The document was issued by academy or academy organization.
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During the development process of CPS, an analysis of whether the system operates safely in its target environment is of utmost importance. For many applications of CPS research such as the transportation industry, this implies interconnected research in formal verification of CPS with research on knowledge representation and reasoning in multi-agent systems. The need for such research has become tragically clear in transportation accidents, one notorious case being the Air France 447 flight incident.
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In this project, we are working on the broad problem of maximizing the penetration of renewables in smart grid. To that end, we are developing strategies for optimal demand response, operation, and real-time pricing. In particular, we propose theory for optimal operation for the independent system operator (ISO), and for the load serving entity (LSE).
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Team decision problems are a static simplification of decentralized Markov decision problems, and form a fundamental building block of decentralized control problems. An early paper by Radner showed that, in the special case when all random variables are Gaussian and the cost function is quadratic, there exist optimal policies that are linear functions. However, Tsitsiklis and Athans proved that the general case is NP-hard.
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Despite many advances in vehicle automation, much remains to be done: the best autonomous vehicle today still lags behind human drivers, and connected vehicle (V2V) and infrastructure (V2I) standards are only just emerging. In order for such cyber-physical systems to fully realize their potential, they must be capable of exploiting one of the richest and most complex abilities of humans, which we take for granted: seeing and understanding the visual world.
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This poster presents progress of the synergistic framework and algorithms development for a group sensors to collaborate in disaster scenarios. A number of fundamental research results were obtained on scene understanding in a network of visual sensors. These were experimentally evaluated, and datasets and results from these evaluations have been released to the community.
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Many safety-critical cyber-physical systems rely on advanced sensing capabilities to react to changing environmental conditions. However, cost-effective deployments of such capabilities have remained elusive. Such deployments will require software infrastructure that enables multiple sensor-processing streams to be multiplexed onto a common hardware platform at reasonable cost, as well as tools and methods for validating that required processing rates can be maintained.
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Stochastic hybrid systems are a class of stochastic models that incorporates both continuous- and discretestate dynamics. They have wide applications in modeling various processes, such as communication networks [4] and biochemical reactions [6]. In this work, we focus on continuous-time stochastic hybrid systems (CTSHS); specifically, we propose a framework using Metric Interval Temporal Logic (MITL) [1] to describe the systems' behavior and verifying the MITL formulas via model reduction and sampling.
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Recent progress in battery technology has made it possible to use batteries to power various physical platforms, such as ground/air/water vehicles. These platforms require hundreds/thousands of battery cells to meet their power and energy needs. Of these, automobiles, locomotives, and unmanned air vehicles (UAVs) face the most stringent environmental challenges.
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This project, NSF grant 1329363, is a modular, computationally-distributed multi-robot cyber-physical system (CPS) for assisting young developmentally-delayed children learning to walk. The challenges of assisting young developmentally-delayed children learning to walk are 1) stabilizing medio-lateral body sway, 2) developing gait that exploits energy exchange, and 3) coordinating multiple degrees of freedom.
