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    EMI FINGERPRINTS: ASSET CONFIGURATION DISCOVERY FOR COUNTERFEIT DETECTION IN CRITICAL UTILITY ASSETS
    13.
    发明申请
    EMI FINGERPRINTS: ASSET CONFIGURATION DISCOVERY FOR COUNTERFEIT DETECTION IN CRITICAL UTILITY ASSETS 有权

    公开(公告)号：US20210247442A1

    公开(公告)日：2021-08-12

    申请号：US16784506

    申请日：2020-02-07

    Applicant: ORACLE INTERNATIONAL CORPORATION

    Inventor： Edward R. WETHERBEE ,  Guang C. WANG ,  Kenny C. GROSS ,  Michael DAYRINGER ,  Andrew LEWIS ,  Matthew T. GERDES

    IPC: G01R31/28 ,  G06F21/55 ,  G01R31/302 ,  G06K9/62

    Abstract: Detecting whether a target utility device that includes multiple electronic components is genuine or suspected counterfeit by: performing a test sequence of energizing and de-energizing the target device and collecting electromagnetic interference (EMI) signals emitted by the target device; generating a target EMI fingerprint from the EMI signals collected; retrieving a plurality of reference EMI fingerprints from a database library, each of which corresponds to a different configuration of electronic components of a genuine device of the same make and model as the target device; iteratively comparing the target EMI fingerprint to the retrieved reference EMI fingerprints and generating a similarity metric between each compared set; and indicating that the target device (i) is genuine where the similarity metric for any individual reference EMI fingerprint satisfies a threshold test, and is a suspect counterfeit device where no similarity metric for any individual reference EMI fingerprint satisfies the test.

    MEASURING GAIT TO DETECT IMPAIRMENT
    14.
    发明公开
    MEASURING GAIT TO DETECT IMPAIRMENT 审中-公开

    公开(公告)号：US20240206766A1

    公开(公告)日：2024-06-27

    申请号：US18085974

    申请日：2022-12-21

    Applicant: ORACLE INTERNATIONAL CORPORATION

    Inventor： Zejin DING ,  Matthew T. GERDES ,  Guang Chao WANG ,  Kenny C. GROSS ,  Andrew VAKHUTINSKY

    IPC: A61B5/11 ,  A61B5/00

    CPC classification number: A61B5/112 ,  A61B5/1126 ,  A61B5/7203 ,  A61B5/746 ,  A61B2562/0219 ,  G01P15/18

    Abstract: Systems, methods, and other embodiments associated with detecting impairment using a vibration fingerprint that characterizes gait dynamics are described. An example method includes receiving measurements of a gait of a being from a sensor. The measurements of the gait are converted into a time series of observations for each frequency bin in a set of frequency bins. A time series of residuals is generated for each range of the set by pointwise subtraction between the time series of observations and a time series of references for each range of the set. An impairment metric is generated based on the time series of residuals. The impairment metric is compared to a threshold for the impairment. In response to the impairment metric satisfying the threshold, the being is indicated to be impaired.

    EXTREMA-PRESERVED ENSEMBLE AVERAGING FOR ML ANOMALY DETECTION
    15.
    发明公开
    EXTREMA-PRESERVED ENSEMBLE AVERAGING FOR ML ANOMALY DETECTION 审中-公开

    公开(公告)号：US20240045927A1

    公开(公告)日：2024-02-08

    申请号：US17881864

    申请日：2022-08-05

    Applicant: ORACLE INTERNATIONAL CORPORATION

    Inventor： Zejin DING ,  Matthew T. GERDES ,  Kenny C. GROSS ,  Guang Chao WANG

    IPC: G06K9/00 ,  G06K9/62 ,  G01M99/00 ,  G06N20/00

    CPC classification number: G06K9/0053 ,  G06K9/6256 ,  G01M99/005 ,  G06N20/00

    Abstract: Systems, methods, and other embodiments associated with associated with preserving signal extrema for ML model training when ensemble averaging time series signals for ML anomaly detection are described. In one embodiment, a method includes identifying locations and values of extrema in a training signal; ensemble averaging the training signal to produce an averaged training signal; placing the values of the extrema into the averaged training signal at respective locations of the extrema to produce an extrema-preserved averaged training signal; placing the values of the extrema into the averaged training signal at respective locations of the extrema to produce an extrema-preserved averaged training signal; and training a machine learning model using the extrema-preserved averaged training signal to detect anomalies in a signal.

    ACOUSTIC FINGERPRINTING
    16.
    发明公开
    ACOUSTIC FINGERPRINTING 审中-公开

    公开(公告)号：US20230358872A1

    公开(公告)日：2023-11-09

    申请号：US17735245

    申请日：2022-05-03

    Applicant: ORACLE INTERNATIONAL CORPORATION

    Inventor： Matthew T. GERDES ,  Guang C. WANG ,  Timothy D. CLINE ,  Kenny C. GROSS

    IPC: G01S7/52 ,  G01S15/89

    CPC classification number: G01S7/52001 ,  G01S15/89 ,  G01S7/52004

    Abstract: Systems, methods, and other embodiments associated with acoustic fingerprint identification of devices are described. In one embodiment, a method includes generating a target acoustic fingerprint from acoustic output of a target device. A similarity metric is generated that quantifies similarity of the target acoustic fingerprint to a reference acoustic fingerprint of a reference device. The similarity metric is compared to a threshold. In response to a first comparison result of the comparing of the similarity metric to the threshold, the target device is indicated to match the reference device. In response to a second comparison result of the comparing of the similarity metric to the threshold, it is indicated that the target device does not match the reference device.

    EVICTION OF WEAKLY CORRELATED SIGNALS FROM COLLECTIONS
    17.
    发明公开
    EVICTION OF WEAKLY CORRELATED SIGNALS FROM COLLECTIONS 审中-公开

    公开(公告)号：US20230327789A1

    公开(公告)日：2023-10-12

    申请号：US17715449

    申请日：2022-04-07

    Applicant: ORACLE INTERNATIONAL CORPORATION

    Inventor： Guang C. WANG ,  Matthew T. GERDES ,  Kenny C. GROSS ,  Alan P. WOOD

    IPC: H04B17/391

    CPC classification number: H04B17/3912

    Abstract: Systems, methods, and other embodiments associated with eviction of weakly correlated signals from collections are described. In one embodiment, a mock signal that has random signal properties is generated. A mock correlation coefficient between the mock signal and a measured time series signal from a collection of measured time series signals is then generated. A discrimination value that indicates a weak signal correlation is then selected, based at least in part on the mock correlation coefficient. A first measured signal is then identified from the collection of measured time series signals that has the weak signal correlation by determining that a first correlation coefficient between the first measured signal and a second measured signal is weak based on the discrimination value. The first measured signal is then evicted from the collection of signals in response to the determination that the first measured signal has the weak signal correlation.

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