Integrated method for chaotic time series analysis
First Claim
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1. A method for automatically discriminating between similar but different states in a nonlinear process comprising the steps of:
- (A) Operating a data provision means selected from the group consisting of data storage means and data acquisition means to provide at least one channel of nonlinear data, called e-data;
(B) Separating the e-data into artifact data, called f-data, and artifact-free data, called g-data, while preventing phase distortions in the data;
(C) Processing g-data through a filter to produce a filtered version of g-data, called h-data;
(D) Applying at least one nonlinear measure to at least one type of data selected from the group consisting of e-data, f-data, g-data, and h-data to provide at least one time serial sequence of nonlinear measures from which at least one indicative trend selected from the group consisting of abrupt increases and abrupt decreases can be determined;
(E) Comparing at least one indicative trend with at least one known discriminating indicator;
(F) Determining from said comparison whether differences between similar but different states are indicated; and
(G) Providing notification whether differences between similar but different states are indicated,Said steps B, C, D, E, and F being accomplishable in one integrated sequence of computer analyses.
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Abstract
Methods and apparatus for automatically detecting differences between similar but different states in a nonlinear process monitor nonlinear data. Steps include: acquiring the data; digitizing the data; obtaining nonlinear measures of the data via chaotic time series analysis; obtaining time serial trends in the nonlinear measures; and determining by comparison whether differences between similar but different states are indicated.
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Citations
20 Claims
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1. A method for automatically discriminating between similar but different states in a nonlinear process comprising the steps of:
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(A) Operating a data provision means selected from the group consisting of data storage means and data acquisition means to provide at least one channel of nonlinear data, called e-data; (B) Separating the e-data into artifact data, called f-data, and artifact-free data, called g-data, while preventing phase distortions in the data; (C) Processing g-data through a filter to produce a filtered version of g-data, called h-data; (D) Applying at least one nonlinear measure to at least one type of data selected from the group consisting of e-data, f-data, g-data, and h-data to provide at least one time serial sequence of nonlinear measures from which at least one indicative trend selected from the group consisting of abrupt increases and abrupt decreases can be determined; (E) Comparing at least one indicative trend with at least one known discriminating indicator; (F) Determining from said comparison whether differences between similar but different states are indicated; and (G) Providing notification whether differences between similar but different states are indicated, Said steps B, C, D, E, and F being accomplishable in one integrated sequence of computer analyses. - View Dependent Claims (2, 3, 4, 5)
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6. Apparatus for automatically discriminating between similar but different states in a nonlinear process comprising:
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(A) Data provision means for providing at least one channel of nonlinear data, called e-data, said data provision means being selected from the group consisting of data storage means and data acquisition means; (B) Separation means for separating the e-data into artifact data, called f-data, and artifact-free data, called g-data, while preventing phase distortions in the data, communicably connected to said data provision means; (C) Filter means for filtering g-data to produce a filtered version of g-data, called h-data, communicably connected to said separation means; (D) Application means for applying at least one nonlinear measure to at least one type of data selected from the group consisting of e-data, f-data, g-data, and h-data to provide at least one time serial sequence of nonlinear measures, from which at least one indicative trend selected from the group consisting of abrupt increases and abrupt decreases can be determined, communicably connected to said filter means; (E) Comparison means for comparing at least one indicative trend with at least one known discriminating indicator, communicably connected to said application means; (F) Determination means for determining from said comparison whether differences between similar but different states are indicated, communicably connected to said comparison means; and (G) Notification means for providing notification whether differences between similar but different states are indicated, communicably connected to said determination means, Said elements B, C, D, E, and F being operable in one integrated sequence of computer analyses. - View Dependent Claims (7, 8, 9, 10)
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11. A method for automatically discriminating between similar but different states in a nonlinear process comprising the steps of:
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(A) Operating a data provision means selected from the group consisting of data storage means and data acquisition means to provide at least one channel of nonlinear data, called e-data; (B) Separating the e-data into artifact data, called f-data, and artifact-free data, called g-data, while preventing phase distortions in the data; (C) Processing g-data through a filter to produce a filtered version of g-data, called h-data; (D) Applying the lag derived from the first minimum in the mutual information function to create a d-dimensional probability density function which forms a high-dimensional topology for at least one type of data selected from the group consisting of e-data, f-data, g-data, and h-data whereby at least one indicative trend can be determined; (E) Comparing at least one indicative trend with at least one known discriminating indicator; (F) Determining from said comparison whether differences between similar but different states are indicated; and (G) Providing notification whether differences between similar but different states are indicated, Steps B, C, D, E, and F being accomplishable in one integrated sequence of computer analyses. - View Dependent Claims (12, 13, 14)
