A thyristor will stay conducting until the current through the device is zero (“current zero”) or perhaps slightly negative. LLNL’s approach is to use the opticondistor (“OTV”) to force this current zero in order to force the device into an “off” state. By combining a light-activated thyristor with an OTV, a noise-immune, high efficiency, high-power switching device can be constructed. The…
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![LLNL energy grid protection device](/sites/default/files/styles/scale_exact_400x400_/public/2023-10/LLNL%20energy%20grid%20protection%20device_0.png?itok=vpqzRY_R)
The approach is to leverage the fact that a momentary “load” equal to the power transmission line impedance, (Z0), during the transient can suppress its propagation. Z(0) is typically a fixed impedance of several hundred ohms based on the geometry of most single wire transmission lines.
So, an isolated self-powered opticondistor (OTV) system may provide an ultrafast method of…
![AI Innovation Incubator](/sites/default/files/styles/scale_exact_400x400_/public/2022-01/AI%20Innovation%20Incubator.jpg?itok=B8jcKPOy)
Lawrence Livermore National Laboratory (LLNL) is offering the opportunity to collaborate in accelerating artificial intelligence (AI) for applied science, including research in key areas such as advanced material design, 3D printing, predictive biology, energy systems, “self-driving” lasers and fusion energy research.
![Catalyst HPC cluster](/sites/default/files/styles/scale_exact_400x400_/public/2022-06/Catalyst%20HPC%20cluster.jpg?itok=k9uYS5xL)
Clinical images have a wealth of data that are currently untapped by physicians and machine learning (ML) methods alike. Most ML methods require more data than is available to sufficiently train them. In order to obtain all data contained in a clinical image, it is imperative to be able to utilize multimodal, or various types of, data such as tags or identifications, especially where spatial…
![medical_x-rays_x-ray_tech](/sites/default/files/styles/scale_exact_400x400_/public/2022-06/medical_x-rays_x-ray_tech_1.jpg?itok=kn0J-DkH)
Some COVID-19 diagnoses are utilizing computed tomography (CT)-scans for triage. CT-scans produce immediate results with high sensitivity. The digital images produced by a CT-scan require physicians to identify objects within the image to determine the presence of disease. Object identification can be done using machine learning (ML) techniques such as deep learning (DL) to improve speed and…
![MimicGAN data set example](/sites/default/files/styles/scale_exact_400x400_/public/2020-05/mimicgan_robustness_to_rotation.png?itok=yacM18ra)
MimicGAN represents a new generation of methods that can “self-correct” for unseen corruptions in the data out in the field. This is particularly useful for systems that need to be deployed autonomously without needing constant intervention such as Automated Driver Assistance Systems. MimicGAN achieves this by treating every test sample as “corrupt” by default. The goal is to determine (a) the…
![mesoscale_nand_gate.jpg mesoscale_nand_gate](/sites/default/files/styles/scale_exact_400x400_/public/2019-08/mesoscale_nand_gate.jpg?itok=0APzWSXN)
Recent advancements in additive manufacturing, also called 3D printing, allow precise placement of materials in three dimensions. LLNL researchers have invented mechanical logic gates based on flexures that can be integrated into the microstructure of a micro-architected material through 3D printing. The logic gates can be combined into circuits allowing complex logic operations to be…
![Sequoia computer panels off](/sites/default/files/styles/scale_exact_400x400_/public/2022-06/Sequoia%20HPC.jpg?itok=sHb2NE1F)
LLNL has developed a new active memory data reorganization engine. In the simplest case, data can be reorganized within the memory system to present a new view of the data. The new view may be a subset or a rearrangement of the original data. As an example, an array of structures might be more efficiently accessed by a CPU as a structure of arrays. Active memory can assemble an alternative…
![medical_x-rays_x-ray_tech](/sites/default/files/styles/scale_exact_400x400_/public/2022-06/medical_x-rays_x-ray_tech_1.jpg?itok=kn0J-DkH)
LLNL has developed a new system, called the Segmentation Ensembles System, that provides a simple and general way to fuse high-level and low-level information and leads to a substantial increase in overall performance of digital image analysis. LLNL researchers have demonstrated the effectiveness of the approach on applications ranging from automatic threat detection for airport security, to…