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1. 发明申请

US20120083933A1 METHOD AND SYSTEM TO PREDICT POWER PLANT PERFORMANCE 审中-公开
标题翻译：预测发电厂性能的方法和系统
公开(公告)号：US20120083933A1
公开(公告)日：2012-04-05
申请号：US12895293
申请日：2010-09-30
申请人： Rajesh Venkat Subbu , Lincoln Mamoru Fujita , Weizhong Yan , Noemie Dion Ouellet , Richard J. Mitchell , Piero Patrone Bonissone , Robert Frank Hoskin
发明人： Rajesh Venkat Subbu , Lincoln Mamoru Fujita , Weizhong Yan , Noemie Dion Ouellet , Richard J. Mitchell , Piero Patrone Bonissone , Robert Frank Hoskin
IPC分类号： G06F1/26 , G06N3/02 , G06N3/08
CPC分类号： G06N3/02
摘要： The present disclosure relates to the use of hybrid predictive models to predict one or more of performance, availability, or degradation of a power plant or a component of the power plant. The hybrid predictive model comprises at least two model components, one based on a physics-based modeling approach and one based on an observational or data-based modeling approach. The hybrid predictive model may self-tune or self-correct as operational performance varies over time.
摘要翻译：本公开涉及使用混合预测模型来预测发电厂或发电厂的部件的性能，可用性或劣化的一个或多个。混合预测模型包括至少两个模型组件，一个基于基于物理的建模方法，另一个基于基于观测或基于数据的建模方法。混合预测模型可以自我调整或自我纠正，因为随着时间的推移，操作性能也随之变化。

2. 发明授权

US07383239B2 System and process for a fusion classification for insurance underwriting suitable for use by an automated system 有权
标题翻译：用于融合分类的系统和过程，适用于自动化系统使用的保险承保
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。

3. 发明申请

US20090048876A1 SYSTEM AND PROCESS FOR A FUSION CLASSIFICATION FOR INSURANCE UNDERWRITING SUITABLE FOR USE BY AN AUTOMATED SYSTEM 有权
标题翻译：用于保险分类的系统和程序，适用于自动系统使用的保险
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。

4. 发明授权

US07813945B2 System and process for multivariate adaptive regression splines classification for insurance underwriting suitable for use by an automated system 有权
标题翻译：适用于自动化系统使用的保险承保的多变量自适应回归样条分类系统和过程
公开(公告)号：US07813945B2
公开(公告)日：2010-10-12
申请号：US10425733
申请日：2003-04-30
申请人： Piero Patrone Bonissone , Richard Paul Messmer , Rajesh Venkat Subbu , Weizhong Yan , Anindya Chakraborty
发明人： Piero Patrone Bonissone , Richard Paul Messmer , Rajesh Venkat Subbu , Weizhong Yan , Anindya Chakraborty
IPC分类号： G06Q40/00
CPC分类号： G06Q40/02 , G06Q40/08
摘要： A method and system for automating the decision-making process used in underwriting of insurance applications is described. While this approach is demonstrated for insurance underwriting, it is broadly applicable to diverse decision-making applications in business, commercial, and manufacturing processes. A structured methodology is used based on a multi-model parallel network of multivariate adaptive regression splines (“MARS”) models to identify the relevant set of variables and their parameters, and build a framework capable of providing automated decisions. The parameters of the MARS-based decision system are estimated from a database consisting of a set of applications with reference decisions against each. Cross-validation and development/hold-out combined with re-sampling techniques are used to build a robust set of models that minimize the error between the automated system's decision and the expert human underwriter. Furthermore, this model building methodology can be used periodically to update and maintain the family of models if required to assure currency.
摘要翻译：描述了一种自动化用于承保保险应用程序的决策过程的方法和系统。虽然这种方法在保险承保方面得到证明，但它广泛适用于商业，商业和制造过程中的各种决策应用。基于多变量自适应回归样条（“MARS”）模型的多模式并行网络，使用一种结构化方法来识别相关的变量及其参数集，并构建能够提供自动化决策的框架。基于MARS的决策系统的参数是由一组数据库估算出来的，该数据库由一组应用程序组成，具有相应的参考决定。交叉验证和开发/保留与重新采样技术相结合，用于构建一套强大的模型，以最大限度地减少自动系统决策与专家人类承保人之间的错误。此外，如果需要确保货币，则可以定期使用此模型构建方法来更新和维护模型系列。

