The terms denote engineering domains that have high CPS content.
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In a team decision problem, a team of agents interacts with a system; each agent receives
a measurement that is probabilistically related to the state of the system; each agent uses its
measurement to choose an action; and the team incurs a cost that is a function of the state of
the system and the actions of the agents; the objective is to design the rules that the agents
use to choose their actions in order to minimize the expected cost. Despite their simplicity,
team decision problems are a fundamental building block of decentralized control, and the
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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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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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Calibration, or accuracy control, of cyber-physical additive manufacturing systems relies on predictive models for geometric shape deformation. However, learning predictive models is made difficult by the wide variety of possible process conditions and shapes. In addition, resource constraints limit the manufacture of test shapes, which further impedes learning of deformation models for new shape varieties. A methodology that can make full use of data collected on different shapes and reduce the haphazard aspect of traditional learning techniques is necessary in this context.
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This project aims at accelerating the deployment of security measures for cyber-physical systems (CPSs) by proposing a framework that combines anomaly identification approaches, which emphasizes on the development of decentralized cyber-attack monitoring and diagnostic-like components, with robust control countermeasure to improve reliability and maintain system functionality. One of the main challenges for cyber physical systems is the security of transmitted data over the communication network.
