Safe adaptive machine learning-based control for cold atmospheric plasma biomedical devices
AssigneeThe Regents of the University of California
InventorsAli Mesbah, Kimberly Chan, Georgios Makrygiorgos
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Abstract
A system to control a cold atmospheric plasma device, including a cold atmospheric device, one or more sensors to measure characteristics of one or more of the plasma and a surface at which the cold atmospheric plasma device is directed, and one or more processors configured to execute code to cause the one or more processors to use a deep neural network (DNN) to provide operating parameters for operation of the device and to receive information from the one or more sensors as feedback on the operation.
Claims13
A system to control a cold atmospheric plasma device, comprising:
- a cold atmospheric biomedical device;
- one or more sensors to measure characteristics of one or more of the plasma and a surface at which the cold atmospheric plasma device is pointed;
- and
- one or more processors configured to execute code to cause the one or more processors to use a deep neural network (DNN) to provide operating parameters for operation of the cold atmospheric plasma device and to receive information from the one or more sensors as feedback on the operation.
A method of controlling a cold atmospheric plasma device, comprising:
- identifying operating parameters for operation of the cold atmospheric plasma device that have the most influence on a positive result of the operation;
- providing data from each operation of the cold atmospheric plasma device to a deep neural network (DNN) to allow the deep neural network to provide optimum values for the identified operating parameters;
- operating the cold atmospheric plasma device using those parameters;
- and
- providing the data from the operating back to the deep neural network.
Dependencies are inferred from the claim text.