公开(公告)号：US09026373B2

公开(公告)日：2015-05-05

申请号：US13371469

申请日：2012-02-12

申请人： Charles C. Zhou ,  Ying Zhao

发明人： Charles C. Zhou ,  Ying Zhao

IPC分类号： G06F19/00 ,  G06F19/24 ,  G06F19/18

CPC分类号： G06F19/24 ,  G06F19/18

摘要： Methods and systems for knowledge pattern search and analysis for selecting microorganisms based on desired metabolic properties or biological behaviors are disclosed in various embodiments of the invention. In one embodiment of the invention, a computer-implemented method for selecting a purpose-specific microorganism first compiles microorganisms' profiles by linking each microorganism's methanogenic, hydrogenic, electrogenic, another metabolic property, and/or another biological behavior to genetic and chemical fingerprints of metabolic and energy-generating biological pathways. Then, based on the compiled profiles of the microorganisms, the computer-implemented method groups the microorganisms into pathway characteristics using machine-learning and pattern recognition performed on a computer system, and subsequently generates a prediction called “discovered characteristics” for a desired metabolic property or a desired biological behavior of at least one microorganism. Furthermore, a profile match score may be calculated to indicate usefulness of one or more microorganisms for renewable energy generation from biological waste materials or wastewater.

摘要翻译： 在本发明的各种实施例中公开了用于基于期望的代谢性质或生物学行为选择微生物的知识模式搜索和分析的方法和系统。 在本发明的一个实施方案中，用于选择目的特异性微生物的计算机实现的方法首先通过将每个微生物的产甲烷，氢，电，另一代谢性质和/或其他生物学行为与遗传和化学指纹 代谢和能量生成途径。 然后，基于微生物的编制概况，计算机实现的方法使用在计算机系统上执行的机器学习和模式识别将微生物组合成路径特征，并且随后产生称为“发现特征”的预期以获得期望的代谢性质 或至少一种微生物的所需生物学行为。 此外，可以计算轮廓匹配分数，以指示生物废物或废水中一种或多种微生物用于可再生能源产生的有用性。

公开(公告)号：US20100010172A1

公开(公告)日：2010-01-14

申请号：US12302175

申请日：2007-05-11

申请人： Sheng Hong ,  Xianfeng Shen ,  Charles C. Zhou ,  Claude C. Granel

发明人： Sheng Hong ,  Xianfeng Shen ,  Charles C. Zhou ,  Claude C. Granel

IPC分类号： C08L53/00 ,  C08L53/02

CPC分类号： C08L69/00 ,  C08F293/005 ,  C08F2438/02 ,  C08L9/00 ,  C08L25/04 ,  C08L53/00 ,  C08L53/02 ,  C08L55/02 ,  C08L101/00 ,  C08L2666/24

摘要： The present invention relates to a toughened transparent thermoplastic composite of a transparent thermoplastic and a block copolymer having a block of a random copolymer and an elastomeric block. One preferred embodiment is a polycarbonate that is modified with a block copolymer having a methyl methacrylate (MMA) and naphthyl methacrylate or a substituted naphthyl methacrylate block and an elastomeric block. This block copolymer has excellent miscibility with polycarbonate resin, even at elevated temperature, producing transparent polycarbonate blends. The blend can provide a toughened strength polycarbonate while maintaining its excellent optical properties.

摘要翻译： 本发明涉及具有无规共聚物和弹性体嵌段的嵌段的透明热塑性塑料和嵌段共聚物的增韧透明热塑复合材料。 一个优选的实施方案是用具有甲基丙烯酸甲酯（MMA）和甲基丙烯酸萘酯或取代的甲基丙烯酸萘酯嵌段和弹性体嵌段的嵌段共聚物改性的聚碳酸酯。 该嵌段共聚物即使在升高的温度下也与聚碳酸酯树脂具有优异的混溶性，生产出透明的聚碳酸酯共混物。 该混合物可以提供增韧的强度聚碳酸酯，同时保持其优异的光学性能。

公开(公告)号：US09323837B2

公开(公告)日：2016-04-26

申请号：US13204713

申请日：2011-08-07

申请人： Ying Zhao ,  Charles C. Zhou ,  Chetan Kotak

发明人： Ying Zhao ,  Charles C. Zhou ,  Chetan Kotak

IPC分类号： G06F17/30

CPC分类号： G06F17/30702

摘要： The present invention discloses various embodiments of multiple domain anomaly detection systems and methods. In one embodiment of the invention, a multiple domain anomaly detection system uses a generic learning procedure per domain to create a “normal data profile” for each domain based on observation of data per domain, wherein the normal data profile for each domain can be used to determine and compute domain-specific anomaly data per domain. Then, domain-specific anomaly data per domain can be analyzed together in a cross-domain fusion data analysis using one or more fusion rules. The fusion rules may involve comparison of domain-specific anomaly data from multiple domains to derive a multiple-domain anomaly score meter for a particular cross-domain analysis task. The multiple domain anomaly detection system and its related method may also utilize domain-specific anomaly indicators of each domain to derive a cross-domain anomaly indicator using the fusion rules.

