公开(公告)号：US08815913B2

公开(公告)日：2014-08-26

申请号：US12480675

申请日：2009-06-08

申请人： Huailin Sun ,  Jian Zhang ,  Qiuhua Liu ,  Lei Yu ,  Jiangyu Zhao

发明人： Huailin Sun ,  Jian Zhang ,  Qiuhua Liu ,  Lei Yu ,  Jiangyu Zhao

IPC分类号： A61K38/00 ,  C07K5/00 ,  C07K7/00 ,  C07K16/00 ,  C07K17/00 ,  C07K2/00 ,  C07K4/00 ,  C07K14/00 ,  C07K1/08 ,  C07K1/02

CPC分类号： C07K1/088 ,  C07K1/02

摘要： Polypeptides of formula (I): wherein R1 and R2 are substituents independently selected from substituted and un-substituted alkyl groups and substituted and un-substituted aryl groups, and n is an integer greater than or equal to 2. Synthesis methods are also provided that do not use amino acids as starting materials, but instead employ imines and carbon monoxide as monomers that undergo transition metal-catalyzed alternating copolymerization to directly provide polypeptides using an acylcobalt catalyst with the following structural formula: wherein R is selected from the group consisting of alkyl, phenyl, and substituted phenyl groups.

摘要翻译： 式（I）的多肽：其中R 1和R 2是独立地选自取代和未取代的烷基和取代和未取代的芳基的取代基，n是大于或等于2的整数。还提供了合成方法 不要使用氨基酸作为原料，而是使用亚胺和一氧化碳作为进行过渡金属催化的交替共聚的单体，以使用具有以下结构式的酰基钴催化剂直接提供多肽：其中R选自烷基 ，苯基和取代的苯基。

公开(公告)号：US20090264619A1

公开(公告)日：2009-10-22

申请号：US12480675

申请日：2009-06-08

申请人： Huailin SUN ,  Jian Zhang ,  Qiuhua Liu ,  Lei Yu ,  Jiangyu Zhao

发明人： Huailin SUN ,  Jian Zhang ,  Qiuhua Liu ,  Lei Yu ,  Jiangyu Zhao

IPC分类号： C07K1/00 ,  C07K2/00

CPC分类号： C07K1/088 ,  C07K1/02

摘要： Polypeptides of formula (I): wherein R1 and R2 are substituents independently selected from substituted and un-substituted alkyl groups and substituted and un-substituted aryl groups, and n is an integer greater than or equal to 2. Synthesis methods are also provided that do not use amino acids as starting materials, but instead employ imines and carbon monoxide as monomers that undergo transition metal-catalyzed alternating copolymerization to directly provide polypeptides using an acylcobalt catalyst with the following structural formula: wherein R is selected from the group consisting of alkyl, phenyl, and substituted phenyl groups.

摘要翻译： 式（I）的多肽：其中R 1和R 2是独立地选自取代和未取代的烷基和取代和未取代的芳基的取代基，n是大于或等于2的整数。还提供了合成方法 不要使用氨基酸作为原料，而是使用亚胺和一氧化碳作为进行过渡金属催化的交替共聚的单体，以使用具有下列结构式的酰基钴催化剂直接提供多肽：其中R选自烷基 ，苯基和取代的苯基。

公开(公告)号：US09334718B2

公开(公告)日：2016-05-10

申请号：US12652405

申请日：2010-01-05

申请人： Henri-Pierre Valero ,  Sandip Bose ,  Qiuhua Liu ,  Ramachandra Shenoy ,  Abderrhamane Ounadjela

发明人： Henri-Pierre Valero ,  Sandip Bose ,  Qiuhua Liu ,  Ramachandra Shenoy ,  Abderrhamane Ounadjela

IPC分类号： G01V1/40 ,  E21B43/26 ,  G01V1/28

CPC分类号： E21B43/26 ,  G01V1/288 ,  G01V2210/123

摘要： Automatic detection and accurate time picking of weak events embedded in strong noise such as microseismicity induced by hydraulic fracturing is accomplished by: a noise reduction step to separate out the noise and estimate its spectrum; an events detection and confidence indicator step, in which a new statistical test is applied to detect which time windows contain coherent arrivals across components and sensors in the multicomponent array and to indicate the confidence in this detection; and a time-picking step to accurately estimate the time of onset of the arrivals detected above and measure the time delay across the array using a hybrid beamforming method incorporating the use of higher order statistics. In the context of hydraulic fracturing, this could enhance the coverage and mapping of the fractures while also enabling monitoring from the treatment well itself where there is usually much higher and spatially correlated noise.

