This project advances the scientific knowledge on design methods for improving the resilience of civil infrastructures to disruptions. To improve resilience, critical services in civil infrastructure sectors must utilize new diagnostic tools and control algorithms that ensure survivability in the presence of both security attacks and random faults, and also include the models of incentives of human decision makers in the design process.

The presentation materials cover results obtained for the two above-mentioned projects.  The poster presents material on an algebraic approach to modeling systems with both continuous and discrete behavior.  The framework is based on process algebra, which was developed for discrete systems, and features the development of a tree-based semantic model, called generalized synchronization trees, that uniformly captures a very general notions of time.

The purpose of this research is to develop optimization and control techniques and integrate them with real-time simulation models to achieve load balancing in complex networks.  Our application case is the regional freight system.  Freight moves on rail and road networks which are also shared by passengers.  These networks today work independently, even though they are highly interdependent, and the result is inefficiencies in the form of congestion, pollution, and excess fuel consumption.

The vision of this work is to unite experts in granular mechanics, optimal control, and learning theory in order to define a methodology for advancing cyber-physical systems (CPS) involving a tight coupling of the physical with the cyber through dynamic interactions that must be learned online. The proposed work will advance the science of cyber-physical systems by more explicitly tying sensing, perception, and computing to the optimization and control of physical systems whose properties are variable and uncertain.

Objective: The objective of this project is to improve the performance and current capabilities of automotive active safety control systems by taking into account the interactions between the driver, the vehicle, the active safety system and the environment. The current approach in the design of automotive active safety systems follows the philosophy "one size fits all," in the sense that active safety systems are the same for all vehicles and do not take into account the skills, habits and state of the human driver who may operate the vehicle. 

How much should a person be allowed to interact with a controlled machine? If that machine is easily destabilized, and if the controller operating it is essential to its operation, the answer may be that the person should not be allowed any control authority at all. Using a combination of techniques coming from machine learning, optimal control, and formal verification, the proposed work focuses on a computable notion of trust that allows the embedded system to assess the safety of instruction.

We present a bifurcation analysis of electrical alternans in the two-current Mitchell-Schaeffer (MS) cardiac-cell model using the theory of -decidability over the reals. Electrical alternans is a phenomenon characterized by a variation in the successive Action Potential Durations (APDs) generated by a single cardiac cell or tissue.