Method and computer program for clustering large multiplexed spatially resolved data of a biological sample

    公开(公告)号:US11989959B2

    公开(公告)日:2024-05-21

    申请号:US17257959

    申请日:2019-07-05

    摘要: The invention relates to a method for processing large multiplexed image data of a biological sample, the method comprising the steps of, recording a plurality of images of a biological sample, wherein the plurality of images comprises images having a different entity of the biological sample targeted with a predefined stain, determining spatially corresponding image pixels in the plurality of registered images, associating the spatially corresponding image pixels to a pixel profile, wherein each pixel profile comprises the pixel values of the spatially corresponding pixels and wherein the pixel profile is associated with the respective image coordinate of the spatially corresponding pixels, pooling the pixel profiles by means of a clustering method configured to determine pixel profiles with similar values, and thereby generating a plurality of clusters, each comprising pixel profiles with similar pixel values, for each cluster assigning a cluster value to the image coordinate of the pixel profiles comprised by said cluster and thereby generating a cluster image with cluster pixels.

    System and method for maintenance recommendation in industrial networks

    公开(公告)号:US11693924B2

    公开(公告)日:2023-07-04

    申请号:US16433858

    申请日:2019-06-06

    申请人: Hitachi, Ltd.

    摘要: Example implementations involve fault detection and isolation in industrial networks through defining a component as a combination of measurements and parameters and define an industrial network as a set of components connected with different degrees of connections (weights). Faults in industrial network are defined as unpermitted changes in component parameters. Further, the fault detection and isolation in industrial networks are formulated as a node classification problem in graph theory.
    Example implementations detect and isolate faults in industrial networks through 1) uploading/learning network structure, 2) detecting component communities in the network, 3) extracting features for each community, 4) using the extracted features for each community to detect and isolate faults, 5) at each time step, based on the faulty components provide maintenance recommendation for the network.