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Livermore researchers have developed a method for implementing closed-loop control in extrusion printing processes by means of novel sensing, machine learning, and optimal control algorithms for the optimization of printing parameters and controllability. The system includes a suite of sensors, including cameras, voltage and current meters, scales, etc., that provide in-situ process monitoring…
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LLNL researchers have developed a system that relies on machine learning to monitor microfluidic devices. The system includes (at least) a microfluidic device, sensor(s), and a local network computer. The system could also include a camera that takes real-time images of channel(s) within an operating microfluidic device. A subset of these images can be used to train/teach a machine learning…
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LLNL’s Optically-based Interstory Drift Meter System provides a means to accurately measure the dynamic interstory drift of a vibrating building (or other structure) during earthquake shaking. This technology addresses many of the shortcomings associated with traditional strong motion accelerometer based building monitoring.
LLNL’s discrete diode position sensitive device is a newly…