摘要翻译： 本发明公开了多域异常检测系统和方法的各种实施例。 在本发明的一个实施例中，多域异常检测系统使用每个域的通用学习过程，基于每个域的数据观察为每个域创建“正常数据简档”，其中可以使用每个域的正常数据简档 确定和计算每个域的特定于异常的数据。 然后，可以在使用一个或多个融合规则的跨域融合数据分析中一起分析每个域的域特异性异常数据。 融合规则可能涉及到来自多个域的域特异性异常数据的比较，以导出用于特定跨域分析任务的多域异常得分计。 多域异常检测系统及其相关方法还可以利用每个域的域特异性异常指标，使用融合规则导出跨域异常指标。

公开(公告)号：US20120041901A1

公开(公告)日：2012-02-16

申请号：US13283442

申请日：2011-10-27

申请人： Ying Zhao ,  Charles C. Zhou

发明人： Ying Zhao ,  Charles C. Zhou

IPC分类号： G06F15/18

CPC分类号： G06N5/043

摘要： One or more systems and methods for knowledge pattern search from networked agents are disclosed in various embodiments of the invention. A system and a related method can utilizes a knowledge pattern discovery process, which involves analyzing historical data, contextualizing, conceptualizing, clustering, and modeling of data to pattern and discover information of interest. This process may involve constructing a pattern-identifying model using a computer system by applying a context-concept-cluster (CCC) data analysis method, and visualizing that information using a computer system interface. In one embodiment of the invention, once the pattern-identifying model is constructed, the real-time data can be gathered using multiple learning agent devices, and then analyzed by the pattern-identifying model to identify various patterns for gains analysis and derivation of an anomalousness score. This system can be useful for knowledge discovery applications in various industries, including business, competitive intelligence, and academic research.

摘要翻译： 在本发明的各种实施例中公开了一种或多种用于来自联网代理的知识模式搜索的系统和方法。 系统和相关方法可以利用知识模式发现过程，其涉及分析历史数据，语境化，概念化，聚类和数据建模以模拟和发现感兴趣的信息。 该过程可以涉及使用计算机系统通过应用上下文概念集群（CCC）数据分析方法来构建模式识别模型，并且使用计算机系统接口来可视化该信息。 在本发明的一个实施例中，一旦构建了模式识别模型，就可以使用多个学习代理设备来收集实时数据，然后通过模式识别模型进行分析，以识别用于增益分析和导出的各种模式 异常评分 该系统可用于各行业的知识发现应用，包括业务，竞争情报和学术研究。

公开(公告)号：US20110295783A1

公开(公告)日：2011-12-01

申请号：US13204713

申请日：2011-08-07

申请人： Ying ZHAO ,  Charles C. ZHOU ,  Chetan KOTAK

发明人： Ying ZHAO ,  Charles C. ZHOU ,  Chetan KOTAK

IPC分类号： G06F15/18

CPC分类号： G06F17/30702

摘要： The present invention discloses various embodiments of multiple domain anomaly detection systems and methods. In one embodiment of the invention, a multiple domain anomaly detection system uses a generic learning procedure per domain to create a “normal data profile” for each domain based on observation of data per domain, wherein the normal data profile for each domain can be used to determine and compute domain-specific anomaly data per domain. Then, domain-specific anomaly data per domain can be analyzed together in a cross-domain fusion data analysis using one or more fusion rules. The fusion rules may involve comparison of domain-specific anomaly data from multiple domains to derive a multiple-domain anomaly score meter for a particular cross-domain analysis task. The multiple domain anomaly detection system and its related method may also utilize domain-specific anomaly indicators of each domain to derive a cross-domain anomaly indicator using the fusion rules.