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15. A method for automatically discriminating between similar but different states in a nonlinear process comprising the steps of:
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(A) Operating a data provision means selected from the group consisting of data storage means and data acquisition means to provide at least one channel of nonlinear data, called e-data; (B) Separating the e-data into artifact data, called f-data, and artifact-free data, called g-data, while preventing phase distortions in the data; (C) Processing g-data through a filter to produce a filtered version of g-data, called h-data; (D) Applying the lag derived from the first minimum in the mutual information function to create a d-dimensional probability density function which forms a high-dimensional topology for at least one type of data selected from the group consisting of e-data, f-data, g-data, and h-data whereby at least one indicative trend can be determined; (E) Generating a key from the set of indices of each bin in the multidimensional probability density function; (F) Representing occupied bins from the same class by a linked list to provide a reduction in the number of elements to be stored in the manner known as hashing whereby at least one hashed discriminating trend can be determined; (G) Comparing at least one indicative trend with at least one known discriminating indicator; (H) Determining from said comparison whether differences between similar but different states are indicated; and (I) Providing notification whether differences between similar but different states are indicated, Said discriminating indicator being hashed, and Steps B, C, D, E, F, G, and H being accomplishable in one integrated sequence of computer analyses.
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16. An apparatus for automatically discriminating between similar but different states in a nonlinear process comprising:
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(A) Data provision means for providing at least one channel of nonlinear data, called e-data, said data provision means being selected from the group consisting of data storage means and data acquisition means; (B) Separation means for separating the e-data into artifact data, called f-data, and artifact-free data, called g-data, while preventing phase distortions in the data, communicably connected to said data provision means; (C) Filter means for filtering g-data to produce a filtered version of g-data, called h-data, communicably connected to said separation means; (D) Application means for applying the lag derived from the first minimum in the mutual information function to create a d-dimensional probability density function which forms a high-dimensional topology for at least one type of data selected from the group consisting of e-data, f-data, g-data, and h-data whereby at least one indicative trend can be determined, communicably connected to said filter means; (E) Comparison means for comparing at least one indicative trend with at least one known discriminating indicator, communicably connected to said application means; (F) Determination means for determining from the comparison whether differences between similar but different states are indicated, communicably connected to said comparison means; and (G) Notification means for providing notification whether differences between similar but different states are indicated, communicably connected to said determination means, Said discriminating indicator being hashed and said elements B, C, D, E, and F being operable in one integrated sequence of computer analyses. - View Dependent Claims (17, 18, 19)
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20. An apparatus for automatically discriminating between similar but different states in a nonlinear process comprising:
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(A) Data provision means for providing at least one channel of nonlinear data, called e-data, said data provision means being selected from the group consisting of data storage means and data acquisition means; (B) Separation means for separating the e-data into artifact data, called f-data, and artifact-free data, called g-data, while preventing phase distortions in the data, communicably connected to said data provision means; (C) Filter means for filtering g-data to produce a filtered version of g-data, called h-data, communicably connected to said separation means; (D) Application means for applying the lag derived from the first minimum in the mutual information function to create a d-dimensional probability density function which forms a high-dimensional topology for at least one type of data selected from the group consisting of e-data, f-data, g-data, and h-data whereby at least one indicative trend can be determined, communicably connected to said filter means; (E) Generation means for generating a key from the set of indices of each bin in the multidimensional probability density function, communicably connected to said application means; (F) Representation means for representing occupied bins from the same class by a linked list to provide a reduction in the number of elements to be stored in the manner known as hashing, whereby at least one hashed indicative trend can be determined, communicably connected to said generation means; (G) Comparison means for comparing at least one indicative trend with at least one known discriminating indicator, communicably connected to said representation means; (H) Determination means for determining from said comparison whether differences between similar but different states are indicated, communicably connected to said comparison means; and (I) Notification means for providing notification whether differences between similar but different states are indicated, communicably connected to said determination means, Said discriminating indicator being hashed, and Elements B, C, D, E, F, G, and H being operable in one integrated sequence of computer analyses.
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Specification