公开(公告)号：WO2013022570A3

公开(公告)日：2013-09-26

申请号：PCT/US2012047065

申请日：2012-07-17

Applicant: HALLIBURTION ENERGY SEVICES INC ,  FREESE ROBERT P ,  JONES CHRISTOPHER M ,  PELLETIER MICHAEL T ,  DAUSSIN RORY D ,  HAYWORTH ROBERT D

Inventor： FREESE ROBERT P ,  JONES CHRISTOPHER M ,  PELLETIER MICHAEL T ,  DAUSSIN RORY D ,  HAYWORTH ROBERT D

IPC: C09K8/72 ,  E21B43/00 ,  E21B47/00 ,  E21B47/10 ,  E21B49/08 ,  G01J3/00

CPC classification number: G01N21/85 ,  C09K8/72 ,  E21B43/26 ,  E21B43/28 ,  E21B2049/085 ,  G01N2201/1296

Abstract: In or near real-time monitoring of fluids can take place using an opticoanalyiical device that is configured for monitoring the fluid. Fluids can be monitored prior to or during their introduction into a subterranean formation using the opticoanalyiical devices. Produced fluids from a subterranean formation can be monitored in a like manner. The methods can comprise providing an acidizing fluid comprising a base fluid and at least one acid; introducing the acidizing fluid into a subterranean formation; allowing the acidizing fluid to perform an acidizing operation in the subterranean formation; and monitoring a characteristic of the acidizing fluid or a formation fluid using at least a first opticoanalyiical device within the subterranean formation, during a flow back of the acidizing fluid produced from the subterranean formation, or both.

Abstract translation: 使用配置用于监测流体的光学分析装置可以在流体中或附近实时监测流体。 可以在使用光学分析装置将流体引入地下地层之前或期间监测流体。 可以以类似的方式监测来自地下地层的产出流体。 该方法可以包括提供包含基液和至少一种酸的酸化流体; 将酸化流体引入地下地层; 使酸化流体在地下地层中进行酸化操作; 以及在由地下地层产生的酸化流体的回流期间，或者在这两者期间，使用地下地层内的至少第一光学分析装置监测酸化流体或地层流体的特征。

公开(公告)号：WO2013093913A1

公开(公告)日：2013-06-27

申请号：PCT/IL2012/050534

申请日：2012-12-19

Applicant: OPTICUL DIAGNOSTICS LTD. ,  OPTICUL DIAGNOSTICS INC.

Inventor： GANNOT, Gallya ,  LEDERMAN, Dror ,  MOINUDDIN, Hassan ,  GANNOT, Israel

IPC: G01N21/00 ,  G01N21/25 ,  G01N33/00

CPC classification number: C12Q1/04 ,  C12M41/36 ,  G01N21/255 ,  G01N21/3577 ,  G01N21/39 ,  G01N21/64 ,  G01N21/65 ,  G01N2021/1742 ,  G01N2021/1744 ,  G01N2021/3595 ,  G01N2021/6417 ,  G01N2201/06113 ,  G01N2201/129 ,  G01N2201/1296 ,  G06F19/10

Abstract: A spectroscopic method for spectroscopic detection and identification of bacteria in culture is disclosed. The method incorporates construction of at least one data set, which may be a spectrum, interference pattern, or scattering pattern, from a cultured sample suspected of containing said bacteria. The data set is corrected for the presence of water in the sample, spectral features are extracted using a principal components analysis, and the features are classified using a learning algorithm. In some embodiments of the invention, for example, to differentiate MRS A from MSSA, a multimodal analysis is performed in which identification of the bacteria is made based on a spectrum of the sample, an interference pattern used to determine cell wall thickness, and a scattering pattern used to determine cell wall roughness. An apparatus for performing the method is also disclosed, one embodiment of which incorporates a multiple sample analyzer.