摘要翻译： 本发明公开了多域异常检测系统和方法的各种实施例。 在本发明的一个实施例中，多域异常检测系统使用每个域的通用学习过程，基于每个域的数据观察为每个域创建“正常数据简档”，其中可以使用每个域的正常数据简档 确定和计算每个域的特定于异常的数据。 然后，可以在使用一个或多个融合规则的跨域融合数据分析中一起分析每个域的域特异性异常数据。 融合规则可能涉及到来自多个域的域特异性异常数据的比较，以导出用于特定跨域分析任务的多域异常得分计。 多域异常检测系统及其相关方法还可以利用每个域的域特异性异常指标，使用融合规则导出跨域异常指标。

公开(公告)号：US09792404B2

公开(公告)日：2017-10-17

申请号：US14093520

申请日：2013-12-02

申请人： Charles C Zhou ,  Ying Zhao

发明人： Charles C Zhou ,  Ying Zhao

IPC分类号： G06F19/10 ,  G06F19/24 ,  G06F19/18 ,  G06F19/12

CPC分类号： G06F19/24 ,  G06F19/12 ,  G06F19/18

摘要： Methods and systems for knowledge pattern search and analysis for selecting microorganisms based on desired metabolic properties or biological behaviors are disclosed in various embodiments of the invention. In one embodiment of the invention, a computer-implemented method for selecting a purpose-specific microorganism first compiles microorganisms' profiles by linking each microorganism's methanogenic, hydrogenic, electrogenic, another metabolic property, and/or another biological behavior to genetic and chemical fingerprints of metabolic and energy-generating biological pathways. Then, based on the compiled profiles of the microorganisms, the computer-implemented method groups the microorganisms into pathway characteristics using machine-learning and pattern recognition performed on a computer system, and subsequently generates a prediction called “discovered characteristics” for a desired metabolic property or a desired biological behavior of at least one microorganism. Furthermore, a profile match score may be calculated to indicate usefulness of one or more microorganisms for renewable energy generation from biological waste materials or wastewater.

公开(公告)号：US20140088883A1

公开(公告)日：2014-03-27

申请号：US14093520

申请日：2013-12-02

申请人： Charles C. ZHOU ,  Ying ZHAO

发明人： Charles C. ZHOU ,  Ying ZHAO

IPC分类号： G06F19/24

CPC分类号： G06F19/24 ,  G06F19/12 ,  G06F19/18

摘要： Methods and systems for knowledge pattern search and analysis for selecting microorganisms based on desired metabolic properties or biological behaviors are disclosed in various embodiments of the invention. In one embodiment of the invention, a computer-implemented method for selecting a purpose-specific microorganism first compiles microorganisms' profiles by linking each microorganism's methanogenic, hydrogenic, electrogenic, another metabolic property, and/or another biological behavior to genetic and chemical fingerprints of metabolic and energy-generating biological pathways. Then, based on the compiled profiles of the microorganisms, the computer-implemented method groups the microorganisms into pathway characteristics using machine-learning and pattern recognition performed on a computer system, and subsequently generates a prediction called “discovered characteristics” for a desired metabolic property or a desired biological behavior of at least one microorganism. Furthermore, a profile match score may be calculated to indicate usefulness of one or more microorganisms for renewable energy generation from biological waste materials or wastewater.

摘要翻译： 在本发明的各种实施例中公开了用于基于期望的代谢性质或生物学行为选择微生物的知识模式搜索和分析的方法和系统。 在本发明的一个实施方案中，用于选择目的特异性微生物的计算机实现的方法首先通过将每个微生物的产甲烷，氢，电，另一代谢性质和/或其他生物学行为与遗传和化学指纹 代谢和能量生成途径。 然后，基于微生物的编制概况，计算机实现的方法使用在计算机系统上执行的机器学习和模式识别将微生物组合成路径特征，并且随后产生称为“发现特征”的预期以获得期望的代谢性质 或至少一种微生物的所需生物学行为。 此外，可以计算轮廓匹配分数，以指示生物废物或废水中一种或多种微生物用于可再生能源产生的有用性。

公开(公告)号：US20120143800A1

公开(公告)日：2012-06-07

申请号：US13371469

申请日：2012-02-12

申请人： Charles C. ZHOU ,  Ying ZHAO

发明人： Charles C. ZHOU ,  Ying ZHAO

IPC分类号： G06F15/18

CPC分类号： G06F19/24 ,  G06F19/18

摘要： Methods and systems for knowledge pattern search and analysis for selecting microorganisms based on desired metabolic properties or biological behaviors are disclosed in various embodiments of the invention. In one embodiment of the invention, a computer-implemented method for selecting a purpose-specific microorganism first compiles microorganisms' profiles by linking each microorganism's methanogenic, hydrogenic, electrogenic, another metabolic property, and/or another biological behavior to genetic and chemical fingerprints of metabolic and energy-generating biological pathways. Then, based on the compiled profiles of the microorganisms, the computer-implemented method groups the microorganisms into pathway characteristics using machine-learning and pattern recognition performed on a computer system, and subsequently generates a prediction called “discovered characteristics” for a desired metabolic property or a desired biological behavior of at least one microorganism. Furthermore, a profile match score may be calculated to indicate usefulness of one or more microorganisms for renewable energy generation from biological waste materials or wastewater.

