Modern electric power grid is a complex, interconnected cyber physical system (CPS) that forms the lifeline of our society. Reliable, secure, and efficient operation of the grid are of paramount importance to national security and economic well-being. Recent trends in security indicate the increasing threat of cyber-based attacks, both in numbers and sophistication, on energy and other critical infrastructure systems of our nation and the world in general.

As an indispensable link of the life-cycle of AM, end-part quality control in Cyber-Physical Additive Manufacturing Systems (CPAMS) is made difficult by enormous differences in product designs/varieties. Statistical monitoring of additive manufacturing (AM) processes faces major challenge due to the nature of one-of-a-kind manufacturing. This posters puts forth a prescriptive SPC scheme to monitor shape deformation from shape to shape. Only a limited number of test shapes are required to establish control limits.

This poster surveys results in the area of specification reconstruction for system models obtained by teams from the University of Maryland and Fraunhofer Center as part of the NSF CPS Frontier program "CyberCardia". The specification-reconstruction problem is this: given a system model, and a template of a property describing a pattern of behavior, determine how to complete the template so that the resulting property holds for all behaviors of the system.

Our results concern using program‐slicing to model software components in a CPS, with applications to verification of aviation software.

This work examines how to get safety and security in Internet of Things (IoT) systems where multiple devices (things), each designed in isolation from others, are brought together to form a networked system, controlled via one or more software applications ("apps"). "Things" in an IoT environment can include simple devices such as switches, lightbulbs, smart locks, thermostats, and safety alarms as well as complex systems such as appliances, smartphones, and cars.

Various critical decision-making and control problems associated with engineering and socio-technical systems are subject to uncertainties. Large-scale data collected from the Internet-of-Things and cyber-physical systems can provide information about the probability distribution of these uncertainties, such as product demand in supermarkets. Such distributional information can be used to dramatically improve the performance of closed-loop systems if they adopt appropriate controllers, which reduce the conservativeness of classical techniques, such as robust control.

The overarching project goal is to advance the design of opportunistic state-triggered aperiodic controllers for networked cyber-physical systems.  This poster considers the problem of opportunistic human-robot collaboration to solve multi-objective optimization problems. We consider scenarios where a human decision maker works with a robot in a supervisory manner in order to find the best Pareto solution to a given optimization problem.  The human has a time-invariant function that represents the value she gives to the different outcomes.