Data validation and classification in optical analysis systems
원문보기
IPC분류정보
국가/구분
United States(US) Patent
등록
국제특허분류(IPC7판)
G06N-005/00
G06F-001/00
G01J-003/28
G01J-003/02
G01J-003/10
G01J-003/12
출원번호
US-0295631
(2007-06-26)
등록번호
US-9170154
(2015-10-27)
국제출원번호
PCT/US2007/072095
(2007-06-26)
§371/§102 date
20090420
(20090420)
국제공개번호
WO2008/002903
(2008-01-03)
발명자
/ 주소
Myrick, Michael L.
Priore, Ryan J.
Freese, Robert P.
Blackburn, John C.
출원인 / 주소
HALLIBURTON ENERGY SERVICES, INC.
인용정보
피인용 횟수 :
5인용 특허 :
116
초록▼
A method of classifying information in an optical analysis system includes obtaining calibration data defining a plurality of data points, each data point representing values for two or more detectors when sampling a material used to construct a multivariate optical element. Based on the calibration
A method of classifying information in an optical analysis system includes obtaining calibration data defining a plurality of data points, each data point representing values for two or more detectors when sampling a material used to construct a multivariate optical element. Based on the calibration data, one or more validation models can be developed to indicate one or more ranges of expected results. Validation data comprising the models can be used to compare data points representing values for two or more detectors when performing a measurement of a material to determine if the data points fall within an expected range. Classification data can be generated based on the comparison and, in some embodiments, one or more indicators, such as a confidence level in a measurement, can be provided.
대표청구항▼
1. A method of classifying measurement results in a multivariate optical computing system, the method comprising: receiving a first signal based on a first portion of an illumination light that has interacted with a sample, the first signal modified by at least one spectral element;receiving a secon
1. A method of classifying measurement results in a multivariate optical computing system, the method comprising: receiving a first signal based on a first portion of an illumination light that has interacted with a sample, the first signal modified by at least one spectral element;receiving a second signal based on the first portion of the illumination light;separating a second portion of the illumination light from a first portion of the illumination light before the first portion of the illumination light has interacted with the sample;determining a baseline for the first signal and the second signal using a calibration signal from the second portion of the illumination light, the calibration signal modified by the at least one spectral element; andproviding classifying information based on determining if the first signal and the second signal lie in a range of expected results. 2. The method as set forth in claim 1, wherein the at least one spectral element comprises a multivariate optical element. 3. The method as set forth in claim 2, wherein the first signal represents the dot product of a spectrum of light with a weighted regression vector. 4. The method as set forth in claim 1, wherein providing classifying information is further based on validation data indicating a range of expected results. 5. A method of classifying measurement results in a multivariate optical computing system, the method comprising: receiving a first signal based on light that has interacted with a material of interest and at least one spectral element;receiving a second signal based on light that has interacted with the material of interest; andproviding classifying information based on determining if the first signal and the second signal lie in a range of expected results;wherein the validation data comprises data indicating at least one boundary that defines expected results as a function of the value of the first and second signal; andwherein providing classifying information comprises determining where a given pair of simultaneous values for the first and second signal lie relative to the at least one boundary. 6. The method as set forth in claim 1, wherein providing classifying information comprises indicating whether a measurement is valid. 7. The method as set forth in claim 1, wherein providing classifying information comprises indicating a confidence interval in which a measurement lies. 8. The method as set forth in claim 1, further comprising providing at least one measurement result based on data including the first and second signals and providing at least one indicator alongside or as part of the at least one measurement. 9. An optical computing system comprising at least one computing device, the at least one computing device adapted to: receive data from a first detector indicative of a first portion of an illumination light that has interacted with a sample, the first signal modified by at least one spectral element;receive data from a second detector indicative of the first portion of the illumination light;separating a second portion of the illumination light from a first portion of the illumination light before the first portion of the illumination light has interacted with the sample;determining a baseline for the data from the first and second detectors using a calibration signal from the second portion of the illumination light, the calibration signal modified by the at least one spectral element; andproduce classification data based on determining if the data from the first and second detectors lie in a range of expected results. 