Titlebook: Dynamic Data Driven Applications Systems; Third International Frederica Darema,Erik Blasch,Alex Aved Conference proceedings 2020 Springer

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Using Dynamic Data Driven Cyberinfrastructure for Next Generation Disaster Intelligenceh a goal to create an integrated system, data and visualization services, and workflows for wildfire monitoring, simulation, and response. Today, WIFIRE provides an end-to-end management infrastructure from the data sensing and collection to artificial intelligence and modeling efforts using a conti

Visual-Acuity

果核

Cupidity

reserve

A DDDAS Protocol for Real-Time Large-Scale UAS Flight Coordination a dynamic distributed protocol for reactive conflict management that serves a similar purpose, albeit functioning at a time-horizon in between strategic deconfliction and sensor-based conflict management. This DDDAS inspired approach obviates the need for any centralized control by having each UAS

FACT

Integrated Planning, Decision-Making, and Weather Modeling for UAS Navigating Complex Weathery suboptimal with respect to a single mission in order to sample the environment and update the model. This tasking reconfigures the observations gathered to target portions of the environment relevant to mission execution. Initial simulations show that this approach is able to reduce error in the m

一美元

Microgrid Operational Planning Using Deviation Clustering Within a DDDAS Framework sensing strategy and the second is the DC algorithm which reduces the execution time of the MG simulation. Numerical analysis was conducted on the IEEE-18 bus test network to assess the performance of the proposed framework and determine an appropriate threshold for clustering. The limitations of t

Deceit

Interpretable Deep Attention Model for Multivariate Time Series Prediction in Building Energy Systemortant timesteps and variables. We demonstrate the results using a public multivariate time series dataset collected from an air handling unit in a building heating, ventilation, and air-conditioning (HVAC) system. The model with enhanced interpretability does not compromise with the prediction accu

不能仁慈

Overcoming Stealthy Adversarial Attacks on Power Grid Load Predictions Through Dynamic Data Repairlements the predictor with a self-representative auto-encoder and works in an iterative manner. The auto-encoder is used to detect and reconstruct the likely adversarial part in the input data. Different reconstruction results come up given different sensitivity levels in detection. As new data flow