A technical operation or procedure that consists of determination of one or more characteristics of a given product, process or service according to a specified procedure.
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Abstract:
Cyber-physical systems (CPS) have become increasingly prevalent in applications 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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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 addresses highly dynamic Cyber-Physical Systems (CPSs) understood as systems where a computing delay of a few milliseconds or an incorrectly computed response to a disturbance can lead to catastrophic consequences. Such is the case of advanced safety systems on passenger cars, unmanned air vehicles performing critical maneuvers such as landing, or disaster and rescue response bipedal robots rushing through the rubble to collect information or save human lives.
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This project develops a theoretical framework as well as software tools to support testing and verification of a Cyber-Physical System (CPS) within a Model-Based Design (MBD) process. The theoretical bases of the framework are stochastic optimization methods, and robustness notions of formal specification languages.
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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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Success of numerous long-term robotic network missions in space, air, ground, and water is measured by the ability of the robots to operate for extended time in highly dynamic and potentially hazardous operating environments. The proposed work responds to the urgency for development of innovative mobile power distribution systems that lower deployment and operating costs, while simultaneously increasing mission efficiency, and supporting the network's need to be responsive to changing physical conditions.
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Modern distributed cyber-physical systems (CPSs) encounter a large variety of physical faults and cyber anomalies and in many cases, they are vulnerable to catastrophic fault propagation scenarios due to strong connectivity among the sub-systems.
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The project seeks to develop cyber-enabled exercise machines (CEEMs), which will be characterized by: i. intrinsic safety, ii. an extended use of sensing and estimation of biomechanical data, iii. real-time adaptation and guidance to the user to achieve optimal exercise. The project will develop optimality criteria on the basis of collective activation of target muscle groups.
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This project addresses urgent challenges in high confidence validation and verification of automotive vehicles due to on-going and anticipated introduction of advanced, connected and autonomous vehicles into mass production. Since such vehicles operate across both physical and cyber domains, faults can occur in traditional physical components, in cyber components (i.e., algorithms, processors, networks, etc.), or in both.
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Submitted by Anonymous on Fri, 01/29/2016 - 11:13am
