公开(公告)号：US07937334B2

公开(公告)日：2011-05-03

申请号：US11755924

申请日：2007-05-31

申请人： Piero Patrone Bonissone ,  Weizhong Yan ,  Naresh Sundaram Iyer ,  Kai Goebel ,  Anil Varma

发明人： Piero Patrone Bonissone ,  Weizhong Yan ,  Naresh Sundaram Iyer ,  Kai Goebel ,  Anil Varma

IPC分类号： G06F17/00

CPC分类号： G05B23/024 ,  G06K9/6284 ,  G06N99/005

摘要： Monitoring dynamic units that operate in complex, dynamic environments, is provided in order to classify and track unit behavior over time. When domain knowledge is available, feature-based models may be used to capture the essential state information of the units. When domain knowledge is not available, raw data is relied upon to perform this task. By analyzing logs of event messages (without having access to their data dictionary), embodiments allow the identification of anomalies (novelties). Specifically, a Normalized Compression Distance (such as one based on Kolmogorov Complexity) may be applied to logs of event messages. By analyzing the similarity and differences of the event message logs, units are identified that did not experience any abnormality (and locate regions of normal operations) and units that departed from such regions.

摘要翻译： 提供了监控在复杂，动态环境中运行的动态单元，以便对时间段内的单元行为进行分类和跟踪。 当领域知识可用时，可以使用基于特征的模型来捕获单位的基本状态信息。 当领域知识不可用时，依靠原始数据来执行此任务。 通过分析事件消息的日志（不访问其数据字典），实施例允许识别异常（新奇事物）。 具体来说，归一化压缩距离（例如基于Kolmogorov复杂度的距离）可以应用于事件消息的日志。 通过分析事件消息日志的相似性和差异，识别出没有经历任何异常（并定位正常操作的区域）的单位和离开这些区域的单位。

公开(公告)号：US07383239B2

公开(公告)日：2008-06-03

申请号：US10425721

申请日：2003-04-30

申请人： Piero Patrone Bonissone ,  Kareem Sherif Aggour ,  Rajesh Venkat Subbu ,  Weizhong Yan ,  Naresh Sundaram Iyer ,  Anindya Chakraborty

发明人： Piero Patrone Bonissone ,  Kareem Sherif Aggour ,  Rajesh Venkat Subbu ,  Weizhong Yan ,  Naresh Sundaram Iyer ,  Anindya Chakraborty

IPC分类号： G06F17/00 ,  G06N5/02

CPC分类号： G06Q40/08 ,  G06Q40/00

摘要： A method and system for fusing a collection of classifiers used for an automated insurance underwriting system and/or its quality assurance is described. Specifically, the outputs of a collection of classifiers are fused. The fusion of the data will typically result in some amount of consensus and some amount of conflict among the classifiers. The consensus will be measured and used to estimate a degree of confidence in the fused decisions. Based on the decision and degree of confidence of the fusion and the decision and degree of confidence of the production decision engine, a comparison module may then be used to identify cases for audit, cases for augmenting the training/test sets for re-tuning production decision engine, cases for review, or may simply trigger a record of its occurrence for tracking purposes. The fusion can compensate for the potential correlation among the classifiers. The reliability of each classifier can be represented by a static or dynamic discounting factor, which will reflect the expected accuracy of the classifier. A static discounting factor is used to represent a prior expectation about the classifier's reliability, e.g., it might be based on the average past accuracy of the model, while a dynamic discounting is used to represent a conditional assessment of the classifier's reliability, e.g., whenever a classifier bases its output on an insufficient number of points it is not reliable.

摘要翻译： 描述用于融合用于自动保险承保系统的分类器集合和/或其质量保证的方法和系统。 具体来说，分类器的集合的输出被融合。 数据的融合通常会导致一些共识和分类器之间的一些冲突。 共识将被测量并用于估计融合决策的信心程度。 根据融合的决定和信心程度以及生产​​决策引擎的决策和决策程度，然后可以使用比较模块来识别审计案例，增加用于重新调整生产的培训/测试集的案例 决策引擎，审查案例，或者可以简单地触发其发生记录以进行跟踪。 融合可以补偿分类器之间的潜在相关性。 每个分类器的可靠性可以由静态或动态折扣因子表示，这将反映分类器的预期准确性。 静态折扣因子用于表示对分类器的可靠性的先前期望，例如，可以基于模型的平均过去精度，而使用动态贴现来表示分类器的可靠性的条件评估，例如，每当 分类器的输出基于不可靠的点数不足。

公开(公告)号：US07317994B2

公开(公告)日：2008-01-08

申请号：US11200156

申请日：2005-08-10

申请人： Naresh Sundaram Iyer ,  John Erik Hershey ,  James Kenneth Aragones ,  Kai Frank Goebel ,  Weizhong Yan ,  Piero Patrone Bonissone ,  Charles Terrance Hatch

