公开(公告)号：US11686756B2

公开(公告)日：2023-06-27

申请号：US17672928

申请日：2022-02-16

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Edward R. Wetherbee ,  Rui Zhong ,  Kenny C. Gross ,  Guang C. Wang

IPC: G01R31/00 ,  G01R29/08 ,  G06F21/44

CPC classification number: G01R31/002 ,  G01R29/0814 ,  G01R29/0878 ,  G06F21/44

Abstract: Detecting a counterfeit status of a target device by: selecting a set of frequencies that best reflect load dynamics or other information content of a reference device while undergoing a power test sequence; obtaining target electromagnetic interference (EMI) signals emitted by the target device while undergoing the same power test sequence; creating a sequence of target kiviat plots from the amplitude of the target EMI signals at each of the set of frequencies at observations over the power test sequence to form a target kiviat tube EMI fingerprint; comparing the target kiviat tube EMI fingerprint to a reference kiviat tube EMI fingerprint for the reference device undergoing the power test sequence to determine whether the target device and the reference device are of the same type; and generating a signal to indicate a counterfeit status based at least in part on the results of the comparison.

公开(公告)号：US20230121897A1

公开(公告)日：2023-04-20

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: G01N29/44 ,  G01N29/46 ,  G01N29/14 ,  G06N20/00

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：US20230008658A1

公开(公告)日：2023-01-12

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 ,  G05B13/02 ,  G05B15/02

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：US20220365820A1

公开(公告)日：2022-11-17

申请号：US17318795

申请日：2021-05-12

Applicant: Oracle International Corporation

Inventor： Wei Jiang ,  Guang C. Wang ,  Kenny C. Gross

IPC: G06F9/50 ,  G06N20/00 ,  G06F9/455

Abstract: We disclose a system that executes an inferential model in VRAM that is embedded in a set of graphics-processing units (GPUs). The system obtains execution parameters for the inferential model specifying: a number of signals, a number of training vectors, a number of observations and a desired data precision. It also obtains one or more formulae for computing memory usage for the inferential model based on the execution parameters. Next, the system uses the one or more formulae and the execution parameters to compute an estimated memory footprint for the inferential model. The system uses the estimated memory footprint to determine a required number of GPUs to execute the inferential model, and generates code for executing the inferential model in parallel while efficiently using available memory in the required number of GPUs. Finally, the system uses the generated code to execute the inferential model in the set of GPUs.

公开(公告)号：US11412387B2

公开(公告)日：2022-08-09

申请号：US17230156

申请日：2021-04-14

Applicant: Oracle International Corporation

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

IPC: G06F21/75 ,  H04L9/40 ,  H04W12/79 ,  H04W12/108

Abstract: The disclosed embodiments relate to a system that camouflages EMI fingerprints in EMI emissions from a computing system to enhance system security. During operation, the system monitors the EMI emissions from the computer system during operation of the computer system to produce corresponding EMI signals. Next, the system determines a dynamic amplitude of the EMI emissions based on the EMI signals. If the dynamic amplitude of the EMI emissions drops below a threshold value, the system executes synthetic transactions, which have interarrival times that, when superimposed on a workload of the computer system, cause the computer system to produce randomized EMI emissions.

公开(公告)号：US20210081573A1

公开(公告)日：2021-03-18

申请号：US16572439

申请日：2019-09-16

Applicant: Oracle International Corporation

Inventor： Kenny C. Gross ,  Guang C. Wang ,  Michael H.S. Dayringer ,  Andrew J. Lewis

IPC: G06F21/73 ,  G06F21/55

Abstract: The disclosed embodiments provide a system that generates a reference EMI fingerprint to be used in detecting unwanted electronic components in a target asset. During operation, the system gathers reference EMI signals generated by a reference asset while the reference asset is executing a periodic workload, wherein the reference asset is of the same type as the target asset and is certified not to contain unwanted electronic components. Next, the system divides the reference EMI signals into a set of profiles, which comprise EMI signals for non-overlapping time intervals of a fixed size. The system then temporally aligns and merges profiles in the set of profiles to produce a reference profile. Next, the system generates the reference EMI fingerprint from the reference profile. Finally, the system compares a target EMI fingerprint for the target asset against the reference EMI fingerprint to determine whether the target asset contains unwanted electronic components.

公开(公告)号：US20210011990A1

公开(公告)日：2021-01-14

申请号：US16506803

申请日：2019-07-09

Applicant: Oracle International Corporation

Inventor： Kenny C. Gross ,  Michael H. S. Dayringer ,  Andrew J. Lewis ,  Guang C. Wang

IPC: G06F21/44 ,  G06F21/76

Abstract: The disclosed embodiments provide a system that detects unwanted electronic components in a target asset. During operation, the system generates a sinusoidal load for the target asset. Next, the system obtains target electromagnetic interference (EMI) signals by monitoring EMI signals generated by the target asset while the target asset is executing the sinusoidal load. The system then generates a target EMI fingerprint from the target EMI signals. Finally, the system compares the target EMI fingerprint against a reference EMI fingerprint for the target asset to determine whether the target asset contains unwanted electronic components.

公开(公告)号：US20200245140A1

公开(公告)日：2020-07-30

申请号：US16258544

申请日：2019-01-26

Applicant: Oracle International Corporation

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

IPC: H04W12/00 ,  G06F21/75 ,  H04W12/10

Abstract: The disclosed embodiments relate to a system that camouflages electromagnetic interference (EMI) fingerprints in EMI emissions from a computing system to enhance system security. During operation, the system monitors the EMI emissions from the computer system while the computer system is operating to produce corresponding EMI signals. Next, the system performs a Fast Fourier Transform (FFT) operation on the EMI signals. The system then converts an output of the FFT operation into a frequency-domain representation of the EMI signals. Next, the system generates a camouflaging signal based on the frequency-domain representation of the EMI signals. Finally, the system outputs the camouflaging signal through a transmitter to camouflage EMI fingerprints in the EMI emissions from the computer system.

公开(公告)号：US12086693B2

公开(公告)日：2024-09-10

申请号：US16419846

申请日：2019-05-22

Applicant: Oracle International Corporation

Inventor： Guang C. Wang ,  Kenny C. Gross

IPC: G05B23/02 ,  G06F17/10 ,  G06N7/01 ,  G06N20/00 ,  H04L9/40

CPC classification number: G06N20/00 ,  G05B23/0218 ,  G05B23/0283 ,  G06F17/10 ,  G06N7/01 ,  H04L63/1425

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.

公开(公告)号：US11948051B2

公开(公告)日：2024-04-02

申请号：US16826478

申请日：2020-03-23

Applicant: ORACLE INTERNATIONAL CORPORATION

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

IPC: G06F16/00 ,  G05B23/02 ,  G06F17/16 ,  G06F17/18 ,  G06F30/27 ,  G06N20/00 ,  G06F111/10 ,  G06N3/08 ,  G06N20/10

CPC classification number: G06N20/00 ,  G05B23/024 ,  G06F17/16 ,  G06F17/18 ,  G06F30/27 ,  G06F2111/10 ,  G06N3/08 ,  G06N20/10

Abstract: In one embodiment, a method for auditing the results of a machine learning model includes: retrieving a set of state estimates for original time series data values from a database under audit; reversing the state estimation computation for each of the state estimates to produce reconstituted time series data values for each of the state estimates; retrieving the original time series data values from the database under audit; comparing the original time series data values pairwise with the reconstituted time series data values to determine whether the original time series and reconstituted time series match; and generating a signal that the database under audit (i) has not been modified where the original time series and reconstituted time series match, and (ii) has been modified where the original time series and reconstituted time series do not match.