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公开(公告)号：US20190303786A1
公开(公告)日：2019-10-03
申请号：US16445593
申请日：2019-06-19
Applicant: SAS Institute Inc.
Inventor： Xin Jiang Hunt , Saba Emrani , Jorge Manuel Gomes da Silva , Ilknur Kaynar Kabul
Abstract: A computing device computes a weight matrix to compute a predicted value. For each of a plurality of related tasks, an augmented observation matrix, a plug-in autocovariance matrix, and a plug-in covariance vector are computed. A weight matrix used to predict the characteristic for each of a plurality of variables and each of a plurality of related tasks is computed. (a) and (b) are repeated with the computed updated weight matrix as the computed weight matrix until a convergence criterion is satisfied: (a) a gradient descent matrix is computed using the computed plug-in autocovariance matrix, the computed plug-in covariance vector, the computed weight matrix, and a predefined relationship matrix, wherein the predefined relationship matrix defines a relationship between the plurality of related tasks, and (b) an updated weight matrix is computed using the computed gradient descent matrix.
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公开(公告)号：US10402741B2
公开(公告)日：2019-09-03
申请号：US15833641
申请日：2017-12-06
Applicant: SAS Institute Inc.
Inventor： Xin Jiang Hunt , Saba Emrani , Jorge Manuel Gomes da Silva , Ilknur Kaynar Kabul
Abstract: A computing device computes a weight matrix to predict a value for a characteristic in a scoring dataset. For each of a plurality of related tasks, an augmented observation matrix, a plug-in autocovariance matrix, and a plug-in covariance vector are computed. A weight matrix used to predict the characteristic for each of a plurality of variables and each of a plurality of related tasks is computed. (a) and (b) are repeated with the computed updated weight matrix as the computed weight matrix until a convergence criterion is satisfied: (a) a gradient descent matrix is computed using the computed plug-in autocovariance matrix, the computed plug-in covariance vector, the computed weight matrix, and a predefined relationship matrix, wherein the predefined relationship matrix defines a relationship between the plurality of related tasks, and (b) an updated weight matrix is computed using the computed gradient descent matrix.
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公开(公告)号：US10157319B2
公开(公告)日：2018-12-18
申请号：US15894002
申请日：2018-02-12
Applicant: SAS Institute Inc.
Inventor： Wei Xiao , Jorge Manuel Gomes da Silva , Saba Emrani , Arin Chaudhuri
Abstract: A computing device detects an abnormal observation vector using a principal components decomposition. The principal components decomposition includes a sparse noise vector st computed for the observation vector that includes a plurality of values, wherein each value is associated with a variable to define a plurality of variables. The sparse noise vector st has a dimension equal to m a number of the plurality of variables. A zero counter time series value ĉt is computed using ĉt=Σi=1mst[i]. A probability value for ĉt is computed using p=Σi=ĉt+1m+1Hc[i]/Σi=0m+1Hc[i], where Hc[i] includes a count of a number of times each value of ĉt occurred for previous observation vectors. The probability value is compared with a predefined abnormal observation probability value. An abnormal observation indicator is set when the probability value indicates the observation vector is abnormal. The observation vector is output when the probability value indicates the observation vector is abnormal.
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Patent Agency Ranking
公开(公告)号：US10303954B2
公开(公告)日：2019-05-28
申请号：US15893959
申请日：2018-02-12
Applicant: SAS Institute Inc.
Inventor： Wei Xiao , Jorge Manuel Gomes da Silva , Saba Emrani , Arin Chaudhuri
Abstract: A computing device updates an estimate of one or more principal components for a next observation vector. An initial observation matrix is defined with first observation vectors. A number of the first observation vectors is a predefined window length. Each observation vector of the first observation vectors includes a plurality of values. A principal components decomposition is computed using the initial observation matrix. The principal components decomposition includes a sparse noise vector s, a first singular value decomposition vector U, and a second singular value decomposition vector v for each observation vector of the first observation vectors. A rank r is determined based on the principal components decomposition. A next principal components decomposition is computed for a next observation vector using the determined rank r. The next principal components decomposition is output for the next observation vector and monitored to determine a status of a physical object.
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5.发明申请公开(公告)号：US20180239740A1
公开(公告)日：2018-08-23
申请号：US15894002
申请日：2018-02-12
Applicant: SAS Institute Inc.