10. The system as set forth in claim 9, wherein the at least one computing device is further adapted to access validation data and to produce classification data based on evaluating the data from the first and second detectors and the validation data. 11. An optical computing system comprising at least one computing device, the at least one computing device adapted to: receive data from a first detector indicative of light that has interacted with a material of interest and at least one spectral element;receive data from a second detector indicative of light that has interacted with the material of interest; andproduce classification data based on determining if the data from the first and second detectors lie in a range of expected results;wherein the at least one computing device is further adapted to access validation data and to produce classification data based on evaluating the data from the first and second detectors and the validation data;wherein the validation data comprises data indicating at least one boundary that defines expected results as a function of the value of the first and second signal; andwherein producing classification data comprises determining where a given pair of simultaneous values for the first and second signal lie relative to the at least one boundary. 12. The system as set forth in claim 9, wherein the classification data comprises an indicator of whether a measurement based on data from at least one of the first and second detectors is valid. 13. The system as set forth in claim 9, wherein the classification data comprises an indicator of a confidence interval of a measurement based on data from at least one of the first and second detectors. 14. The system as set forth in claim 9, wherein the at least one computing device is further adapted to: provide at least one measurement result based on data from the first and second detectors; andprovide at least one indicator of the classification data alongside or as part of the at least one measurement result. 15. The system as set forth in claim 9, further comprising: at least one light source configured to illuminate a material of interest;first and second broadband detectors each configured to detect light over a wavelength range and to provide data to the at least one computing device; anda multivariate optical element positioned to interact with light from the material of interest;wherein said first broadband detector is positioned to receive light that has interacted with the material of interest and the multivariate optical element; andwherein said second broadband detector is positioned to receive light that has interacted with the material of interest, but not the multivariate optical element. 16. The system as set forth in claim 9, wherein the at least one computing device comprises a computer adapted by software to receive data and produce classification data. 17. A method of configuring a measurement system, the method comprising: obtaining calibration data, the calibration data comprising a plurality of data points;based on the calibration data, constructing at least one multivariate optical element based on the data points;based on the data points, preparing a validation model, the validation model comprising a boundary of values dividing an area into a valid result area and an invalid result area;including the at least one multivariate optical element in a measurement system comprising at least one controller; andconfiguring the at least one controller to validate measurement results based on the validation model. 18. The method of claim 17, wherein validation model further comprises a two-dimensional area defined by the plurality of data points, the two-dimensional area comprising one or more boundaries. 19. The method of claim 1 wherein the sample comprises oil contained in water or water contained in oil. 20. A method of classifying measurement results in a multivariate optical computing system, the method comprising: receiving a first signal based on a first portion of an illumination light that has interacted with a material of interest, the first signal modified by at least one spectral element;receiving a second signal based on the first portion of the illumination light;providing classifying information based on determining if the first signal and the second signal lie in a range of expected results; anddetermining a baseline of at least one of the first signal and the second signal using a second portion of the illumination light, wherein:the second portion of the illumination light is separated from the first portion of the illumination light before the first portion of the illumination light has interacted with the sample. 21. An optical computing system comprising at least one computing device, the at least one computing device adapted to: receive data from a first detector indicative of a first portion of an illumination light that has interacted with a sample, the first signal modified by at least one spectral element;receive data from a second detector indicative of the first portion of the illumination light;determine a baseline for at least one of the data from a first detector and the second detector using data indicative of a second portion of the illumination light, wherein: the second portion of the illumination light is separated from the first portion of the illumination light before the first portion of the illumination light has interacted with the sample; andproduce classification data based on determining if the data from the first and second detectors lie in a range of expected results. 22. The method of claim 5 wherein the material of interest comprises oil contained in water or water contained in oil. 23. The method of claim 5 wherein the classifying information comprises an octane rating or a lead content in a gasoline sample. 24. The optical computing system of claim 11 wherein the material of interest comprises oil contained in water or water contained in oil. 25. The optical computing system of claim 11 wherein the classifying information comprises an octane rating or a lead content in a gasoline sample. 26. The method of claim 20 wherein the sample comprises oil contained in water or water contained in oil. 27. The method of claim 20 wherein the classifying information comprises an octane rating or a lead content in a gasoline sample. 28. The optical computing system of claim 21 wherein the material of interest comprises oil contained in water or water contained in oil. 29. The optical computing system of claim 21 wherein the classifying information comprises an octane rating or a lead content in a gasoline sample.
