Deep Learning Networks and Sensorimotor Control
08-08-17
Professor John Doyle and colleagues are among only nineteen groups in the United States to receive National Science Foundation (NSF) funding to conduct innovative research focused on neural and cognitive systems. They aim is to integrate the capabilities of deep learning networks into a biologically inspired architecture for sensorimotor control that can be used to design more robust platforms for complex engineered systems. [NSF release]
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John Doyle
Next-Generation Distribution Infrastructure
12-17-15
Caltech’s smart grid team led by Professors John Doyle, Steven Low, and Adam Wierman along with their collaborators have been awarded $3.9 million for an Advanced Research Projects Agency - Energy (ARPA-E) Network Optimized Distributed Energy System (NODES) project entitled "Real-time Optimization and Control of Next-Generation Distribution Infrastructure." NODES is ARPA-E’s new program focused on enabling more than 50% usage of renewable power on the grid. The Caltech team will develop a comprehensive distribution network management framework that unifies real-time voltage and frequency control at the home/distributed energy resource controllers’ level with network-wide energy management at the utility/aggregator level. [Learn more]
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Adam Wierman
John Doyle
Steven Low
Variability Keeps The Body In Balance
09-22-14
By combining heart rate data from real athletes with a branch of mathematics called control theory, John Doyle, Jean-Lou Chameau Professor of Control and Dynamical Systems, Electrical Engineering, and Bioengineering and colleagues have devised a way to better understand the relationship between reduced heart rate variability (HRV) and health.
"A familiar related problem is in driving," Doyle says. "To get to a destination despite varying weather and traffic conditions, any driver—even a robotic one—will change factors such as acceleration, braking, steering, and wipers. If these factors suddenly became frozen and unchangeable while the car was still moving, it would be a nearly certain predictor that a crash was imminent. Similarly, loss of heart rate variability predicts some kind of malfunction or 'crash,' often before there are any other indications," he says. [Caltech Release] [Read the Paper]
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John Doyle