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Evolving Gaussian Processes and Kernel Observers for Learning and Control in Spatiotemporally Varying Domains: With Applications in Agriculture, Weather Monitoring, and Fluid Dynamics
Monitoring and modeling large-scale stochastic phenomena with both spatial and temporal (spatiotemporal) evolution by using a network of distributed sensors is a critical problem in many control applications (see "Summary"). Consider, for example, a team of robots that has the task of destroying herbicide- resistant weeds on a farm (see Figure 1 and "Key Control Problems in Agriculture"). This team must predict weed growth across the whole farm to make intelligent, coordinated decisions [1].
- University of Illinois at Urbana Champaign United States
- Ford Motor Company (United States) United States
Microsoft Academic Graph classification: Computer science business.industry Distributed computing Control (management) Task (project management) symbols.namesake Kernel (image processing) Agriculture symbols Key (cryptography) Fluid dynamics Robot business Gaussian process
Control and Systems Engineering, Modeling and Simulation, Electrical and Electronic Engineering
Control and Systems Engineering, Modeling and Simulation, Electrical and Electronic Engineering
Microsoft Academic Graph classification: Computer science business.industry Distributed computing Control (management) Task (project management) symbols.namesake Kernel (image processing) Agriculture symbols Key (cryptography) Fluid dynamics Robot business Gaussian process
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).4 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).4 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average Powered byBIP!