公开(公告)号：US11822036B2

公开(公告)日：2023-11-21

申请号：US17495880

申请日：2021-10-07

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： James Rohrkemper ,  Yifan Wu ,  Guang C. Wang ,  Kenny C. Gross

IPC: G01V3/10

CPC classification number: G01V3/10

Abstract: Embodiments for passive spychip detection through polarizability and advanced pattern recognition are described. For example a method includes inducing a magnetic field in a passive component of a target system while the target system is emitting EMI with changes in amplitude repeating at a time interval; generating a time series of measurements of a combined magnetic field strength of the induced magnetic field and the EMI; executing a frequency-domain to time-domain transformation on the time series of measurements to create time series signals of combined magnetic field strength over time at a specific frequency range; monitoring the time series signals with an ML model trained to predict correct signal values to determine whether predicted and measured values of the time series agree; and indicating that the target device may contain a passive spychip where anomalies are detected, and is free of passive spychips where no anomalies are detected.

公开(公告)号：US11782429B2

公开(公告)日：2023-10-10

申请号：US17368840

申请日：2021-07-07

Applicant: Oracle International Corporation

Inventor： Richard P. Sonderegger ,  Kenneth P. Baclawski ,  Guang C. Wang ,  Anna Chystiakova ,  Dieter Gawlick ,  Zhen Hua Liu ,  Kenny C. Gross

IPC: G05B23/02 ,  G06N20/00 ,  G05B15/02 ,  G05B13/02

CPC classification number: G05B23/0221 ,  G05B13/0265 ,  G05B15/02 ,  G06N20/00 ,  G05B2223/02

Abstract: The disclosed embodiments relate to a system that automatically adapts a prognostic-surveillance system to account for aging phenomena in a monitored system. During operation, the prognostic-surveillance system is operated in a surveillance mode, wherein a trained inferential model is used to analyze time-series signals from the monitored system to detect incipient anomalies. During the surveillance mode, the system periodically calculates a reward/cost metric associated with updating the trained inferential model. When the reward/cost metric exceeds a threshold, the system swaps the trained inferential model with an updated inferential model, which is trained to account for aging phenomena in the monitored system.

公开(公告)号：US11775873B2

公开(公告)日：2023-10-03

申请号：US16005495

申请日：2018-06-11

Applicant: Oracle International Corporation

Inventor： Guang C. Wang ,  Kenny C. Gross ,  Dieter Gawlick

IPC: G06N20/10 ,  G06N5/04 ,  G06N20/00 ,  G06F17/18 ,  G06F17/17 ,  G06F18/214

CPC classification number: G06N20/10 ,  G06F17/17 ,  G06F17/18 ,  G06F18/214 ,  G06N5/04 ,  G06N20/00

Abstract: First, the system obtains time-series sensor data. Next, the system identifies missing values in the time-series sensor data, and fills in the missing values through interpolation. The system then divides the time-series sensor data into a training set and an estimation set. Next, the system trains an inferential model on the training set, and uses the inferential model to replace interpolated values in the estimation set with inferential estimates. If there exist interpolated values in the training set, the system switches the training and estimation sets. The system trains a new inferential model on the new training set, and uses the new inferential model to replace interpolated values in the new estimation set with inferential estimates. The system then switches back the training and estimation sets. Finally, the system combines the training and estimation sets to produce preprocessed time-series sensor data, wherein missing values are filled in with imputed values.

公开(公告)号：US11740122B2

公开(公告)日：2023-08-29

申请号：US17506200

申请日：2021-10-20

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Yixiu Liu ,  Matthew T. Gerdes ,  Guang C. Wang ,  Kenny C. Gross ,  Hariharan Balasubramanian

IPC: G01H1/00 ,  G06N20/00 ,  G01H17/00 ,  G01M15/12

CPC classification number: G01H1/003 ,  G01H17/00 ,  G06N20/00 ,  G01M15/12

Abstract: Systems, methods, and other embodiments associated with autonomous discrimination of operation vibration signals are described herein. In one embodiment, a method includes partitioning a frequency spectrum of output into a plurality of discrete bins, wherein the output is collected from vibration sensors monitoring a reference device; generating a representative time series signal for each bin while the device is operated in a deterministic stress load; generating a PSD for each bin by converting each signal from the time domain to the frequency domain; determining a maximum power spectral density value and a peak frequency value for each bin; selecting a subset of the bins that have maximum PSD values exceeding a threshold; assigning the representative time series signals from the selected subset of bins as operation vibration signals indicative of operational load on the reference device; and configuring a machine learning model based on at least the operation vibration signals.

公开(公告)号：US11720823B2

公开(公告)日：2023-08-08

申请号：US17825189

申请日：2022-05-26

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Edward R. Wetherbee ,  Kenny C. Gross ,  Guang C. Wang ,  Matthew T. Gerdes

IPC: G06F15/177 ,  G06N20/00 ,  H04L41/08 ,  H04L41/16 ,  H04L67/10 ,  H04L41/22 ,  H04L67/12 ,  G06N20/10

CPC classification number: G06N20/00 ,  G06N20/10 ,  H04L41/0883 ,  H04L41/16 ,  H04L41/22 ,  H04L67/10 ,  H04L67/12

Abstract: Systems, methods, and other embodiments associated with autonomous cloud-node scoping for big-data machine learning use cases are described. In some example embodiments, an automated scoping tool, method, and system are presented that, for each of multiple combinations of parameter values, (i) set a combination of parameter values describing a usage scenario, (ii) execute a machine learning application according to the combination of parameter values on a target cloud environment, and (iii) measure the computational cost for the execution of the machine learning application. A recommendation regarding configuration of central processing unit(s), graphics processing unit(s), and memory for the target cloud environment to execute the machine learning application is generated based on the measured computational costs.