发明人： Naresh Sundaram Iyer ,  John Erik Hershey ,  James Kenneth Aragones ,  Kai Frank Goebel ,  Weizhong Yan ,  Piero Patrone Bonissone ,  Charles Terrance Hatch

IPC分类号： G01H9/00

CPC分类号： G01M15/12

摘要： A method for analyzing vibration including: acquiring a vibration signal; isolating a vibration signal event in the acquired signal; determining a frequency of a damped sinusoid of the vibration signal event, wherein the damped sinusoid characterizes the vibration signal event, and using the characteristic damped sinusoid to identify an occurrence of the vibration signal event in another vibration signal.

摘要翻译： 一种用于分析振动的方法，包括：获取振动信号; 隔离所获取的信号中的振动信号事件; 确定所述振动信号事件的阻尼正弦曲线的频率，其中所述阻尼正弦曲线表征所述振动信号事件，并且使用所述特征阻尼正弦曲线来识别所述振动信号事件在另一振动信号中的出现。

公开(公告)号：US20090048876A1

公开(公告)日：2009-02-19

申请号：US12131545

申请日：2008-06-02

申请人： Piero Patrone Bonissone ,  Kareem Sherif AGGOUR ,  Rajesh Venkat SUBBU ,  Weizhong YAN ,  Naresh Sundaram IYER ,  Anindya CHAKRABORTY

发明人： Piero Patrone Bonissone ,  Kareem Sherif AGGOUR ,  Rajesh Venkat SUBBU ,  Weizhong YAN ,  Naresh Sundaram IYER ,  Anindya CHAKRABORTY

IPC分类号： G06Q40/00 ,  G06Q10/00

CPC分类号： G06Q40/08 ,  G06Q40/00

摘要： A method and system for fusing a collection of classifiers used for an automated insurance underwriting system and/or its quality assurance is described. Specifically, the outputs of a collection of classifiers are fused. The fusion of the data will typically result in some amount of consensus and some amount of conflict among the classifiers. The consensus will be measured and used to estimate a degree of confidence in the fused decisions. Based on the decision and degree of confidence of the fusion and the decision and degree of confidence of the production decision engine, a comparison module may then be used to identify cases for audit, cases for augmenting the training/test sets for re-tuning production decision engine, cases for review, or may simply trigger a record of its occurrence for tracking purposes. The fusion can compensate for the potential correlation among the classifiers. The reliability of each classifier can be represented by a static or dynamic discounting factor, which will reflect the expected accuracy of the classifier. A static discounting factor is used to represent a prior expectation about the classifier's reliability, e.g., it might be based on the average past accuracy of the model, while a dynamic discounting is used to represent a conditional assessment of the classifier's reliability, e.g., whenever a classifier bases its output on an insufficient number of points it is not reliable.

摘要翻译： 描述用于融合用于自动保险承保系统的分类器集合和/或其质量保证的方法和系统。 具体来说，分类器的集合的输出被融合。 数据的融合通常会导致一些共识和分类器之间的一些冲突。 共识将被测量并用于估计融合决策的信心程度。 根据融合的决定和信心程度以及生产​​决策引擎的决策和决策程度，然后可以使用比较模块来识别审计案例，增加用于重新调整生产的培训/测试集的案例 决策引擎，审查案例，或者可以简单地触发其发生记录以进行跟踪。 融合可以补偿分类器之间的潜在相关性。 每个分类器的可靠性可以由静态或动态折扣因子表示，这将反映分类器的预期准确性。 静态折扣因子用于表示对分类器的可靠性的先前期望，例如，可以基于模型的平均过去精度，而使用动态贴现来表示分类器的可靠性的条件评估，例如，每当 分类器的输出基于不可靠的点数不足。

公开(公告)号：US08214314B2

公开(公告)日：2012-07-03

申请号：US12131545

申请日：2008-06-02

申请人： Piero Patrone Bonissone ,  Kareem Sherif Aggour ,  Rajesh Venkat Subbu ,  Weizhong Yan ,  Naresh Sundaram Iyer ,  Anindya Chakraborty

发明人： Piero Patrone Bonissone ,  Kareem Sherif Aggour ,  Rajesh Venkat Subbu ,  Weizhong Yan ,  Naresh Sundaram Iyer ,  Anindya Chakraborty

