公开(公告)号：US20230204502A1

公开(公告)日：2023-06-29

申请号：US18163712

申请日：2023-02-02

Applicant: VIAVI Solutions Inc.

Inventor： ChangMeng HSIUNG ,  Lan SUN ,  Edward GOODING ,  Michael KLIMEK

IPC: G01N21/3577 ,  G01N21/359 ,  G01N33/15 ,  G01N1/38

CPC classification number: G01N21/3577 ,  G01N21/359 ,  G01N33/15 ,  G01N1/38

Abstract: A device may receive spectroscopic data associated with a dynamic process. The device may identify a pseudo steady state end point based on the spectroscopic data. The pseudo steady state end point may indicate an end of a pseudo steady state associated with the dynamic process. The device may identify a reference block and a test block based on the pseudo steady state end point, and may generate a raw detection signal associated with the reference block and a raw detection signal associated with the test block. The device may generate an averaged statistical detection signal based on the raw detection signal associated with the reference block and the raw detection signal associated with the test block, and may determine whether the dynamic process has reached a steady state based on the averaged statistical detection signal.

公开(公告)号：US20250021892A1

公开(公告)日：2025-01-16

申请号：US18352072

申请日：2023-07-13

Applicant: VIAVI Solutions Inc.

Inventor： ChangMeng HSIUNG ,  Lan SUN

IPC: G06N20/10

Abstract: In some implementations, a device may receive a spectroscopic dataset associated with an unknown sample. The device may obtain a multiclass classification model to be used for classification of the unknown sample into at least one class of a plurality of classes; wherein the multiclass classification model comprises a plurality of local auto-scaled one-versus-one (OVO) binary classifiers, each local auto-scaled OVO binary classifier of the plurality of local auto-scaled OVO binary classifiers being associated with a different pair of classes from the plurality of classes. The device may apply local auto-scaling to the spectroscopic dataset associated with the unknown sample to create a local auto-scaled spectroscopic dataset. The device may perform a classification of the unknown sample based on the local auto-scaled spectroscopic dataset and using the multiclass classification model comprising the plurality of local auto-scaled OVO binary classifiers.

公开(公告)号：US20230385383A1

公开(公告)日：2023-11-30

申请号：US18363060

申请日：2023-08-01

Applicant: VIAVI Solutions Inc.

Inventor： ChangMeng HSIUNG

IPC: G06F18/2411 ,  G06F18/2433 ,  G01N21/25 ,  G01N21/27

CPC classification number: G06F18/2411 ,  G06F18/2433 ,  G01N21/255 ,  G01N21/27 ,  G01N21/359

Abstract: A device may receive information identifying results of a spectroscopic measurement performed on an unknown sample. The device may determine a decision boundary for a quantification model based on a configurable parameter, such that a first plurality of training set samples of the quantification model is within the decision boundary and a second plurality of training set samples of the quantification model is not within the decision boundary. The device may determine a distance metric for the spectroscopic measurement performed on the unknown sample relative to the decision boundary. The device may determine a plurality of distance metrics for the second plurality of training set samples of the quantification model relative to the decision boundary. The device may provide information indicating whether the spectroscopic measurement performed on the unknown sample corresponds to the quantification model.

公开(公告)号：US20230273122A1

公开(公告)日：2023-08-31

申请号：US18312241

申请日：2023-05-04

Applicant: VIAVI Solutions Inc.

Inventor： ChangMeng HSIUNG ,  Christopher G. PEDERSON ,  Marc K. VON GUNTEN ,  Lan SUN

IPC: G06V20/69 ,  G01J3/10 ,  G01N21/25 ,  G01N21/35 ,  G01N21/359 ,  G06F18/2433 ,  G06N20/00 ,  G16C20/20 ,  G16C20/70 ,  G06F18/2411

CPC classification number: G06V20/698 ,  G01J3/108 ,  G01N21/253 ,  G01N21/35 ,  G01N21/359 ,  G06F18/2433 ,  G06N20/00 ,  G16C20/20 ,  G16C20/70 ,  G06F18/2411 ,  G01N2201/129

Abstract: A device may receive information identifying results of a set of spectroscopic measurements of a training set of known samples and a validation set of known samples. The device may generate a classification model based on the information identifying the results of the set of spectroscopic measurements, wherein the classification model includes at least one class relating to a material of interest for a spectroscopic determination, and wherein the classification model includes a no-match class relating to at least one of at least one material that is not of interest or a baseline spectroscopic measurement. The device may receive information identifying a particular result of a particular spectroscopic measurement of an unknown sample. The device may determine whether the unknown sample is included in the no-match class using the classification model. The device may provide output indicating whether the unknown sample is included in the no-match class.