연구과제 타임라인
LOADING...
LOADING...
LOADING...
LOADING...
LOADING...
이 특허에 인용된 특허 (116)
Pletcher Derek (Romsey GBX) Evans John (Romsey GBX) Warburton Piers R. G. (Guernsey GBX) Gibbs Trevor K. (Rayne GBX), Acidic gas sensors and method of using same.
Clarke David J. (Salisbury GBX) Zamani-Farahani Freidoun (Andover GBX), Analytical apparatus utilizing a colorimetric or other optically detectable effect.
Cheung Peter W. (Mercer Island WA) Gauglitz Karl F. (Kirkland WA) Hunsaker Scott W. (Seattle WA) Prosser Stephen J. (Lynnwood WA) Wagner Darrell O. (Monroe WA) Smith Robert E. (Edmonds WA), Apparatus for the automatic calibration of signals employed in oximetry.
Sleep Nicholas J.,GBX ; Proudfoot Andrew H.,GBX ; Owen Stephen,GBX ; Bargh Adrian Neil,GBX ; Kennedy Michael, Customer specific packaging line having containers with tag means containing medication order information.
Weirich John B. ; Bland Ronald G. ; Smith ; Jr. William W. ; Krueger Volker,DEX ; Harrell John W. ; Nasr Hatem N. ; Papanyan Valeri, Drilling systems with sensors for determining properties of drilling fluid downhole.
Lewis, E. Neil; Strachan, David J.; Kidder, Linda H., High-volume on-line spectroscopic composition testing of manufactured pharmaceutical dosage units.
Nagaya Kazuhiko (Tokyo JPX) Sakurai Hiroshi (Yokohama JPX) Michell Andrew K. (Cambridge GB2), Hygrometer with plural measuring bones and redundancy system circuit.
Tambo Norihito (Hokkaido) Matsui Yoshihiko (Hokkaido) Ohto Tokio (Kawasaki) Zaitsu Yasushi (Kawasaki) Hiraoka Mutsuhisa (Kawasaki) Hoshikawa Hiroshi (Kawasaki) Ito Haruo (Kawasaki JPX), Method and apparatus for detecting flocculation process of components in liquid.
Son Jung Y. (Seoul KRX) Jeon Hyung W. (Seoul KRX) Choi Yong J. (Seoul KRX) Bobrinev Vladimir I. (Moscow RUX), Method and apparatus for direct transmission of an optical image.
Sabsabi Mohamad,CAX ITX J4B 6J4 ; Bussiere Jean F.,CAX ITX J3V 5N8, Method and apparatus for rapid in situ analysis of preselected components of homogeneous solid compositions, especially.
Richmond Eric W. (North Wales PA) Buchanan Bruce R. (Perkiomenville PA) Baxter Mark A. (Lansdale PA) Duff Andy (Lansdale PA) Tully Oksana M. (Lansdale PA) Thornton Samuel A. (Lansdale PA), Method and system for determining the homogeneity of tablets.
Baylor Lewis C. (North Augusta SC) Buchanan Bruce R. (Aiken SC) O\Rourke Patrick E. (Martinez GA), Method for verification of constituents of a process stream just as they go through an inlet of a reaction vessel.
