Dynamically informed digital twins

    公开(公告)号:US11985074B1

    公开(公告)日:2024-05-14

    申请号:US18331819

    申请日:2023-06-08

    IPC分类号: H04L47/70 H04L41/16

    摘要: One example method includes adjusting overall resource usage in a digital twin network that includes a dynamically informed digital twin of a near-edge node and a dynamically informed digital twin of far-edge nodes. Operational conditions of a dynamically informed digital twin are evaluated based on contextual variables that represent operating properties of the dynamically informed digital twin. Updated information levels are received from an orchestration service of the dynamically informed digital twin. The updated information levels define an amount of resources the dynamically informed digital twin will use in the performance of primary tasks. The updated information levels are parsed. A physical entity associated with the dynamically informed digital twin is informed to adjust sampling properties or increase or decrease its activity and the dynamically informed digital twin is informed to modify information processing methods to thereby adjust the overall resource usage in the digital twin network.

    PREPROCESSING TO REDUCE ANNEALING PROCESSOR SEARCH SPACE

    公开(公告)号:US20240362295A1

    公开(公告)日:2024-10-31

    申请号:US18307963

    申请日:2023-04-27

    IPC分类号: G06F17/11

    CPC分类号: G06F17/11

    摘要: One example method includes accessing a parameter space including a set of binary inputs for an unconstrained objective function. The set of binary inputs are solved at a CPU or GPU using an algorithm that is different from the unconstrained objective function to generate a target solution. A subset of the binary inputs is selected. The unconstrained objective function is solved using the selected subset of binary inputs to generate a solution for each of the selected subset of binary inputs. A maximum possible change for each of the selected subset of binary inputs is determined. The maximum possible change defines a subspace including related binary inputs that are located around each of the selected subset of binary inputs. Those binary inputs and their corresponding related binary inputs whose solutions are greater than the target solution are removed from the parameter space to thereby generate a reduced parameter space.

    SERIES AUGMENTATION FOR DRIFT DATASET COMPOSITION

    公开(公告)号:US20240289682A1

    公开(公告)日:2024-08-29

    申请号:US18175331

    申请日:2023-02-27

    IPC分类号: G06N20/00

    CPC分类号: G06N20/00

    摘要: One example method, which may be performed by a drift upsampling pipeline, includes receiving, by a drift upsampling pipeline, input including both a time series of data expressed as an initial drift curve, and a drift characterization of the drift curve. The method further includes, performing a first upsampling stage on the time series of data to generate a first family of new drift curves based on the drift characterization of the initial drift curve, performing a second upsampling stage to determine respective frequencies of the first family of new drift curves to generate a second family of new drift curves with the respective frequencies, performing a third upsampling stage on respective noise levels of the second family of new drift curves to generate a third family of new drift curves with new respective noise levels, and outputting the third family of new drift curves.

    AUTONOMOUS MOBILE ROBOT BEHAVIORAL ANALYSIS FOR DEVIATION WARNINGS

    公开(公告)号:US20240126270A1

    公开(公告)日:2024-04-18

    申请号:US18046767

    申请日:2022-10-14

    IPC分类号: G05D1/02 G06N20/00

    CPC分类号: G05D1/0291 G06N20/00

    摘要: One example method includes receiving real-time operational data related to the operation of Autonomous Mobile Robots (AMRs) belonging to an AMR group. Clusters of expected behavior, for the AMRs, are accessed. The clusters were generated using historical operational data. Each cluster defines a possible behavioral scenario for each AMR and includes a cluster boundary that defines a limit of the expected behavior and a cluster centroid that defines an average of expected behavior of each AMR. Resultant vectors that extend from the cluster centroid to the most recent operational point of each AMR are generated. A predetermined phase threshold value is used to determine when two or more of the resultant vectors are close to each other. The close resultant vectors are grouped to generate Resultant of Resultant Vectors (RoRs). The RoRs are used to identify behavioral scenarios of the AMRs.

    ONLINE DRIFT DETECTION FOR FULLY UNSUPERVISED EVENT DETECTION IN EDGE ENVIRONMENTS

    公开(公告)号:US20240028944A1

    公开(公告)日:2024-01-25

    申请号:US17813714

    申请日:2022-07-20

    IPC分类号: G06N20/00

    CPC分类号: G06N20/00

    摘要: One example method includes receiving a stream of unlabeled data samples from a model, obtaining a first reconstruction error for the unlabeled data samples, obtaining a second reconstruction error for a set of normative data, defining a margin based on the first reconstruction error and the second reconstruction error, computing an initial proportion of samples from the set of normative data whose reconstruction errors fall within a range of reconstruction errors defined by the margin, computing a new proportion of unlabeled data samples that fall within the range of reconstruction errors defined by the margin, and signaling drift in the performance of the model when said new proportion differs from said initial proportion by more than a predefined tolerance threshold.