DATA DE-IDENTIFICATION METHOD AND APPARATUS

    公开(公告)号:US20210192296A1

    公开(公告)日:2021-06-24

    申请号:US17131039

    申请日:2020-12-22

    Abstract: A data de-identification method and an apparatus performing the data de-identification method are disclosed. The data de-identification method includes receiving identification data including a plurality of input feature vectors and generating a graph neural network (GNN) model including a plurality of nodes each having a value corresponding to each of the input feature vectors, determining a de-identification vector to which a correlation between the nodes is applied from the input feature vectors through the GNN model, and extracting an output feature vector by grouping values in each of the input feature vectors using the GNN model.

    SOFTWARE ROUTER AND METHODS FOR LOOKING UP ROUTING TABLE AND FOR UPDATING ROUTING ENTRY OF THE SOFTWARE ROUTER
    2.
    发明申请
    SOFTWARE ROUTER AND METHODS FOR LOOKING UP ROUTING TABLE AND FOR UPDATING ROUTING ENTRY OF THE SOFTWARE ROUTER 审中-公开
    软件路由器和查看路由表和更新软件路由器路由入口的方法

    公开(公告)号:US20170012874A1

    公开(公告)日:2017-01-12

    申请号:US15017016

    申请日:2016-02-05

    CPC classification number: H04L45/021 H04L45/56 H04L45/7453

    Abstract: A software router includes a main memory configured to comprise a hash table consisting of one or more buckets (hereinafter, referred to as “buckets A”) wherein each bucket stores destination information to which a unique key is mapped; and a central processing unit (CPU) configured to comprise a temporary table that stores the destination information present in the hash table, to determine a location of a bucket (hereinafter, referred to as “bucket B”) in the temporary table by applying a specific key to a designated hash function, wherein the specific key is extracted from a received packet, and to transmit the received packet by obtaining destination information from bucket B at the determined location in the temporary table.

    Abstract translation: 软件路由器包括被配置为包括由一个或多个桶(以下称为“桶A”)组成的散列表的主存储器,其中每个桶存储映射了唯一密钥的目的地信息; 以及中央处理单元(CPU),被配置为包括存储在哈希表中的目的地信息的临时表,用于通过应用一个或多个临时表来确定一个桶的位置(以下称为“桶B”) 指定散列函数的特定密钥,其中从接收到的分组中提取特定密钥,并且通过从临时表中的确定位置处的来自桶B的获取目的地信息来发送所接收的分组。

    IMAGE STITCHING METHOD USING IMAGE MASKING
    4.
    发明公开

    公开(公告)号:US20240062501A1

    公开(公告)日:2024-02-22

    申请号:US18339444

    申请日:2023-06-22

    CPC classification number: G06V10/16 G06V10/44 G06V10/82

    Abstract: Provided are a method and apparatus capable of improving the feature extraction required for image stitching, the computational efficiency of homography calculation based on the feature extraction, and the performance of image stitching. The image stitching method according to the present invention includes: based on two images and additional information as needed, calculating an overlapping area in which two images overlap each other; masking an area in which the two images do not overlap each other based on information about the calculated overlapping area to provide masked images; extracting significant features to be used for image stitching from the masked images; calculating a homography to be used for image transformation based on the extracted features; and transforming and stitching the images based on the calculated homography.

    METHOD OF MANAGING SYSTEM HEALTH
    5.
    发明公开

    公开(公告)号:US20230161653A1

    公开(公告)日:2023-05-25

    申请号:US17965537

    申请日:2022-10-13

    CPC classification number: G06F11/008 G06N3/08 G06N3/04 G06K9/6234

    Abstract: A method of managing system health is provided. The method includes calculating a reconstruction missing value with respect to the second domain data, determining a degree of degradation of the system on the basis of the reconstruction missing value, predicting a second remaining useful life (RUL) prediction value {tilde over (y)} of the system on the basis of the second domain data, based on a result of the determination of the degree of degradation, optimizing a degradation compensation function on the basis of a distribution of a first RUL prediction value y of the system predicted based on the first domain data in a pre-learning process of the diagnosis model, and predicting a final RUL prediction value {tilde over (y)}′ obtained by compensating for the second RUL prediction value {tilde over (y)}, by using the optimized degradation compensation function.

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