专利快速检索-快速检索全球专利，免费商用专利数据库-IPRDB

1. 发明申请

US20100161530A1 METHOD FOR ENHANCED ACCURACY IN PREDICTING PEPTIDES ELUTION TIME USING LIQUID SEPARATIONS OR CHROMATOGRAPHY 审中-公开
标题翻译：使用液相分离或色谱法预测肽时间的增强精度的方法
公开(公告)号：US20100161530A1
公开(公告)日：2010-06-24
申请号：US12573738
申请日：2009-10-05
申请人： Konstantinos Petritis , Lars J. Kangas , Gordon A. Anderson , Richard D. Smith
发明人： Konstantinos Petritis , Lars J. Kangas , Gordon A. Anderson , Richard D. Smith
IPC分类号： G06N3/12 , G06N3/08 , G06F15/18
CPC分类号： G01N33/6806 , G01N30/8693 , G01N2030/8831 , G16B40/00
摘要： A method for predicting the elution time of a peptide in chromatographic and electrophoretic separations by first providing a data set of known elution times of known peptides, then creating a plurality of vectors, each vector having a plurality of dimensions, and each dimension representing positional information about at least a portion of the amino acids present in the known peptides. A hypothetical vector is then created by assigning dimensional values for at least one hypothetical peptide, and a predicted elution time for the hypothetical vector is created by performing at least one multivariate regression fitting the hypothetical peptide to the plurality of vectors. Preferably, the multivariate regression is accomplished by the use of an artificial neural network and the elution times are first normalized using linear regression.
摘要翻译：通过首先提供已知肽的已知洗脱时间的数据集，然后产生多个向量，每个载体具有多个维度，并且每个维度表示位置信息，用于预测肽在色谱和电泳分离中的洗脱时间的方法约至少一部分已知肽中存在的氨基酸。然后通过为至少一个假想肽分配尺寸值来产生假想载体，并且通过对多个载体进行拟合假想肽的至少一个多变量回归而产生假设载体的预测洗脱时间。优选地，通过使用人造神经网络实现多变量回归，并且首先使用线性回归归一化洗脱时间。

2. 发明授权

US07136759B2 Method for enhanced accuracy in predicting peptides using liquid separations or chromatography 失效
标题翻译：使用液体分离或色谱法增强预测肽的准确性的方法
公开(公告)号：US07136759B2
公开(公告)日：2006-11-14
申请号：US10323387
申请日：2002-12-18
申请人： Lars J. Kangas , Kenneth J. Auberry , Gordon A. Anderson , Richard D. Smith
发明人： Lars J. Kangas , Kenneth J. Auberry , Gordon A. Anderson , Richard D. Smith
IPC分类号： G06F17/11 , G06F17/50 , G06F19/00
CPC分类号： G01N30/8693 , G01N33/6806 , G01N2030/8831 , G06F19/24
摘要： A method for predicting the elution time of a peptide in chromatographic and electrophoretic separations by first providing a data set of known elution times of known peptides, then creating a plurality of vectors, each vector having a plurality of dimensions, and each dimension representing the elution time of amino acids present in each of these known peptides from the data set. The elution time of any protein is then be predicted by first creating a vector by assigning dimensional values for the elution time of amino acids of at least one hypothetical peptide and then calculating a predicted elution time for the vector by performing a multivariate regression of the dimensional values of the hypothetical peptide using the dimensional values of the known peptides. Preferably, the multivariate regression is accomplished by the use of an artificial neural network and the elution times are first normalized using a transfer function.
摘要翻译：一种通过首先提供已知肽的已知洗脱时间的数据集，然后产生多个载体，每个载体具有多个维度，并且每个维度代表洗脱的方法，用于预测肽在色谱和电泳分离中的洗脱时间的方法来自数据集的这些已知肽各自存在的氨基酸的时间。然后通过首先通过分配至少一个假想肽的氨基酸的洗脱时间的维度值来产生载体，然后通过执行尺寸的多元回归来计算载体的预测洗脱时间来预测任何蛋白质的洗脱时间使用已知肽的尺寸值的假想肽的值。优选地，通过使用人造神经网络实现多元回归，并且首先使用传递函数归一化洗脱次数。

3. 发明授权

US5680866A Artificial neural network cardiopulmonary modeling and diagnosis 失效
标题翻译：人工神经网络心肺建模与诊断
公开(公告)号：US5680866A
公开(公告)日：1997-10-28
申请号：US625025
申请日：1996-03-29
申请人： Lars J. Kangas , Paul E. Keller
发明人： Lars J. Kangas , Paul E. Keller
IPC分类号： G06F19/00 , A61B5/0205
CPC分类号： G06F19/345 , G06F19/3487 , G06F19/322 , Y10S706/924
摘要： The present invention is a method of diagnosing a cardiopulmonary condition in an individual by comparing data from a progressive multi-stage test for the individual to a non-linear multi-variate model, preferably a recurrent artificial neural network having sensor fusion. The present invention relies on a cardiovascular model developed from physiological measurements of an individual. Any differences between the modeled parameters and the parameters of an individual at a given time are used for diagnosis.
摘要翻译：本发明是通过比较来自个体的渐进多阶段测试的数据与非线性多变量模型（优选具有传感器融合的复发性人工神经网络）的数据来诊断个体中的心肺病的方法。本发明依赖于从个体的生理测量开发的心血管模型。模拟参数和给定时间个体参数之间的差异用于诊断。

