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    EXTREMA-PRESERVED ENSEMBLE AVERAGING FOR ML ANOMALY DETECTION
    1.
    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

    申请人： ORACLE INTERNATIONAL CORPORATION

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

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

    CPC分类号： G06K9/0053 ,  G06K9/6256 ,  G01M99/005 ,  G06N20/00

    摘要： 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.

    DYNAMIC LEARNING SYSTEM
    4.
    发明申请
    DYNAMIC LEARNING SYSTEM 审中-公开

    公开(公告)号：US20180357554A1

    公开(公告)日：2018-12-13

    申请号：US15914967

    申请日：2018-03-07

    申请人： Princeton University

    发明人： Elad HAZAN ,  Karan SINGH ,  Cyril ZHANG

    IPC分类号： G06N7/00 ,  G06N3/08 ,  G06K9/00 ,  G06F17/28 ,  G06F17/17

    CPC分类号： G06N7/005 ,  G06F17/17 ,  G06F17/28 ,  G06K9/0053 ,  G06K9/00664 ,  G06N3/08

    摘要： A method of performing time series prediction by improper learning comprising calculating a plurality of filters based on a symmetric matrix and generating a mapping term based on a time series input and a function. The method may include comprising iteratively: transforming the function using the calculated plurality of filters; predicting an interim output using the transformed function and the mapping term; computing an error of the interim output based on a known output; and updating the mapping term based on the computed error. The method may include generating the mapping term through iterations over a predetermined interval and performing a time series prediction using the mapping term generated over the iterations.

    METHOD AND APPARATUS FOR THE SENSOR-INDEPENDENT REPRESENTATION OF TIME-DEPENDENT PROCESSES
    8.
    发明申请
    METHOD AND APPARATUS FOR THE SENSOR-INDEPENDENT REPRESENTATION OF TIME-DEPENDENT PROCESSES 审中-公开

    公开(公告)号：US20180181543A1

    公开(公告)日：2018-06-28

    申请号：US15831694

    申请日：2017-12-05

    申请人： David Levin

    发明人： David Levin

    IPC分类号： G06F17/18

    CPC分类号： G06F17/18 ,  G06K9/0053

    摘要： This disclosure shows how a time series of measurements of an evolving system can be processed to create an “inner” time series that is unaffected by any instantaneous invertible, possibly nonlinear transformation of the measurements. An inner time series contains information that does not depend on the nature of the sensors, which the observer chose to monitor the system. Instead, it encodes information that is intrinsic to the evolution of the observed system. Because of its sensor-independence, an inner time series may produce fewer false negatives when it is used to detect events in the presence of sensor drift. Furthermore, if the observed physical system is comprised of non-interacting subsystems, its inner time series is separable; i.e., it consists of a collection of time series, each one being the inner time series of an isolated subsystem. Because of this property, an inner time series can be used to detect a specific behavior of one of the independent subsystems without using blind source separation to disentangle that subsystem from the others. The method is illustrated by applying it to: 1) an analytic example; 2) the audio waveform of one speaker; 3) mixtures of audio waveforms of two speakers.