5. 发明授权

US08214314B2 System and process for a fusion classification for insurance underwriting suitable for use by an automated system 有权
标题翻译：用于融合分类的系统和过程，适用于自动化系统使用的保险承保
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。

6. 发明申请

US20090300417A1 SYSTEM AND METHOD FOR ADVANCED CONDITION MONITORING OF AN ASSET SYSTEM 有权
标题翻译：资产系统高级状态监测系统与方法
公开(公告)号：US20090300417A1
公开(公告)日：2009-12-03
申请号：US12129632
申请日：2008-05-29
申请人： Piero Patrone Bonissone , John Erik Hershey , Robert James Mitchell, JR. , Rajesh Venkat Subbu, JR. , Avinash Vinayak Taware , Xiao Hu
发明人： Piero Patrone Bonissone , John Erik Hershey , Robert James Mitchell, JR. , Rajesh Venkat Subbu, JR. , Avinash Vinayak Taware , Xiao Hu
IPC分类号： G06F11/28
CPC分类号： G05B23/0254 , G05B2219/33295 , G05B2219/33296 , G07C3/00
摘要： A method for advanced condition monitoring of an asset system includes using a plurality of auto-associative neural networks to determine estimates of actual values sensed by at least one sensor in at least one of the plurality of operating regimes; determining a residual between the estimated sensed values and the actual values sensed by the at least one sensor from each of the plurality of auto-associative neural networks; and combining the residuals by using a fuzzy supervisory model blender; performing a fault diagnostic on the combined residuals; and determining a change of the operation of the asset system by analysis of the combined residuals. An alert is provided if necessary. A smart sensor system includes an on-board processing unit for performing the method of the invention.
摘要翻译：一种用于资产系统的高级状态监测的方法包括使用多个自动关联神经网络来确定至少一个传感器在所述多个操作方案中的至少一个中感测到的实际值的估计; 从所述多个自相关神经网络中的每一个确定估计的感测值与由所述至少一个传感器感测的实际值之间的残差; 并通过使用模糊监督模型混合器组合残差; 对组合残差进行故障诊断; 以及通过分析组合残差来确定资产系统的操作变化。必要时提供警报。智能传感器系统包括用于执行本发明的方法的车载处理单元。

7. 发明申请

US20090295561A1 SYSTEM AND METHOD FOR ADVANCED CONDITION MONITORING OF AN ASSET SYSTEM 有权
标题翻译：资产系统高级状态监测系统与方法
公开(公告)号：US20090295561A1
公开(公告)日：2009-12-03
申请号：US12129611
申请日：2008-05-29
申请人： Xiao Hu , John Erik Hershey , Robert James Mitchell, JR. , Rajesh Venkat Subbu , Avinash Vinayak Taware , Piero Patrone Bonissone
发明人： Xiao Hu , John Erik Hershey , Robert James Mitchell, JR. , Rajesh Venkat Subbu , Avinash Vinayak Taware , Piero Patrone Bonissone
IPC分类号： G08B23/00
CPC分类号： G05B19/41875 , G05B2219/31455 , G05B2219/32201 , G05B2219/33027 , Y02P90/18 , Y02P90/22
摘要： A method for advanced condition monitoring of an asset system includes sensing actual values of an operating condition for an operating regime of the asset system using at least one sensor; estimating sensed values of the operating condition by using an auto-associative neural network; determining a residual vector between the estimated sensed values and the actual values; and performing a fault diagnostic on the residual vector. In another method, an operating space of the asset system is segmented into operating regimes; the auto-associative neural network determines estimates of actual measured values; a residual vector is determined from the auto-associative neural network; a fault diagnostic is performed on the residual vector; and a change of the operation of the asset system is determined by analysis of the residual vector. An alert is provided if necessary. A smart sensor system includes an on-board processing unit for performing the method of the invention.
摘要翻译：一种用于资产系统的高级状态监测的方法包括使用至少一个传感器来感测资产系统的操作状态的操作条件的实际值; 通过使用自动关联神经网络来估计操作条件的感测值; 确定估计的感测值和实际值之间的残差矢量; 并对残差向量进行故障诊断。在另一种方法中，资产系统的运营空间被划分为运行状态; 自相关神经网络确定实际测量值的估计值; 从自相关神经网络确定残差向量; 对残差矢量执行故障诊断; 通过分析残差向量来确定资产系统的运作变化。必要时提供警报。智能传感器系统包括用于执行本发明的方法的车载处理单元。

