DDDAS16 Presentations

Keynote Talks

Next-Generation Digital Twin Meets DDDAS - Dr. David Parekh

New Challenges for DDDAS For Broad Societal Impact - Sangtae “Sang” Kim

Day 1: August 9, 2016

Session-1: Structures Monitoring

1.    Multiscale DDDAS Framework for Aerospace Composite Structures with Emphasis on Unmanned Aerial Vehicles – A. Korobenko et al.
2.    Structural Damage Growth Prediction via Integration of Model Response Prediction and Bayesian Estimation – Y. Liu et al.
3.    Use of Operationally Flexible Robust Optimization in Dynamic Data Driven Application Systems – Kania et al.
4.    A Dynamic Data-driven Stochastic State-Awareness Framework for the Next Generation of Bio-inspired Fly-by-feel Aerospace Vehicles – Kopsaftopoulos et al.

Session-2: Processes Monitoring


1.    Towards Learning Spatio-Temporal Data Stream Relationships for Failure Detection in Avionics- Varela et al.
2.    Anomaly Detection and Fault Classification in Large Flight Data using Multi-modal Deep Learning Ap-proaches – Reddy et al. (30min, 2 abs).
3.    Markov Modeling of Time Series Data via Spectral Analysis – A. Ray et al.
4.    Dynamic Data-Driven Monitoring of Nanoparticle Self Assembly Processes – C. Park et al.
5.    Online Droplet Detection and Correction System for Inkjet Metal 3D Printing Process – W.  Xu et al.

Day 2: August 10, 2016

Session-3: Assimilation, UQ

1.    Cooperative Autonomous Observation and Dynamically Deformable and Resampled Manifolds for the entire DDDAS cycle  - Ravela et al. (30m, 2 abstracts)
2.    Dynamic Data-Driven Adaptive Observations in Data Assimilation for Multi-Scale Systems - Namachchivaya et al.
3.    Dynamic Data-Driven Uncertainty Quantification via Generalized Polynomial Chaos - Linares et al.

Session-4: Earth and Space Systems/Environments

1.    Applications of Photometric Stereopsis for Shape Estimation of Resident Space Objects (RSOs) - Singla et al.
2.    Dynamic Data-Driven Adaptive Sampling and Monitoring of Big Spatial-Temporal Data Streams for Real-Time Solar Flare Detection - K. Liu et al.
3.    Dynamic-Data Driven Estimation of Plumes using Adaptive Sampling - Gastonis et al.
4.    Transforming Wildfire Detection and Prediction using New and Underused Sensor and Data Sources In-tegrated with Modeling – Coen et al.

Session-5: Multisensing

1.    A DDDAS Approach to Sensor Trajectory Generation – Lin et al.
2.    Approximate Potential Game Approach for Cooperative Sensor Network Planning – S. Lee et al.
3.    Dynamic Sensor-Actor Interactions for Path Planning in an Uncertain Threat Field – R. Cowlagi et al.

Session-6: Tracking Methods

1.    Aided Optimal Search: Data-Driven Target Pursuit from On-Demand Delayed Binary Observations – L. Carlone et al.
2.    Optimization of Target Tracking with a Sensor Network by Using Expected Likelihood Measurements – Soderlund et al.

Session-7: Tracking Methods Contd.

1.    Predictive modelling using coarse and fine evidence – Ramamoorthy
2.    Sensor Selection for Target Tracking in Sensor Networks Based on a Proximal Algorithm & Sign-Aware Distributed Approximate Message Passing – Niu et al.
3.    Data-driven Prediction of Confidence and EVAR in Time-varying Datasets with Online-Computable Error Bounds – Chowdary et al.
4.    New Bandit and MDP Models that Provide Optimal DDDA Methods – Cowan et al.

Day 3: August 11, 2016

Session-8: Coordinated Control

1.    A DDDAS Paradigm for Scalable Sensor Actuator Networks – Agha et al.
2.    Model-based Fuzzy Logic Classifier Synthesis for Optimization of Data-Adaptable Embedded Systems – Lysecky et al.
3.    DDDAS for Attacks Detection, Isolation, and Reconfiguration of Control Systems – Cardenas et al.

Session-9: Energy and Energy-Aware Systems

1.    A DDDAS-based Autonomous Situational Awareness System for 3-Dimentional Border Surveillance – Son et al.
2.    Energy-Aware Dynamic Data-Driven Distributed Traffic Simulations – Fujimoto et al.
3.    Energy-Aware Airborne Dynamic Data Driven Application Systems for Persistent Surveillance and Sampling – Frew et al.
4.    DDDAS for interruptible load management – Celik et al.
5.    Dynamic Data Driven Partitioning of Smart Grid Using Learning Methods – Nasiakou et al.

Session-10: Image and Video Computing, Methods


1.    Dynamic Data-Driven Geo-Location Via Matrix Factorization Clustering of Multi-View Imagery – Chakarecki et al.
2.    Dynamic Space-Time Model for Syndromic Surveillance with Particle Filters and Dirichlet Process – Zou et al.
3.    Dynamic, Data-Driven Processing of Multispectral Video Streams – Bhattacharyya et al.
4.    On Compression of Machine-derived Context Sets for Fusion of Multi-modal Sensor Data – Phoha et al.
5.    Data-driven Real-Time Crowd Behavior Analysis and Prediction – Bera et al.
6.    Dynamic Data Driven Application Systems (DDDAS) for Multimedia Content Analysis - Blasch et al.

Session–11: Biological Systems

1.    Modeling Transient Phenomena Using Dynamically-Data Driven Subspaces – Sapsis et al.
2.    A Dynamic Data-Driven Hierarchical Learning Model for Identification of Biomarkers in DNA Methyl-ation - Celik et al.
3.    Real-time Dynamic Data Driven System for Stress Management - Fink et al.
4.    Discrete Modeling, Discovery and Prediction for Evolving, Living Systems – Cohen et al.

Day 4: August 12, 2016

Session-12: Security & Computing Systems Environments

1.    Simulation-based Optimization as a Service for Dynamic Data-driven Applications Systems – Gokhale et al.
2.    Data Acquisition with Privacy Protection in Next Generation DDDAS Systems – Sunderam et al.
3.    Dynamic Data-Driven Policy-based Information Dissemination - Schermerhorn et al.
4.    Factory-on-wheels – Chaturvedi et al.
5.    A Model-driven Resource Allocation Framework for Dynamic Data Driven Applications Systems (DDDAS) on the Cloud – M. Khan et al.