Abstract translation: 公开了用于光谱检测和鉴定培养细菌的光谱方法。 该方法包括来自怀疑含有所述细菌的培养样品的至少一个数据集的构建，其可以是光谱，干涉图案或散射图案。 对样品中水的存在进行数据集的校正，使用主成分分析提取光谱特征，并使用学习算法对特征进行分类。 在本发明的一些实施方案中，例如为了区分MRS A和MSSA，进行多模式分析，其中基于样品的光谱，用于确定细胞壁厚度的干涉图案和 用于确定细胞壁粗糙度的散射图。 还公开了一种用于执行该方法的装置，其一个实施例包括多个样本分析器。

公开(公告)号：WO2011065814A1

公开(公告)日：2011-06-03

申请号：PCT/MY2010/000302

申请日：2010-11-25

Applicant: UNIVERSITI TEKNOLOGI MALAYSIA ,  RUBlYAH Yusof ,  MARZUKI KHALID

Inventor： RUBlYAH Yusof ,  MARZUKI KHALID

IPC: G01N33/46 ,  G01N21/17 ,  G06T5/00 ,  G01N21/01 ,  G01N21/84 ,  G06T7/00

CPC classification number: G01N21/8986 ,  G01N33/46 ,  G01N2201/1296 ,  G06K9/52 ,  G06K2209/17 ,  G06T7/0012 ,  G06T2207/20076 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30161

Abstract: A method, a system and a computer program are disclosed for recognizing of at least one wood species. In particularity, the method acquires an image of the at least one wood species for analyzing the image using an image acquisition module (IAM) (220). In addition, the method processes the image for enhancing quality of the acquired image using a pre processing module (PPM) (230). Additionally, the method extracts a plurality of features of the processed image for classifying at least one pattern using a feature extraction module (FEM) (240). Further, the method classifies the at least one pattern for the recognizing the at least one wood species using a pattern classification module (PCM) (250).

Abstract translation: 公开了用于识别至少一种木材种类的方法，系统和计算机程序。 特别地，该方法获取用于使用图像获取模块（IAM）分析图像的至少一种木材种类的图像（220）。 此外，该方法使用预处理模块（PPM）处理图像以提高所获取的图像的质量（230）。 此外，该方法提取处理图像的多个特征，以使用特征提取模块（FEM）（240）对至少一个图案进行分类。 此外，该方法使用模式分类模块（PCM）对用于识别至少一种木材种类的至少一种模式进行分类（250）。

公开(公告)号：WO2009080049A1

公开(公告)日：2009-07-02

申请号：PCT/DK2008/050329

申请日：2008-12-19

Applicant: DMA SORPTION APS ,  OLSEN, Ole ,  KAMPMANN, Kristoffer

Inventor： OLSEN, Ole ,  KAMPMANN, Kristoffer

IPC: G01N21/35 ,  G01N21/64 ,  G01J3/44 ,  G01N33/22 ,  G01N33/28

CPC classification number: G01N21/359 ,  G01N21/3577 ,  G01N21/64 ,  G01N21/6408 ,  G01N21/6445 ,  G01N21/8507 ,  G01N21/94 ,  G01N33/2888 ,  G01N2021/6417 ,  G01N2201/129 ,  G01N2201/1293 ,  G01N2201/1296

Abstract: The present invention relates to a method and a device for monitoring oil condition and/or quality based on fluorescence and/or NIR spectra as well as laboratory reference measurements on a set of oil samples. Through the use of chemometric data analysis (i.e. multivariate data analysis) the spectroscopic signals and patterns will be correlated to the laboratory reference measurements that describe the condition and/or quality of the oil. Based on this relation it is possible to predict the reference measurements and/or conditions of a new oil sample based solely on a fluorescence and/or NIR spectrum of the sample.

Abstract translation: 本发明涉及一种用于基于荧光和/或NIR光谱以及一组油样品上的实验室参考测量来监测油状况和/或质量的方法和装置。 通过使用化学计量数据分析（即多变量数据分析），光谱信号和模式将与描述油的状况和/或质量的实验室参考测量值相关。 基于该关系，可以仅基于样品的荧光和/或NIR光谱预测新的油样品的参考测量和/或条件。

公开(公告)号：WO2008100582A3

公开(公告)日：2008-10-09

申请号：PCT/US2008001988

申请日：2008-02-14

Applicant: CHEMIMAGE CORP ,  MAIER JOHN ,  COHEN JEFFREY ,  PANZA JANICE L ,  STEWART SHONA ,  DRAUGH AMY J

Inventor： MAIER JOHN ,  COHEN JEFFREY ,  PANZA JANICE L ,  STEWART SHONA ,  DRAUGH AMY J

IPC: C12Q1/00

CPC classification number: G01N21/65 ,  G01J3/02 ,  G01J3/0291 ,  G01J3/0294 ,  G01J3/2823 ,  G01J3/44 ,  G01J2003/2826 ,  G01N2021/656 ,  G01N2201/1293 ,  G01N2201/1296

