To address the need for realistic and high-fidelity first responder training, a multidisciplinary team at LLNL has worked to establish the new gold standard simulator called TARANTULA (Tactical Augmented Reality Applications for Nuclear Emergency Support Team (NEST) Training using Livermore Analytics). TARANTULA is a scientifically accurate, fully functional, field-deployable simulator that…
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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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There are three main components to the RaFTS system: 1) the radiation detector, which can be of any type and from any manufacturer; 2) the RaFTS electronics, which produce the electronic pulses that are injected into the electronics of the radiation detector through a (to be) standardized port interface; and 3) the exercise scenario, which defines the synthetic radiation field and time-varying…
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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…