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