5th Annual NSF Cyber-Physical Systems PI Meeting.
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Abstract:
The electric power CPS faces an alarmingly high risk of catastrophic damage from cyber--attacks. However, modeling cyber--attacks, evaluating consequences, and developing appropriate countermeasures require a detailed, realistic, and tractable model of electric power CPS operations. The primary barrier is the lack of access to models for the complex legacy proprietary systems that the electric power grid has relied on for decades.
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The use of deductive techniques, such as theorem provers, has several advantages in safety verification of hybrid systems. State-of-the-art theorem provers, however, suffer from a significant lack of automation.
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The objective of this research is to develop semantic foundations, cross-layer system architectures and adaptation services to improve dependability in instrumented cyberphysical spaces (ICPS). The approach is based on the principles of "computation reflection" where information from heterogeneous sensing devices is used to create a digital representation of the evolving cyberphysical world for use by mission-critical applications such as infrastructure monitoring, and incident-site emergency response.
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The objective of this research is to develop the theory, hardware and computational infrastructure that will enable automatically transforming user-defined, high-level tasks into correct, low-level perception informed control and configurations for modular robots.
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The goal of this project is to develop a tool-chain for composition of safety-critical cyber-physical systems from a small code base of verified components and a large code base of unverified commercial off-the- shelf components. Unlike tool-chains that aim to deliver end-to-end verified component code, starting from formal languages, specifications, or models, an explicit goal of this project is to accommodate large amounts of legacy code that is typically too complex to verify.
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This project aims to develop the theory and technology for a new frontier in cyber-physical systems: cyber- physical manipulation. The ultimate goal of cyber-physical manipulation is to enable a group of hundreds or thousands of individual robotic agents to collaboratively explore an environment, manipulate objects in that environment, and transport those objects to desired locations.
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A more recent thrust of our work in this project focuses on continuous control tasks performed by a human operator. Tracking random-appearing and oscillatory signals is a human in the loop task that has been used in many areas such as piloting of vehicles, rehabilitation engineering, and neuroscience. Understanding the control strategies in a human operator for these tracking tasks is of great importance in these areas.
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Many cyber-physical systems (CPS) deployed in a number of applications ranging from airport security systems and transportation systems to health-care and manufacturing rely on a wide variety of sensors for prediction and control. In many of these systems, acquisition of information requires the deployment and activation of physical sensors, which can result in increased expense or delay.
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The objective of this research is to develop a theory of "ActionWebs", that is, networked embedded sensor-rich systems, which can be tasked to coordinate multiple decision- makers. The approach is to first identify models of ActionWebs using stochastic hybrid systems, an interlinking of continuous dynamical physical models with discrete state representations of interconnection and computation. Second, algorithms will be designed for tasking individual sensors, based on information objectives for the entire system.
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This project is concerned with ensuring operational safety of complex cyber-physical systems such as automobiles, aircraft, and medical devices. Modern development techniques for such systems rely on independent implementation of safety features in software and subsequent integration of these features within system platform architectures . The current trend in developing these systems, driven by the need to reduce cost and energy consumption, is to share computational resources between different features .
