The formalization of system engineering models and approaches.
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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.
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Wide-area management of terrestrial scale infrastructures often involves human operators, who are sandwiched between physical-world systems and cyber- assets. These Management Coupled Cyber-Physical Infrastructures (MCCPIs) are subject to diverse threats that can propagate across network elements. In this research effort, a layered network modeling paradigm for MCCPIs is developed, and threats to cyber, physical, and human assets are modeled at several resolutions.
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This poster presents a uniform method for translating an arbitrary nondeterministic nite automaton (NFA) into a deterministic mass action chemical reaction network (CRN) that simulates it. The CRN receives its input as a continuous time signal consisting of concentrations of chemical species that vary to represent the NFA's input string in a natural way. The CRN exploits the inherent parallelism of chemical kinetics to simulate the NFA in real time, and its size is linear in the number of states of the NFA. The compiler of Chen et al.
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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.
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Starting from a database of 100's of real patient electrogram records, we describe how to develop and use a large
in-silico cohort consisting of 10,000+ heart models to improve the planning and execution of a clinical trial (CT)
for implantable cardioverter defibrillators (ICDs). We illustrate our approach by applying it retrospectively to a
real CT that compares two discrimination algorithms (DA) within ICDs for the detection of potentially fatal
cardiac arrhythmias. The CT posited that one algorithm would be better than the other but the results of the trial
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Cyber-physical systems (CPS) have become increasingly prevalent in appli- cations including health care, energy, and transportation. The tight coupling between cyber and physical components of CPS implies that cyber attacks can degrade the safety, availability, and performance of physical components. The cyber components also introduce multiple entry points to the CPS, lowering the cost of attacks compared to purely physical systems.
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Start Date: September 1, 2011
The goal of this project is to integrate digital microfluidics systems with thin-film photodetectors in the top plate to realize biochemical target sensing using fluorescence. System control, adaptation, and reconfiguration through software will lead to a general-purpose lab-on-chip computing platform, in the same way as programmable computing devices allow multifunctional capabilities via software on a hardware platform. This level of integration, decision, and controlled reconfigurability will be a significant step forward in clinical diagnostics.
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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.
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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.
