dynamic Space-Time Model for Syndromic Surveillance with Particle Filters and Dirichlet Process.- 8 A Computational Steering Framework for Large-Scale Composite Structures.- 9 Development of Intelligent and Predictive Self-Healing Composite Structures using Dynamic Data-Driven Applications Systems.- 10 Dynamic Data-Driven Approach for Unmanned Aircraft Systems aero-elastic response analysis.- 11 Transforming Wildfire Detection and Prediction using New and Underused Sensor and Data Sources Integrated with Modeling.- 12 Dynamic Data Driven Application Systems for Identification of Biomarkers in DNA Methylation.- 13 Photometric Steropsis for 3D Reconstruction of Space Objects.- 14 Aided Optimal Search: Data-Driven Target Pursuit from On-Demand Delayed Binary Observations.- 15 Optimization of Multi-Target Tracking within a Sensor Network via Information Guided Clustering.- 16 Data-Driven Prediction of Confidence for EVAR in Time-varying Datasets.- 17 DDDAS for Attack Detection and Isolation of Control Systems.- 18 Approximate Local Utility Design for Potential Game Approach to Cooperative Sensor Network Planning.- 19 Dynamic Sensor-Actor Interactions for Path-Planning in a Threat Field.- 20 Energy-Aware Dynamic Data-Driven Distributed Traffic Simulation for Energy and Emissions Reduction.- 21 A Dynamic Data-Driven Optimization Framework for Demand Side Management in Microgrids.- 22 Dynamic Data Driven Partitioning of Smart Grid Using

Dr. 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. Recognitions include the Military Sensing Society Mignogna leadership in data fusion award, IEEE Aerospace and Electronics Systems Society Mimno best magazine paper award, IEEE Russ bioengineering award, and founding member of the International Society of Information Fusion (ISIF). 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 AIAA, Fellow of SPIE, and Fellow of IEEE.

 

Dr. Sai Ravela directs the Earth Signals and Systems Group with research interests in Dynamic Data Driven Observing Systems at the Massachusetts Institute of Technology (MIT). He has made key contributions to Dynamic Data Driven cooperative autonomous observation of fluids, atmosphere, wildlife, retail intelligence, and micro-positioning radar. He has pioneered DDDAS concepts, and organized the first three DDDAS conferences that form the basis of this book. He has over 100 publications and patents, is the co-founder of Windrisktech LLC and E5 Aerospace LLC, and is a recipient of the MIT Infinite Kilometer award for exceptional research and outstanding mentorship.

 

Dr. 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