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公开(公告)号：US20220170840A1
公开(公告)日：2022-06-02
申请号：US17108362
申请日：2020-12-01
摘要： In an approach for controlling a multiphase flow configured to create a plurality of particles, a processor obtains images of a plurality of particles in a multiphase flow. A processor provides the images to a neural network adapted to determine a distribution of a spatial property of the plurality of particles from the provided images. A processor determines the distribution of the spatial property of the plurality of particles in the multiphase flow, based on the provided images, using the neural network. A processor controls the multiphase flow based on the determined distribution.
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公开(公告)号：US20240181321A1
公开(公告)日：2024-06-06
申请号：US18060599
申请日：2022-12-01
IPC分类号： A63B71/06
CPC分类号： A63B71/06 , A63B2225/50
摘要： Embodiments of the invention are directed to wireless sport scoring. Aspects include detecting, by a sensor of a first user device, a scoring event between a first user and a second user and transmitting, by a wireless transmitter of the first user device, a signal indicating the scoring event to a scoring system. Aspects also include recording, in a memory of the first user device, a record of the scoring event and transmitting, by a wired transmitter of the first user device, the record of the scoring event to the scoring system based on detecting a wired connection between the first user device and the scoring system.
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公开(公告)号：US20240005065A1
公开(公告)日：2024-01-04
申请号：US17809562
申请日：2022-06-29
发明人： LEVENTE KLEIN , Wang Zhou , Eloisa Bentivegna , Theodore G. van Kessel , Bruce Gordon ELMEGREEN , Chulin Wang
摘要： A method, computer program product and system to generate higher resolution geospatial images is provided. A processor receives time sequenced spatial data images at a first resolution. A processor determines from the plurality of spatial data images physics laws applicable to the spatial data images. A processor subdivides each of the plurality of spatial data images into a plurality of small spatial region images. A processor solves each of the physics laws in each of the small spatial region images. A processor trains a neural network to apply each of the physics laws to each small spatial region image by applying a regional physics law loss function. A processor determines the most applicable regional physics law based on the difference between the small spatial region image and the image predicted for that region by the physics law. A processor generates a second higher-resolution image than the first resolution.
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公开(公告)号：US20230418999A1
公开(公告)日：2023-12-28
申请号：US17809037
申请日：2022-06-27
CPC分类号： G06F30/27 , G06N20/20 , G06F30/28 , G06T7/73 , G06T3/40 , G06T2207/10032 , G06T2207/20081
摘要： In an approach for estimating emission source location from satellite plume data, a processor creates a dataset of plume concentration data. A processor down samples the dataset to an array at satellite resolution. A processor partitions the array into two separate datasets according to a preset proportion. A processor trains two machine learning models on at least one of the two separate datasets, wherein a first machine learning model of the two machine learning models is for identifying a presence of a plume and a second machine learning model of the two machine learning models is for identifying a source position and magnitude of the plume. A processor applies the two machine learning models to new concentration data.
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