IPC分类号： G06F17/00 ,  G06N5/02

CPC分类号： G06Q40/08 ,  G06Q40/00

摘要： A method and system for fusing a collection of classifiers used for an automated insurance underwriting system and/or its quality assurance is described. Specifically, the outputs of a collection of classifiers are fused. The fusion of the data will typically result in some amount of consensus and some amount of conflict among the classifiers. The consensus will be measured and used to estimate a degree of confidence in the fused decisions. Based on the decision and degree of confidence of the fusion and the decision and degree of confidence of the production decision engine, a comparison module may then be used to identify cases for audit, cases for augmenting the training/test sets for re-tuning production decision engine, cases for review, or may simply trigger a record of its occurrence for tracking purposes. The fusion can compensate for the potential correlation among the classifiers. The reliability of each classifier can be represented by a static or dynamic discounting factor, which will reflect the expected accuracy of the classifier. A static discounting factor is used to represent a prior expectation about the classifier's reliability, e.g., it might be based on the average past accuracy of the model, while a dynamic discounting is used to represent a conditional assessment of the classifier's reliability, e.g., whenever a classifier bases its output on an insufficient number of points it is not reliable.

摘要翻译： 描述用于融合用于自动保险承保系统的分类器集合和/或其质量保证的方法和系统。 具体来说，分类器的集合的输出被融合。 数据的融合通常会导致一些共识和分类器之间的一些冲突。 共识将被测量并用于估计融合决策的信心程度。 根据融合的决定和信心程度以及生产​​决策引擎的决策和决策程度，然后可以使用比较模块来识别审计案例，增加用于重新调整生产的培训/测试集的案例 决策引擎，审查案例，或者可以简单地触发其发生记录以进行跟踪。 融合可以补偿分类器之间的潜在相关性。 每个分类器的可靠性可以由静态或动态折扣因子表示，这将反映分类器的预期准确性。 静态折扣因子用于表示对分类器的可靠性的先前期望，例如，可以基于模型的平均过去精度，而使用动态贴现来表示分类器的可靠性的条件评估，例如，每当 分类器的输出基于不可靠的点数不足。

公开(公告)号：US07725293B2

公开(公告)日：2010-05-25

申请号：US11608058

申请日：2006-12-07

申请人： Piero Patrone Bonissone ,  Feng Xue ,  Anil Varma ,  Kai Frank Goebel ,  Weizhong Yan ,  Neil Holger White Eklund

发明人： Piero Patrone Bonissone ,  Feng Xue ,  Anil Varma ,  Kai Frank Goebel ,  Weizhong Yan ,  Neil Holger White Eklund

IPC分类号： G06F3/00

CPC分类号： G06N5/04

摘要： A method to predict remaining life of a target is disclosed. The method includes receiving information regarding a behavior of the target, and identifying from a database at least one piece of equipment having similarities to the target. The method further includes retrieving from the database data prior to an end of the equipment useful life, the data having a relationship to the behavior, evaluating a similarity of the relationship, predicting the remaining life of the target based upon the similarity, and generating a signal corresponding to the predicted remaining equipment life.

摘要翻译： 公开了一种预测目标的剩余寿命的方法。 该方法包括接收关于目标的行为的信息，以及从数据库中识别具有与目标相似的至少一件设备。 该方法还包括在设备使用寿命结束之前从数据库中获取数据，数据与行为有关系，评估关系的相似性，基于相似度预测目标的剩余寿命，并产生 信号对应于预计的剩余设备寿命。

公开(公告)号：US07958062B2

公开(公告)日：2011-06-07

申请号：US11755898

申请日：2007-05-31

申请人： Weizhong Yan ,  Anil Varma ,  Piero Patrone Bonissone

发明人： Weizhong Yan ,  Anil Varma ,  Piero Patrone Bonissone

IPC分类号： G06N5/00

CPC分类号： G06F11/2263

摘要： A method and system for creating healthy operating envelope from only data samples obtained during normal operation/behavior of dynamic systems is provided. This method determines healthy operating envelope by clustering a stream of discrete event code sequences from the underlying system under normal operation condition only. The method is unsupervised, that is, requiring no prior knowledge of event code patterns corresponding to different operation conditions. Such created envelope can be used for fault detection and health monitoring of dynamic systems.

摘要翻译： 提供了一种从动态系统的正常操作/行为中获得的数据样本创建健康操作包络的方法和系统。 该方法通过在正常操作条件下聚类来自底层系统的离散事件代码序列流来确定健康操作包络。 该方法是无监督的，也就是说，不需要事先知道与不同操作条件对应的事件代码模式。 这种创建的信封可用于动态系统的故障检测和健康监测。

公开(公告)号：US20080140361A1

公开(公告)日：2008-06-12

申请号：US11608058

申请日：2006-12-07

申请人： Piero Patrone Bonissone ,  Feng Xue ,  Anil Varma ,  Kai Frank Goebel ,  Weizhong Yan ,  Neil Holger White Eklund

发明人： Piero Patrone Bonissone ,  Feng Xue ,  Anil Varma ,  Kai Frank Goebel ,  Weizhong Yan ,  Neil Holger White Eklund

IPC分类号： G06F17/10

CPC分类号： G06N5/04

摘要： A method to predict remaining life of a target is disclosed. The method includes receiving information regarding a behavior of the target, and identifying from a database at least one piece of equipment having similarities to the target. The method further includes retrieving from the database data prior to an end of the equipment useful life, the data having a relationship to the behavior, evaluating a similarity of the relationship, predicting the remaining life of the target based upon the similarity, and generating a signal corresponding to the predicted remaining equipment life.