Inventor： Wei Xiao , Jorge Manuel Gomes da Silva , Saba Emrani , Arin Chaudhuri
CPC classification number: G06K9/00771 , G06F9/30036 , G06F17/16 , G06F17/18 , G06K9/481 , G06K9/623 , G06K9/6232 , G06K9/6247 , G06K9/6249 , G06K2009/3291
Abstract: A computing device detects an abnormal observation vector using a principal components decomposition. The principal components decomposition includes a sparse noise vector st computed for the observation vector that includes a plurality of values, wherein each value is associated with a variable to define a plurality of variables. The sparse noise vector st has a dimension equal to m a number of the plurality of variables. A zero counter time series value ĉt is computed using ĉt=Σi=1mst[i]. A probability value for ĉt is computed using p=Σi=ĉt+1m+1Hc[i]/Σi=0m+1Hc[i], where Hc[i] includes a count of a number of times each value of ĉt occurred for previous observation vectors. The probability value is compared with a predefined abnormal observation probability value. An abnormal observation indicator is set when the probability value indicates the observation vector is abnormal. The observation vector is output when the probability value indicates the observation vector is abnormal.
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公开(公告)号：US10474959B2
公开(公告)日：2019-11-12
申请号：US16445593
申请日：2019-06-19
Applicant: SAS Institute Inc.
Inventor： Xin Jiang Hunt , Saba Emrani , Jorge Manuel Gomes da Silva , Ilknur Kaynar Kabul
Abstract: A computing device computes a weight matrix to compute a predicted value. For each of a plurality of related tasks, an augmented observation matrix, a plug-in autocovariance matrix, and a plug-in covariance vector are computed. A weight matrix used to predict the characteristic for each of a plurality of variables and each of a plurality of related tasks is computed. (a) and (b) are repeated with the computed updated weight matrix as the computed weight matrix until a convergence criterion is satisfied: (a) a gradient descent matrix is computed using the computed plug-in autocovariance matrix, the computed plug-in covariance vector, the computed weight matrix, and a predefined relationship matrix, wherein the predefined relationship matrix defines a relationship between the plurality of related tasks, and (b) an updated weight matrix is computed using the computed gradient descent matrix.
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公开(公告)号：US20180336484A1
公开(公告)日：2018-11-22
申请号：US15833641
申请日：2017-12-06
Applicant: SAS Institute Inc.
Inventor： Xin Jiang Hunt , Saba Emrani , Jorge Manuel Gomes da Silva , Ilknur Kaynar Kabul
CPC classification number: G06N7/005 , G06F17/16 , G06F17/18 , G06N5/003 , G06N20/00 , G16H50/20 , G16H50/70
Abstract: A computing device computes a weight matrix to predict a value for a characteristic in a scoring dataset. For each of a plurality of related tasks, an augmented observation matrix, a plug-in autocovariance matrix, and a plug-in covariance vector are computed. A weight matrix used to predict the characteristic for each of a plurality of variables and each of a plurality of related tasks is computed. (a) and (b) are repeated with the computed updated weight matrix as the computed weight matrix until a convergence criterion is satisfied: (a) a gradient descent matrix is computed using the computed plug-in autocovariance matrix, the computed plug-in covariance vector, the computed weight matrix, and a predefined relationship matrix, wherein the predefined relationship matrix defines a relationship between the plurality of related tasks, and (b) an updated weight matrix is computed using the computed gradient descent matrix.
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8.发明申请公开(公告)号：US20180239966A1
公开(公告)日：2018-08-23
申请号：US15893959
申请日：2018-02-12
Applicant: SAS Institute Inc.
Inventor： Wei Xiao , Jorge Manuel Gomes da Silva , Saba Emrani , Arin Chaudhuri
CPC classification number: G06K9/00771 , G06F9/30036 , G06F17/16 , G06F17/18 , G06K9/481 , G06K9/623 , G06K9/6232 , G06K9/6247 , G06K9/6249 , G06K2009/3291
Abstract: A computing device updates an estimate of one or more principal components for a next observation vector. An initial observation matrix is defined with first observation vectors. A number of the first observation vectors is a predefined window length. Each observation vector of the first observation vectors includes a plurality of values. A principal components decomposition is computed using the initial observation matrix. The principal components decomposition includes a sparse noise vector s, a first singular value decomposition vector U, and a second singular value decomposition vector ν for each observation vector of the first observation vectors. A rank r is determined based on the principal components decomposition. A next principal components decomposition is computed for a next observation vector using the determined rank r. The next principal components decomposition is output for the next observation vector and monitored to determine a status of a physical object.
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