摘要翻译： 在本发明的各种实施例中公开了用于基于期望的代谢性质或生物学行为选择微生物的知识模式搜索和分析的方法和系统。 在本发明的一个实施方案中，用于选择目的特异性微生物的计算机实现的方法首先通过将每个微生物的产甲烷，氢，电，另一代谢性质和/或其他生物学行为与遗传和化学指纹 代谢和能量生成途径。 然后，基于微生物的编制概况，计算机实现的方法使用在计算机系统上执行的机器学习和模式识别将微生物组合成路径特征，并且随后产生称为“发现特征”的预期以获得期望的代谢性质 或至少一种微生物的所需生物学行为。 此外，可以计算轮廓匹配分数，以指示生物废物或废水中一种或多种微生物用于可再生能源产生的有用性。

公开(公告)号：US20090142537A1

公开(公告)日：2009-06-04

申请号：US12302163

申请日：2007-05-11

申请人： Sheng Hong ,  Charles C. Zhou ,  Xianfeng Shen

发明人： Sheng Hong ,  Charles C. Zhou ,  Xianfeng Shen

IPC分类号： C08L69/00 ,  G11B7/245

CPC分类号： C08L33/12 ,  C08L33/06 ,  C08L33/10 ,  C08L69/00 ,  C08L2666/18 ,  C08L2666/04

摘要： The invention relates to a transparent thermoplastic blend of polycarbonate (PC) and a copolymer of methyl methacrylate (MMA) and naphthyl methacrylate or a substituted naphthyl methacrylate. This copolymer has excellent miscibility with polycarbonate resin, even at elevated temperature, producing transparent polycarbonate blends. The blend provides an improved scratch resistance of polycarbonate while maintaining its excellent optical properties.

摘要翻译： 本发明涉及聚碳酸酯（PC）和甲基丙烯酸甲酯（MMA）和甲基丙烯酸萘酯或取代的甲基丙烯酸萘酯的共聚物的透明热塑性共混物。 该共聚物即使在升高的温度下也与聚碳酸酯树脂具有优异的混溶性，生产透明的聚碳酸酯共混物。 该混合物提供了改善聚碳酸酯的耐刮擦性，同时保持了其优异的光学性能。

公开(公告)号：US08903756B2

公开(公告)日：2014-12-02

申请号：US13283442

申请日：2011-10-27

申请人： Ying Zhao ,  Charles C. Zhou

发明人： Ying Zhao ,  Charles C. Zhou

IPC分类号： G06F17/00 ,  G06N5/04

CPC分类号： G06N5/043

摘要： One or more systems and methods for knowledge pattern search from networked agents are disclosed in various embodiments of the invention. A system and a related method can utilizes a knowledge pattern discovery process, which involves analyzing historical data, contextualizing, conceptualizing, clustering, and modeling of data to pattern and discover information of interest. This process may involve constructing a pattern-identifying model using a computer system by applying a context-concept-cluster (CCC) data analysis method, and visualizing that information using a computer system interface. In one embodiment of the invention, once the pattern-identifying model is constructed, the real-time data can be gathered using multiple learning agent devices, and then analyzed by the pattern-identifying model to identify various patterns for gains analysis and derivation of an anomalousness score. This system can be useful for knowledge discovery applications in various industries, including business, competitive intelligence, and academic research.

摘要翻译： 在本发明的各种实施例中公开了一种或多种用于来自联网代理的知识模式搜索的系统和方法。 系统和相关方法可以利用知识模式发现过程，其涉及分析历史数据，语境化，概念化，聚类和数据建模以模拟和发现感兴趣的信息。 该过程可以涉及使用计算机系统通过应用上下文概念集群（CCC）数据分析方法来构建模式识别模型，并且使用计算机系统接口来可视化该信息。 在本发明的一个实施例中，一旦构建了模式识别模型，就可以使用多个学习代理设备来收集实时数据，然后通过模式识别模型进行分析，以识别用于增益分析和导出的各种模式 异常评分 该系统可用于各行业的知识发现应用，包括业务，竞争情报和学术研究。