摘要翻译： 自动检测和精确时间采集嵌入强噪声（如水力压裂引起的微震）的弱事件是通过以下方式实现的：通过降噪步骤分离噪声并估计其频谱; 事件检测和置信指标步骤，其中应用新的统计测试以检测哪个时间窗口包含多组件阵列中的组件和传感器之间的相干到达，并指示该检测的置信度; 以及精确估计上述检测到达时间的时间选择步骤，并且使用结合使用更高阶统计量的混合波束成形方法来测量阵列上的时间延迟。 在水力压裂的背景下，这可以增强裂缝的覆盖和映射，同时还能够从处理井本身进行监测，其中通常有更高的空间相关噪声。

公开(公告)号：US20100228530A1

公开(公告)日：2010-09-09

申请号：US12652405

申请日：2010-01-05

申请人： Henri-Pierre Valero ,  Sandip Bose ,  Qiuhua Liu ,  Ramachandra Shenoy ,  Abderrhamane Ounadjela

发明人： Henri-Pierre Valero ,  Sandip Bose ,  Qiuhua Liu ,  Ramachandra Shenoy ,  Abderrhamane Ounadjela

IPC分类号： G06F17/10

CPC分类号： E21B43/26 ,  G01V1/288 ,  G01V2210/123

摘要： Automatic detection and accurate time picking of weak events embedded in strong noise such as microseismicity induced by hydraulic fracturing is accomplished by: a noise reduction step to separate out the noise and estimate its spectrum; an events detection and confidence indicator step, in which a new statistical test is applied to detect which time windows contain coherent arrivals across components and sensors in the multicomponent array and to indicate the confidence in this detection; and a time-picking step to accurately estimate the time of onset of the arrivals detected above and measure the time delay across the array using a hybrid beamforming method incorporating the use of higher order statistics. In the context of hydraulic fracturing, this could enhance the coverage and mapping of the fractures while also enabling monitoring from the treatment well itself where there is usually much higher and spatially correlated noise.

摘要翻译： 自动检测和精确时间采集嵌入强噪声（如水力压裂引起的微震）的弱事件是通过以下方式实现的：通过降噪步骤分离噪声并估计其频谱; 事件检测和置信指标步骤，其中应用新的统计测试以检测哪个时间窗口包含多组件阵列中的组件和传感器之间的相干到达，并指示该检测的置信度; 以及精确估计上述检测到达时间的时间选择步骤，并且使用结合使用更高阶统计量的混合波束成形方法来测量阵列上的时间延迟。 在水力压裂的背景下，这可以增强裂缝的覆盖和映射，同时还能够从处理井本身进行监测，其中通常有更高的空间相关噪声。

公开(公告)号：US20080288255A1

公开(公告)日：2008-11-20

申请号：US12153370

申请日：2008-05-16

申请人： Lawrence Carin ,  John Paisely ,  Yuting Qi ,  Xuejun Liao ,  Qiuhua Liu

发明人： Lawrence Carin ,  John Paisely ,  Yuting Qi ,  Xuejun Liao ,  Qiuhua Liu

IPC分类号： G10L15/14

CPC分类号： G10L15/142 ,  G06K9/6297

摘要： A method of quantifying similarities between sequential data streams typically includes providing a pair of sequential data streams, designing a Hidden Markov Model (HMM) of at least a portion of each stream; and computing a quantitative measure of similarity between the streams using the HMMs. For a plurality of sequential data streams, a matrix of quantitative measures of similarity may be created. A spectral analysis may be performed on the matrix of quantitative measure of similarity matrix to define a multi-dimensional diffusion space, and the plurality of sequential data streams may be graphically represented and/or sorted according to the similarities therebetween. In addition, semi-supervised and active learning algorithms may be utilized to learn a user's preferences for data streams and recommend additional data streams that are similar to those preferred by the user. Multi-task learning algorithms may also be applied.

摘要翻译： 量化顺序数据流之间的相似性的方法通常包括提供一对顺序数据流，设计每个流的至少一部分的隐马尔可夫模型（HMM）; 并使用HMM计算流之间的相似性的定量测量。 对于多个顺序数据流，可以创建相似度的定量测量矩阵。 可以对相似矩阵的定量测量矩阵执行频谱分析以定义多维扩散空间，并且可以根据它们之间的相似性图形地表示和/或分类多个顺序数据流。 此外，可以利用半监督和主动学习算法来学习用户对数据流的偏好并推荐与用户优选的附加数据流相似的附加数据流。 也可以应用多任务学习算法。