公开(公告)号：US12189715B2

公开(公告)日：2025-01-07

申请号：US17334392

申请日：2021-05-28

Applicant: Oracle International Corporation

Inventor： Matthew T. Gerdes ,  Guang C. Wang ,  Kenny C. Gross ,  Timothy David Cline

IPC: G06F18/214 ,  G06F1/02 ,  G06N3/08

Abstract: The disclosed system produces synthetic signals for testing machine-learning systems. During operation, the system generates a set of N composite sinusoidal signals, wherein each of the N composite sinusoidal signals is a combination of multiple constituent sinusoidal signals with different periodicities. Next, the system adds time-varying random noise values to each of the N composite sinusoidal signals, wherein a standard deviation of the time-varying random noise values varies over successive time periods. The system also multiplies each of the N composite sinusoidal signals by time-varying amplitude values, wherein the time-varying amplitude values vary over successive time periods. Finally, the system adds time-varying mean values to each of the N composite sinusoidal signals, wherein the time-varying mean values vary over successive time periods. The time-varying random noise values, amplitude values and mean values can be selected through a roll-of-the-die process from a library of values, which are learned from industry-specific signals.

公开(公告)号：US12001254B2

公开(公告)日：2024-06-04

申请号：US17516975

申请日：2021-11-02

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： James Rohrkemper ,  Sanjeev R. Sondur ,  Kenny C. Gross ,  Guang C. Wang

IPC: G06F1/20 ,  H05K7/20

CPC classification number: G06F1/206 ,  H05K7/20136 ,  H05K7/20718 ,  H05K7/20836

Abstract: Systems, methods, and other embodiments associated with detecting feedback control instability in computer thermal controls are described herein. In one embodiment, a method includes for a set of dwell time intervals, wherein the dwell time intervals are associated with a range of periods of time from an initial period to a base period, executing a workload that varies from minimum to maximum over the period on a computer during the dwell time interval; recording telemetry data from the computer during execution of the workload; incrementing the period towards a base period; determining that either the base period is reached or a thermal inertia threshold is reached; and analyzing the recorded telemetry data over the set of dwell time intervals to either (i) detect presence of a feedback control instability in thermal control for the computer; or (ii) confirm feedback control stability in thermal control for the computer.

公开(公告)号：US11663369B2

公开(公告)日：2023-05-30

申请号：US17090131

申请日：2020-11-05

Applicant: Oracle International Corporation

Inventor： Matthew T. Gerdes ,  Kenny C. Gross ,  Guang C. Wang ,  Shreya Singh ,  Aleksey M. Urmanov

IPC: G06F21/00 ,  G06F21/88

CPC classification number: G06F21/88

Abstract: During operation, the system uses N sensors to sample an electromagnetic interference (EMI) signal emitted by a target asset while the target asset is running a periodic workload, wherein each of the N sensors has a sensor sampling frequency f, and wherein the N sensors perform sampling operations in a round-robin ordering with phase offsets between successive samples. During the sampling operations, the system performs phase adjustments among the N sensors to maximize phase offsets between successive sensors in the round-robin ordering. Next, the system combines samples obtained through the N sensors to produce a target EMI signal having an EMI signal sampling frequency F=f×N. The system then generates a target EMI fingerprint from the target EMI signal. Finally, the system compares the target EMI fingerprint against a reference EMI fingerprint for the target asset to determine whether the target asset contains any unwanted electronic components.

公开(公告)号：US11367018B2

公开(公告)日：2022-06-21

申请号：US16732558

申请日：2020-01-02

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

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：US20220138358A1

公开(公告)日：2022-05-05

申请号：US17090131

申请日：2020-11-05

Applicant: Oracle International Corporation

Inventor： Matthew T. Gerdes ,  Kenny C. Gross ,  Guang C. Wang ,  Shreya Singh ,  Aleksey M. Urmanov

IPC: G06F21/88

Abstract: During operation, the system uses N sensors to sample an electromagnetic interference (EMI) signal emitted by a target asset while the target asset is running a periodic workload, wherein each of the N sensors has a sensor sampling frequency f, and wherein the N sensors perform sampling operations in a round-robin ordering with phase offsets between successive samples. During the sampling operations, the system performs phase adjustments among the N sensors to maximize phase offsets between successive sensors in the round-robin ordering. Next, the system combines samples obtained through the N sensors to produce a target EMI signal having an EMI signal sampling frequency F=f×N. The system then generates a target EMI fingerprint from the target EMI signal. Finally, the system compares the target EMI fingerprint against a reference EMI fingerprint for the target asset to determine whether the target asset contains any unwanted electronic components.