4. 发明授权

US07457785B1 Method and apparatus to predict the remaining service life of an operating system 有权
标题翻译：用于预测操作系统的剩余使用寿命的方法和装置
公开(公告)号：US07457785B1
公开(公告)日：2008-11-25
申请号：US10362717
申请日：2000-08-25
申请人： Frank L. Greitzer , Lars J. Kangas , Kristine M. Terrones , Melody A. Maynard , Ronald A. Pawlowski , Thomas A. Ferryman , James R. Skorpik , Bary W. Wilson
发明人： Frank L. Greitzer , Lars J. Kangas , Kristine M. Terrones , Melody A. Maynard , Ronald A. Pawlowski , Thomas A. Ferryman , James R. Skorpik , Bary W. Wilson
IPC分类号： G06F15/18
CPC分类号： G07C3/00 , G01D1/18 , G05B23/0283 , G05B2219/32234 , G05B2219/37252
摘要： A method and computer-based apparatus for monitoring the degradation of, predicting the remaining service life of, and/or planning maintenance for, an operating system are disclosed. Diagnostic information on degradation of the operating system is obtained through measurement of one or more performance characteristics by one or more sensors onboard and/or proximate the operating system. Though not required, it is preferred that the sensor data are validated to improve the accuracy and reliability of the service life predictions. The condition or degree of degradation of the operating system is presented to a user by way of one or more calculated, numeric degradation figures of merit that are trended against one or more independent variables using one or more mathematical techniques. Furthermore, more than one trendline and uncertainty interval may be generated for a given degradation figure of merit/independent variable data set. The trendline(s) and uncertainty interval(s) are subsequently compared to one or more degradation figure of merit thresholds to predict the remaining service life of the operating system. The present invention enables multiple mathematical approaches in determining which trendline(s) to use to provide the best estimate of the remaining service life.
摘要翻译：公开了一种用于监视操作系统的劣化，预测剩余使用寿命和/或计划维护的方法和基于计算机的装置。通过船上和/或操作系统附近的一个或多个传感器测量一个或多个性能特征来获得关于操作系统退化的诊断信息。虽然不是必需的，但是优选地，传感器数据被验证以提高使用寿命预测的准确性和可靠性。通过使用一种或多种数学技术针对一个或多个独立变量趋势化的一个或多个计算的数字退化品质因数向用户呈现操作系统的劣化程度或程度。此外，对于给定的优劣/独立变量数据集的劣化图可以产生多于一个趋势线和不确定性间隔。趋势线和不确定性间隔随后与一个或多个劣化品质阈值进行比较，以预测操作系统的剩余使用寿命。本发明使多种数学方法能够确定哪些趋势线用于提供剩余使用寿命的最佳估计。

5. 发明授权

US5775330A Neurometric assessment of intraoperative anesthetic 失效
标题翻译：术中麻醉的神经测量评估
公开(公告)号：US5775330A
公开(公告)日：1998-07-07
申请号：US681196
申请日：1996-07-22
申请人： Lars J. Kangas , Paul E. Keller
发明人： Lars J. Kangas , Paul E. Keller
IPC分类号： A61B5/11 , A61M16/01 , A61B5/04
CPC分类号： A61B5/4821 , A61B5/1106 , A61B5/7264 , A61B5/6814 , A61B5/7257 , A61B5/7267 , A61M16/01 , A61M2230/10 , Y10S128/923
摘要： The present invention is a method and apparatus for collecting EEG data, reducing the EEG data into coefficients, and correlating those coefficients with a depth of unconsciousness or anesthetic depth, and which obtains a bounded first derivative of anesthetic depth to indicate trends. The present invention provides a developed artificial neural network based method capable of continuously analyzing EEG data to discriminate between awake and anesthetized states in an individual and continuously monitoring anesthetic depth trends in real-time. The present invention enables an anesthesiologist to respond immediately to changes in anesthetic depth of the patient during surgery and to administer the correct amount of anesthetic.
摘要翻译：本发明是用于收集EEG数据的方法和装置，将EEG数据减少为系数，并将这些系数与无意识深度或麻醉深度相关联，并获得麻醉深度的有界一阶指示趋势。本发明提供了一种开发的基于人工神经网络的方法，其能够连续地分析EEG数据以区分个体中的唤醒和麻醉状态，并且实时地连续监测麻醉深度趋势。本发明使得麻醉医师能够立即响应手术期间患者的麻醉深度的变化并施用正确量的麻醉剂。

你已经成功收藏专利！

检索式保存成功!

IPRDB

热门服务

关于我们

友情链接

联系方式