摘要翻译： 公开了一种预测目标的剩余寿命的方法。 该方法包括接收关于目标的行为的信息，以及从数据库中识别具有与目标相似的至少一件设备。 该方法还包括在设备使用寿命结束之前从数据库中获取数据，数据与行为有关系，评估关系的相似性，基于相似度预测目标的剩余寿命，并且生成 信号对应于预计的剩余设备寿命。

公开(公告)号：US08219477B2

公开(公告)日：2012-07-10

申请号：US10781805

申请日：2004-02-20

申请人： Rajesh Venkat Subbu ,  Srinivas Bollapragada ,  Piero Patrone Bonissone ,  Kete Charles Chalermkraivuth ,  Neil Holger White Eklund ,  Naresh Sundaram Iyer

发明人： Rajesh Venkat Subbu ,  Srinivas Bollapragada ,  Piero Patrone Bonissone ,  Kete Charles Chalermkraivuth ,  Neil Holger White Eklund ,  Naresh Sundaram Iyer

IPC分类号： G06Q40/00

CPC分类号： G06Q40/06

摘要： The systems and methods of the invention are directed to portfolio optimization and related techniques. For example, the invention provides a method for multi-objective portfolio optimization for use in investment decisions based on competing objectives and a plurality of constraints constituting a portfolio problem, the method comprising: generating an initial population of solutions of portfolio allocations; committing the initial population of solutions to an initial population archive; performing a multi-objective process, based on the initial population archive and on multiple competing objectives, to generate an efficient frontier, the multi-objective process including a evolutionary algorithm process, the evolutionary algorithm process utilizing a dominance filter, the efficient frontier being used in investment decisioning.

摘要翻译： 本发明的系统和方法针对组合优化和相关技术。 例如，本发明提供了一种用于基于竞争目标和构成组合问题的多个约束的投资决策中的多目标投资组合优化的方法，所述方法包括：生成投资组合分配的最初解决方案群体; 将最初的人口解决方案提交给初始人口档案; 基于初始人口档案和多个竞争目标，进行多目标过程，以产生有效的前沿，多目标过程包括进化算法过程，利用优势过滤器的进化算法过程，使用的有效边界 投资决策。

公开(公告)号：US07567914B2

公开(公告)日：2009-07-28

申请号：US10425723

申请日：2003-04-30

申请人： Piero Patrone Bonissone ,  Naresh Sundaram Iyer

发明人： Piero Patrone Bonissone ,  Naresh Sundaram Iyer

IPC分类号： G06Q40/00

CPC分类号： G06Q10/10 ,  G06F19/00 ,  G06Q40/02 ,  G06Q40/08 ,  G06Q50/22 ,  G06Q50/24

摘要： A risk classification technique that exploits the existing risk structure of the decision problem in order to produce risk categorizations for new candidates is described. The technique makes use of a set of candidates for which risk categories have already been assigned (in the case of insurance underwriting, for example, this would pertain to the premium class assigned to an application). Using this set of labeled candidates, the technique produces two subsets for each risk category: the Pareto-best subset and the Pareto-worst subset by using Dominance. These two subsets can be seen as representing the least risky and the most risky candidates within a given risk category. If there are a sufficient number of candidates in these two subsets, then the candidates in these two subsets can be seen as samples from the two hypothetical risk surfaces in the feature space that bound the risk category from above and below respectively. A new candidate is assigned a risk category by verifying if the candidate lies within these two bounding risk surfaces.

摘要翻译： 描述了利用现有风险决策问题的风险分类技术，以便为新候选人提供风险分类。 该技术利用已经分配了风险类别的一组候选人（在保险承保的情况下，例如，这将涉及分配给应用程序的溢价级别）。 使用这组标记候选者，该技术为每个风险类别产生两个子集：通过使用Dominance，帕累托最佳子集和帕累托最差子集。 这两个子集可以被视为代表风险类别中风险最低，风险最高的候选人。 如果这两个子集中有足够数量的候选人，那么这两个子集中的候选人可以被看作是从特征空间的两个假设风险面的样本，分别从上下分别界定风险类别。 通过验证候选人是否位于这两个边界风险表面内，为新的候选人分配了风险类别。