Abstract: A system and method to predict the progression of disease of a test sample is provided A group of known biological samples is provided, each having an associated known outcome including a non-diseased or diseased sample. A Raman data set is obtained for each known biological sample. Each data set is analyzed to identify a diseased or non-diseased reference data set. A first database is generated which contains reference Raman data sets for all diseased samples. A second-database is generated which contains reference data sets for all non-diseased samples. A test Raman data set of a test biological sample having an unknown disease status is received. A diagnostic is provided as to whether the test sample is non-diseased or diseased The diagnostic is obtained by comparing the test data set against the reference data sets in the databases using a chemometric technique to predict the progression of disease.

Abstract translation: 提供了一种用于预测测试样品的疾病进展的系统和方法。提供了一组已知的生物样品，每个具有相关联的已知结果，包括非患病或患病样品。 获得每个已知生物样品的拉曼数据集。 分析每个数据集以识别患病或非患病参考数据集。 生成包含所有患病样本的参考拉曼数据集的第一个数据库。 生成包含所有非病变样本的参考数据集的第二数据库。 接收具有未知疾病状态的测试生物样品的测试拉曼数据集。 提供了关于测试样品是否患病或患病的诊断。诊断是通过使用化学计量技术比较测试数据集与数据库中的参考数据集来预测疾病进展而获得的。

公开(公告)号：WO2008100582A2

公开(公告)日：2008-08-21

申请号：PCT/US2008001988

申请日：2008-02-14

Applicant: CHEMIMAGE CORP ,  MAIER JOHN ,  COHEN JEFFREY ,  PANZA JANICE L ,  STEWART SHONA ,  DRAUGH AMY J

Inventor： MAIER JOHN ,  COHEN JEFFREY ,  PANZA JANICE L ,  STEWART SHONA ,  DRAUGH AMY J

IPC: C12Q1/00

CPC classification number: G01N21/65 ,  G01J3/02 ,  G01J3/0291 ,  G01J3/0294 ,  G01J3/2823 ,  G01J3/44 ,  G01J2003/2826 ,  G01N2021/656 ,  G01N2201/1293 ,  G01N2201/1296

Abstract: A system and method to predict the progression of disease of a test sample. A group of known biological samples is provided. Each known biological sample has an associated known outcome including a non-diseased sample or a diseased sample. A Raman data set is obtained for each known biological sample. Each Raman data set is analyzed to identify a diseased or non-diseased reference Raman data set depending on whether respective biological sample is the non-diseased sample or the diseased sample. A first database is generated where the first database contains reference Raman data sets for all diseased samples. A second database is generated where the second database contains reference Raman data sets for all non-diseased samples. A test Raman data set of a test biological sample is received, where the test biological sample has an unknown disease status. A diagnostic is provided as to whether the test sample is a non-diseased sample or a diseased sample. The diagnostic is obtained by comparing the test Raman data set against the reference Raman data sets in the first and the second databases using a chemometric technique. A prediction of the progression of disease may be then provided.

Abstract translation: 一种预测测试样本疾病进展的系统和方法。 提供了一组已知的生物样品。 每个已知的生物样品具有相关的已知结果，包括非患病样品或患病样品。 获得每个已知生物样品的拉曼数据集。 分析每个拉曼数据集，以根据相应的生物样品是非患病样品还是患病样品来鉴定患病或非患病参考拉曼数据集。 生成第一个数据库，其中第一个数据库包含所有患病样本的参考拉曼数据集。 生成第二数据库，其中第二数据库包含所有非患病样本的参考拉曼数据集。 接受测试生物样品的测试拉曼数据集，其中测试生物样品具有未知的疾病状态。 提供了关于测试样品是否是非患病样品或患病样品的诊断。 通过使用化学计量技术将测试拉曼数据集与第一和第二数据库中的参考拉曼数据集进行比较来获得诊断。 可以提供疾病进展的预测。