8. 发明授权

US08219477B2 Systems and methods for multi-objective portfolio analysis using pareto sorting evolutionary algorithms 有权
标题翻译：使用帕累托分类进化算法进行多目标投资组合分析的系统和方法
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。

9. 发明授权

US08144005B2 System and method for advanced condition monitoring of an asset system 有权
标题翻译：资产系统高级状态监测系统和方法
公开(公告)号：US08144005B2
公开(公告)日：2012-03-27
申请号：US12129611
申请日：2008-05-29
申请人： Xiao Hu , John Erik Hershey , Robert James Mitchell, Jr. , Rajesh Venkat Subbu , Avinash Vinayak Taware , Piero Patrone Bonissone
发明人： Xiao Hu , John Erik Hershey , Robert James Mitchell, Jr. , Rajesh Venkat Subbu , Avinash Vinayak Taware , Piero Patrone Bonissone
IPC分类号： G08B23/00
CPC分类号： G05B19/41875 , G05B2219/31455 , G05B2219/32201 , G05B2219/33027 , Y02P90/18 , Y02P90/22
摘要： A method for advanced condition monitoring of an asset system includes monitoring a variable of an asset system using the at least one sensor of a smart sensor system; determining whether the asset system has departed from normal operation; and identifying the variable of the asset system indicating the departure from normal operation. In another method, the time sequential values of the monitored variable is analyzed by using a Rank Permutation Transformation test, a Hotelling's T2 statistic test, and a Likelihood Ratio Test; and a change of an operating condition of the asset system is determined using the analyzed values. An alert is provided if necessary. A smart sensor system includes an on-board processing unit for performing the method of the invention.
摘要翻译：一种用于资产系统的高级状态监测的方法包括使用智能传感器系统的至少一个传感器监视资产系统的变量; 确定资产体系是否已经脱离正常经营; 并识别资产系统的变量，指示偏离正常操作。在另一种方法中，通过使用秩排列变换测试，Hotelling的T2统计测试和似然比测试来分析被监测变量的时间序列值; 并且使用所分析的值确定资产系统的操作条件的变化。必要时提供警报。智能传感器系统包括用于执行本发明的方法的车载处理单元。

10. 发明授权

US07844476B2 Process for case-based insurance underwriting suitable for use by an automated system 有权
标题翻译：基于案例的保险承保流程，适用于自动化系统
公开(公告)号：US07844476B2
公开(公告)日：2010-11-30
申请号：US10171190
申请日：2002-06-14
申请人： Piero Patrone Bonissone , Richard Paul Messmer , Dan Yang , Marc Pavese , Angela Neff Patterson , Antonio Mogro-Campero , Anil Varma , William Michael Durham , Diane Marie Russell , Rajesh Venkat Subbu
发明人： Piero Patrone Bonissone , Richard Paul Messmer , Dan Yang , Marc Pavese , Angela Neff Patterson , Antonio Mogro-Campero , Anil Varma , William Michael Durham , Diane Marie Russell , Rajesh Venkat Subbu
IPC分类号： G06Q40/00
CPC分类号： G06Q40/08 , G06F19/00 , G06Q50/22 , G06Q50/24
摘要： A process for at least a partial underwriting of insurance policies is described. Based on the similarity to previous insurance applications, a decision on the current request for underwriting may be made. This decision-making process represents an analogical approach to the placement of an insurance application to an underwriting category, whereby a given insurance application request is compared to previous requests.
摘要翻译：描述了至少部分承保保险单的过程。根据与以往保险申请的相似性，可以对目前的承保请求作出决定。该决策过程代表将保险申请放置于承保类别的类比方法，从而将给定的保险申请请求与以前的请求进行比较。

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