The proposed research has two key objectives (a) develop an accurate understanding of the cascading failure mechanism and its prevention, and (b) develop a recovery plan following blackouts under uncertainty of failure locations and budget constraints. The quasi-steady-state (QSS) model of power grid used in literature for studying cascade propagation produces inaccurate results towards the later stages of blackouts, whereas a fully dynamic model is impractical for large-scale statistical analyses.

The overarching goal of the project is to detect spurious or maliciously injected sensor data originating from cyber-attacks that can have similar appearance as system’s dynamic response and correct the same. Energy CPS is chosen as an example system. Thus far we have obtained positive results using principal component pursuit and robust PCA approach. In this poster, a Kernel PCA-based approach is used for event detection, which is resilient to data anomaly due to spurious outliers or false data injection attack on synchrophasor data stream.

The goal of this project is to create a scalable and robust cyber-physical system (CPS) framework for the observation and control of the functional interdependencies between bridge structures (stationary physical systems) and trucks (mobile physical agents).  A CPS framework (Figure 1) is being developed to monitor and control trucks within a single highway corridor to manage the imposed loads and the consumption of structural life by trucks on highway infrastructure including bridges.

In this work, we use control improvisation to synthesize voluntary lane-change policy that meets human preferences under given traffic environments. We first train Markov models to describe traffic patterns and the motion of vehicles responding to such patterns using traffic data. The trained parameters are calibrated using control improvisation to ensure the traffic scenario assumptions are satisfied. Based on the traffic pattern, vehicle response models, and Bayesian switching rules, the lane-change environment for an automated vehicle is modeled as a Markov decision process.

Explanation of Demonstration: Scenic is a new domain-specific probabilistic programming language for specifying the environment of a cyber-physical system. Scenic can be used to describe scenarios of interest and automatically generate realistic data from them for testing or training purposes. We will demonstrate how to use Scenic to generate images of traffic scenarios to improve the performance of an object-recognition neural network for autonomous driving.

Episodic brain disorders such as epilepsy have a considerable impact on a patient’s quality of life and may be life-threatening when seizures cannot be controlled with medications. We are creating a second generation brain-implantable sensing and stimulating device (BISSD) based on CPS principles and practice. The BISSD will be composed of modules placed intracranially to continuously monitor brain state and vulnerability to seizure and intervene with electrical stimulation to block the development of seizure.

Trustworthy operation of industrial control systems depends on secure and real-time code execution on the embedded programmable logic controllers (PLCs). The controllers monitor and control the critical infrastructures, such as electric power grids and healthcare platforms, and continuously report back the system status to human operators. We present Zeus, a contactless embedded controller security monitor to ensure its execution control flow integrity. Zeus leverages the electromagnetic emission by the PLC circuitry during the execution of the controller programs.

The VISualization of Terrestrial and Aquatic Systems (VISTAS) team, an NSF/BIO/ABI-funded collaboration among environmental-, computer-, and social-scientists, has integrated new technologies and computer science research into terrain visualization software for environmental scientists.  The system allows scientists to overlay 2D data