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21.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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22.发明申请公开(公告)号：US20170236074A1
公开(公告)日：2017-08-17
申请号：US15583067
申请日：2017-05-01
Applicant: SAS Institute Inc.
Inventor： Sergiy Peredriy , Deovrat Vijay Kakde , Arin Chaudhuri
CPC classification number: G06N99/005 , G06F17/30958
Abstract: A computing device determines a kernel parameter value for a support vector data description for outlier identification. A first candidate optimal kernel parameter value is computed by computing a first optimal value of a first objective function that includes a kernel function for each of a plurality of kernel parameter values from a starting kernel parameter value to an ending kernel parameter value using an incremental kernel parameter value. The first objective function is defined for a SVDD model using observation vectors to define support vectors. A number of the observation vectors is a predefined sample size. The predefined sample size is incremented by adding a sample size increment. A next candidate optimal kernel parameter value is computed with an incremented number of vectors until a computed difference value is less than or equal to a predefined convergence value.
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公开(公告)号：US09639809B1
公开(公告)日：2017-05-02
申请号：US15390236
申请日：2016-12-23
Applicant: SAS Institute Inc.
Inventor： Deovrat Vijay Kakde , Sergiy Peredriy , Arin Chaudhuri , Anya M. McGuirk
CPC classification number: G06N99/005
Abstract: A computing device identifies outliers. Support vectors, Lagrange constants, a center threshold value, an upper control limit value, and a lower control limit value are received that define a normal operating condition of a system. The center threshold value, the upper control limit value, and the lower control limit value are computed from the vectors and the Lagrange constants. A first plurality of observation vectors is received for a predefined window length. A window threshold value and a window center vector are computed. A window distance value is computed between the window center vector and the support vectors. Based on comparisons between the computed values and the received values, the first plurality of observation vectors is identified as an outlier relative to the normal operating condition of the system. When the first plurality of observation vectors are identified as the outlier, an alert is output.
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