公开(公告)号：US20220138316A1

公开(公告)日：2022-05-05

申请号：US17086855

申请日：2020-11-02

Applicant: Oracle International Corporation

Inventor： Zexi Chen ,  Kenny C. Gross ,  Ashin George ,  Guang C. Wang

IPC: G06F21/55 ,  G06N20/00 ,  G06N5/04 ,  G06F17/16

Abstract: The disclosed embodiments relate to a system that characterizes susceptibility of an inferential model to follow signal degradation. During operation, the system receives a set of time-series signals associated with sensors in a monitored system during normal fault-free operation. Next, the system trains the inferential model using the set of time-series signals. The system then characterizes susceptibility of the inferential model to follow signal degradation. During this process, the system adds degradation to a signal in the set of time-series signals to produce a degraded signal. Next, the system uses the inferential model to perform prognostic-surveillance operations on the set of time-series signals with the degraded signal. Finally, the system characterizes susceptibility of the inferential model to follow degradation in the signal based on results of the prognostic-surveillance operations.

公开(公告)号：US20220129457A1

公开(公告)日：2022-04-28

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: G06F16/2458 ,  G06N20/00

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：US20210406374A1

公开(公告)日：2021-12-30

申请号：US16915593

申请日：2020-06-29

Applicant: Oracle International Corporation

Inventor： Guang C. Wang ,  Kenny C. Gross

IPC: G06F21/56 ,  G06F21/55 ,  G06F17/14

Abstract: The disclosed embodiments provide a system that detects unwanted electronic components in a target asset. During operation, the system obtains target electromagnetic interference (EMI) signals by monitoring EMI signals generated by the target asset while the target asset is running a periodic workload. Next, the system generates a target EMI fingerprint from the target EMI signals. The system then applies a compression/dilation technique to time-series signals in the target EMI fingerprint to achieve alignment with corresponding time-series signals in a reference EMI fingerprint to produce a synchronized target EMI fingerprint. Finally, the system compares the synchronized target EMI fingerprint against the reference EMI fingerprint to determine whether the target asset contains any unwanted electronic components.

公开(公告)号：US10860011B2

公开(公告)日：2020-12-08

申请号：US16371694

申请日：2019-04-01

Applicant: Oracle International Corporation

Inventor： Kenny C. Gross ,  Guang C. Wang

IPC: G05B23/02 ,  G06F30/20

Abstract: During operation, the system receives time-series signals from sensors in the asset while the asset is operating. Next, the system obtains real-time environmental parameters for an environment in which the asset is operating. The system then selects an environment-specific inferential model for the asset based on the real-time environmental parameters, wherein the environment-specific inferential model was trained on a golden system while the golden system was operating in an environment that matches the real-time environmental parameters. Next, the system uses the environment-specific inferential model to generate estimated values for the received time-series signals based on correlations among the received time-series signals, and performs a pairwise-differencing operation between actual values and the estimated values for the received time-series signals to produce residuals. Finally, the system determines from the residuals whether the asset is operating correctly.

公开(公告)号：US20200372385A1

公开(公告)日：2020-11-26

申请号：US16419846

申请日：2019-05-22

Applicant: Oracle International Corporation

Inventor： Guang C. Wang ,  Kenny C. Gross

IPC: G06N7/00 ,  G06N20/00

Abstract: The disclosed embodiments provide a system that performs seasonality-compensated prognostic-surveillance operations for an asset. During operation, the system obtains time-series sensor signals gathered from sensors in the asset during operation of the asset. Next, the system identifies seasonality modes in the time-series sensor signals. The system then determines frequencies and phase angles for the identified seasonality modes. Next, the system uses the determined frequencies and phase angles to filter out the seasonality modes from the time-series sensor signals to produce seasonality-compensated time-series sensor signals. The system then applies an inferential model to the seasonality-compensated time-series sensor signals to detect incipient anomalies that arise during operation of the asset. Finally, when an incipient anomaly is detected, the system generates a notification regarding the anomaly.