公开(公告)号：WO2004023974A3

公开(公告)日：2004-06-03

申请号：PCT/US0328230

申请日：2003-09-08

Applicant: EURO CELTIQUE SA ,  CIURCZAK EMIL ,  RITCHIE GARY

Inventor： CIURCZAK EMIL ,  RITCHIE GARY

IPC: G01N21/35 ,  A61B20060101 ,  A61B5/00 ,  A61B5/145 ,  A61B5/1455 ,  A61B19/00 ,  G01J3/08 ,  G01J3/12 ,  G01J3/18 ,  G01J3/26 ,  G01N21/47

CPC classification number: G01J3/12 ,  A61B5/0022 ,  A61B5/14532 ,  A61B5/14546 ,  A61B5/1455 ,  A61B5/14551 ,  G01J3/021 ,  G01J3/0229 ,  G01J3/0232 ,  G01J3/0254 ,  G01J3/0256 ,  G01J3/0264 ,  G01J3/08 ,  G01J3/1256 ,  G01J3/18 ,  G01J3/26 ,  G01J2003/1226 ,  G01J2003/1243 ,  G01N21/359 ,  G01N21/474 ,  G01N2021/1744 ,  G01N2021/4757 ,  G01N2201/0221 ,  G01N2201/065 ,  G01N2201/1293 ,  G01N2201/1296

Abstract: A system for predicting blood constituent values in a patient includes a remote wireless noninvasive spectral device (2) for generating a spectral scan of a body part of the patient. The system also includes a remote invasive device (1) and a central processing device (3). The remote invasive device (1) generates a constituent value for the patient, which the central processing device (3) predicts a blood constituent value of the patient based upon the spectral scan and the constituent value.

Abstract translation: 用于预测患者血液成分值的系统包括用于产生患者身体部位的光谱扫描的远程无线无创光谱设备（2）。 该系统还包括远程侵入设备（1）和中央处理设备（3）。 远程侵入性装置（1）生成患者的构成值，中央处理装置（3）基于光谱扫描和构成值预测患者的血液成分值。

公开(公告)号：US20240355423A1

公开(公告)日：2024-10-24

申请号：US18684138

申请日：2021-08-25

Applicant: Hewlett-Packard Development Company, L.P.

Inventor： Yiming Zuo ,  Anton Wiranata ,  Amy Devitt ,  Yang Lei ,  Steven James Barcelo ,  Brian John Keefe

IPC: G16B40/10 ,  C12Q1/6844 ,  G01N21/64 ,  G16B40/20

CPC classification number: G16B40/10 ,  C12Q1/6844 ,  G01N21/6408 ,  G16B40/20 ,  G01N2201/1296

Abstract: Examples of methods are described herein. In some examples, a method includes determining signal variation data of a fluorescence signal measured from an amplification procedure of a nucleic acid sample. In some examples, the method includes detecting, using a machine learning model, a target nucleic acid strand in the nucleic acid sample based on the signal variation data.

公开(公告)号：US20240280474A1

公开(公告)日：2024-08-22

申请号：US18565310

申请日：2022-06-06

Applicant: RENISHAW plc

Inventor： Ian Mac BELL

IPC: G01N21/27

CPC classification number: G01N21/274 ,  G01N2201/127 ,  G01N2201/1296

Abstract: A method for removing noise from spectral data recorded using a spectrometer. The method includes normalising spectral data to generate normalised spectral data and applying a machine learning model to the normalised spectral data. The machine learning model is trained to remove noise from spectral data using normalised training data, wherein the spectral data is normalised based on a different scaling to the normalisation of the training data.

公开(公告)号：US20240255440A1

公开(公告)日：2024-08-01

申请号：US18419952

申请日：2024-01-23

Applicant: EBARA CORPORATION

Inventor： Yuki WATANABE

IPC: G01N21/95 ,  G01B11/06

CPC classification number: G01N21/9501 ,  G01B11/0633 ,  G01B2210/56 ,  G01N2201/1293 ,  G01N2201/1296

Abstract: An abnormality detection method that can automatically detect abnormality in preset spectrum data, such as reference intensity data (base intensity data) used for optical measurement of a film thickness is disclosed. The abnormality detection method includes: creating the preset spectrum data before polishing of the workpiece; inputting the preset spectrum data to an autoencoder which is a trained model constructed by machine learning using training data including a plurality of normal preset spectra data; calculating a difference between output data output from the autoencoder and the preset spectrum data; and determining that there is an abnormality in the preset spectrum data when the difference is larger than a threshold value.