公开(公告)号：US11586522B2

公开(公告)日：2023-02-21

申请号：US16801590

申请日：2020-02-26

Applicant: Oracle International Corporation

Inventor： Kenny C. Gross ,  Sanjeev Raghavendrachar Sondur ,  Guang Chao Wang

IPC: G06F11/34 ,  G06N20/00 ,  G05B13/02 ,  G05B13/04 ,  G06F1/20

Abstract: A model-based approach to determining an optimal configuration for a data center may use an environmental chamber to characterize the performance of various data center configurations at different combinations of temperature and altitude. Telemetry data may be recorded from different configurations as they execute a stress workload at each temperature/altitude combination, and the telemetry data may be used to train a corresponding library of models. When a new data center is being configured, the temperature/altitude of the new data center may be used to select a pre-trained model from a similar temperature/altitude. Performance of the current configuration can be compared to the performance of the model, and if the model performs better, a new configuration based on the model may be used as an optimal configuration for the data center.

公开(公告)号：US20230035541A1

公开(公告)日：2023-02-02

申请号：US17386965

申请日：2021-07-28

Applicant: Oracle International Corporation

Inventor： Menglin Liu ,  Richard P. Sonderegger ,  Kenneth P. Baclawski ,  Dieter Gawlick ,  Anna Chystiakova ,  Guang C. Wang ,  Zhen Hua Liu ,  Hariharan Balasubramanian ,  Kenny C. Gross

IPC: G06N7/00 ,  G06N5/04 ,  G06K9/62 ,  G06K9/00

Abstract: The disclosed embodiments relate to a system that optimizes a prognostic-surveillance system to achieve a user-selectable functional objective. During operation, the system allows a user to select a functional objective to be optimized from a set of functional objectives for the prognostic-surveillance system. Next, the system optimizes the selected functional objective by performing Monte Carlo simulations, which vary operational parameters for the prognostic-surveillance system while the prognostic-surveillance system operates on synthesized signals, to determine optimal values for the operational parameters that optimize the selected functional objective.

公开(公告)号：US11556555B2

公开(公告)日：2023-01-17

申请号：US17081859

申请日：2020-10-27

Applicant: Oracle International Corporation

Inventor： Kenny C. Gross ,  Aakash K. Chotrani ,  Beiwen Guo ,  Guang C. Wang ,  Alan P. Wood ,  Matthew T. Gerdes

IPC: G06F11/00 ,  G06F16/2458 ,  G06N20/00 ,  G06F11/30

Abstract: The disclosed embodiments relate to a system that automatically selects a prognostic-surveillance technique to analyze a set of time-series signals. During operation, the system receives the set of time-series signals obtained from sensors in a monitored system. Next, the system determines whether the set of time-series signals is univariate or multivariate. When the set of time-series signals is multivariate, the system determines if there exist cross-correlations among signals in the set of time-series signals. If so, the system performs subsequent prognostic-surveillance operations by analyzing the cross-correlations. Otherwise, if the set of time-series signals is univariate, the system performs subsequent prognostic-surveillance operations by analyzing serial correlations for the univariate time-series signal.

公开(公告)号：US20220391754A1

公开(公告)日：2022-12-08

申请号：US17370388

申请日：2021-07-08

Applicant: Oracle International Corporation

Inventor： Beiwen Guo ,  Matthew T. Gerdes ,  Guang C. Wang ,  Hariharan Balasubramanian ,  Kenny C. Gross

IPC: G06N20/00 ,  G06N5/04

Abstract: The disclosed embodiments relate to a system that produces anomaly-free training data to facilitate ML-based prognostic surveillance operations. During operation, the system receives a dataset comprising time-series signals obtained from a monitored system during normal, but not necessarily fault-free operation of the monitored system. Next, the system divides the dataset into subsets. The system then identifies subsets that contain anomalies by training one or more inferential models using combinations of the subsets, and using the one or more trained inferential models to detect anomalies in other target subsets of the dataset. Finally, the system removes any identified subsets from the dataset to produce anomaly-free training data.

公开(公告)号：US11500411B2

公开(公告)日：2022-11-15

申请号：US16052638

申请日：2018-08-02

Applicant: Oracle International Corporation

Inventor： Kenny C. Gross ,  Guang C. Wang ,  Steven T. Jeffreys ,  Alan Paul Wood ,  Coleen L. MacMillan

IPC: G06F1/02 ,  G06F17/18 ,  G06K9/00 ,  G06F16/2458 ,  G06F1/03

Abstract: The disclosed embodiments relate to a system that compactly stores time-series sensor signals. During operation, the system receives original time-series signals comprising sequences of observations obtained from sensors in a monitored system. Next, the system formulizes the original time-series sensor signals to produce a set of equations, which can be used to generate synthetic time-series signals having the same correlation structure and the same stochastic properties as the original time-series signals. Finally, the system stores the formulized time-series sensor signals in place of the original time-series sensor signals.