Kraus Paul R. ; McClain Robert D. ; Poindexter Michael K., Method to monitor and control chemical treatment of petroleum, petrochemical and processes with on-line quartz crystal.
Yongdong Wang ; David H. Tracy ; Paul G. Saviano ; Alan M. Ganz ; Koichi Nishikida ; Gitesh Kumar, Monitoring constituents of an animal organ using statistical correlation.
Nazarian, Richard A.; Lucke, Lori E.; Alfini, Susan S.; Bina, Mark J.; Harris, Paul; Geatz, Michael W.; Evans, Don W. E., Motion cancellation of optical input signals for physiological pulse measurement.
Dono Nicholas R. (Hopewell Junction NY) Green ; Jr. Paul E. (Mount Kisco NY) Perrier Philippe A. (Viroslay FRX), Multiple-cavity optical filter using change of cavity length.
Myrick, Michael L.; Freese, Robert P.; James, Jonathan H.; Priore, Ryan J.; Blackburn, John C., Multivariate optical elements for optical analysis system.
Myrick, Michael L.; Freese, Robert P.; Profeta, Luisa T. M.; James, Jonathan H.; Blackburn, John C.; Priore, Ryan J., Optical analysis system and elements to isolate spectral region.
Freese, Robert P.; Priore, Ryan J.; Blackburn, John C.; James, Jonathan H.; Perkins, David L., Optical analysis systems and methods for dynamic, high-speed detection and real-time multivariate optical computing.
Butler Michael A. ; Ricco Antonio J. ; Sinclair Michael B. ; Senturia Stephen D., Optical apparatus for forming correlation spectrometers and optical processors.
Vogel, Kurt R.; Dinneen, Timothy P.; Deeds, Michael E.; Ensher, Jason R.; Myatt, Christopher J., Optical frequency sweep control and readout by using a phase lock.
Khoury Jehad (Concord NH) Woods Charles L. (Stow MA) Fu Jack (Brighton MA), Phase coding technique for one-way image transmission through an aberrating medium.
Watson Joseph,GBX ; Tamadoni Reza,GBX ; Jones Barbara L.,GBX ; Peter Kenneth W.,GBX ; Wylie Thomas F.,GBX, Portable electrometric apparatus for roadside analysis of automotive exhaust emission.
Chow Calvin Y. H. (Portola Valley CA) Humphries Gillian M. (Los Altos CA) Kung Viola T. (Menlo Park CA) Lacy Michael M. (Ben Lomand CA) Hayter Paul (Los Altos CA), Single source multi-site photometric measurement system.
Pearson Lee H. (Bear River City UT) Stover John (Bozeman MT) Knighton Mary (Bozeman MT) Swimley Brett (Bozeman MT), Surface inspection and characterization system and process.
Harris Michael T., System and method for providing a comparable branded product based on a current branded product for non-comparison shopped products.
Freese, Robert; Jones, Christopher Michael; Perkins, David; Simcock, Michael; Soltmann, William, Methods and devices for optically determining a characteristic of a substance.
Freese, Robert P.; Jones, Christopher M.; Pelletier, Michael T.; Daussin, Rory D.; Hayworth, Robert D., Methods for monitoring fluids within or produced from a subterranean formation during acidizing operations using opticoanalytical devices.
Freese, Robert P.; Jones, Christopher M.; Pelletier, Michael T.; Daussin, Rory D.; Loveless, David M.; Haggstrom, Johanna, Methods for monitoring fluids within or produced from a subterranean formation using opticoanalytical devices.
Freese, Robert P.; Jones, Christopher M.; Pelletier, Michael T.; Daussin, Rory D.; Loveless, David M.; Haggstrom, Johanna, Methods for monitoring the formation and transport of a treatment fluid using opticoanalytical devices.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.