The imitation of the operation of a real-world process or system over time.
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The goal of this project is to develop fundamental theory, computationally efficient algorithms, and real-world experiments for the analysis and design of safety-critical cyber-physical transportation systems with human operators. To this end, we propose a modeling, theoretical, and experimental collaborative effort combining human factors, control theory, and computer science. As crashes at traffic intersections account for about 40% of overall vehicle crashes, we will focus on intersection crashes in this project.
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In order to improve the current capabilities of automotive active safety control systems (ASCS) one needs to take into account the interactions between driver/vehicle/ASCS/environment. To achieve this goal, this research will infer longterm and short-term driver behavior via the use of Bayesian networks and neuromorphic algorithms to estimate the driver's skills and current state of attention from eye movement data, together with dynamic motion cues obtained from steering and pedal inputs.
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This project will design next-generation defense mechanisms to protect critical infrastructures, such as power grids, large industrial plants, and water distribution systems. These critical infrastructures are complex primarily due to the integration of cyber and physical components, the presence of high-order behaviors and functions, and an intricate and large interconnection pattern.
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Automotive applications require an exceptionally high level of confidence. For instance, the faulty ignition switches in General Motors vehicles have resulted in 52 crashes and at least 2.6 million vehicles with these switches have been recalled. If each vehicle spent only 1,000 hours on the road, there has been less than 1 crash every 5 x 107 hours of operation. Test tracks and simulations are unable to reliably detect failures that occur this infrequently.
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In many important situations, analytically predicting the behavior of physical systems is not possible. For example, the three dimensional nature of physical systems makes it provably impossible to express closed-form analytical solutions for even the simplest systems. This has made experimentation the primary modality for designing new cyber-physical systems (CPS).
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The objective of this research is the development of a framework for assessing the reliability and safety of robotic surgery systems during development, field testing, and general deployment. The framework uses accurate simulations to assess pre-clinical reliability before deployment. After deployment, the framework uses data collection through online monitoring of the system as it is being used in the field, followed by analysis to obtain assessments of operational reliability and safety.
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Overview. The fundamental challenge in cyber-physical systems is the confluence of distinct scientific and engineering models, methods, and tools for cyber and physical systems. Cyber systems are primarily about processing information, formally modeled as patterns of bits. Physical systems are primarily about structure and dynamics, the evolution of the state of the system in time. There are certainly connections between these models, methods, and tools.
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This project develops an integrated design and simulation environment for the creation of miniature capsule robots (MCRs). An MCR is a biocompatible Cyber-Physical System (CPS) designed to operate in the human body to accomplish diagnostic or therapeutic tasks (e.g., colonoscopy, abdominal surgery, etc.). A typical MCR has to fulfill three main constraints: safety, low power operation and small size. Advances in miniaturization of electronic devices have made MCRs a reality.
