公开(公告)号：US07739213B1

公开(公告)日：2010-06-15

申请号：US11715068

申请日：2007-03-06

Applicant: Krzysztof W. Przytula ,  Tsai Ching Lu

Inventor： Krzysztof W. Przytula ,  Tsai Ching Lu

IPC: G06N5/02

CPC classification number: G06N7/005

Abstract: A method and computer program product to capture expert knowledge and data using probabilistic models. A custom layered structure and nodes reduce the complexity of the model, allowing for representation of the model using tables. An editor is used for entry and verification of expert knowledge and data into tables and a probabilistic model is generated from the tables.

Abstract translation: 一种使用概率模型捕获专家知识和数据的方法和计算机程序产品。 定制分层结构和节点降低了模型的复杂性，允许使用表格来表示模型。 编辑器用于将专家知识和数据输入和验证到表中，并从表中生成概率模型。

公开(公告)号：US07158958B2

公开(公告)日：2007-01-02

申请号：US10746840

申请日：2003-12-24

Applicant: Krzysztof W. Przytula ,  Richard I. Milford

Inventor： Krzysztof W. Przytula ,  Richard I. Milford

IPC: G06N5/00

CPC classification number: G06N7/005

Abstract: Fault trees are automatically converted to Bayesian networks for assisting in system reliability, failure analysis and diagnostics by using information from the fault tree structure to create the Bayesian network structure, creating parameters of the Bayesian network using information from the fault tree, obtaining information about observation nodes for the Bayesian network from a list of observations that augments information contained in the fault tree, and inserting the observation nodes into the Bayesian network. The fault tree is pre-processed into an intermediate format prior to conversion that may include adding reliability values from a separate text document when the fault tree is in such format that requires it.

Abstract translation: 故障树被自动转换为贝叶斯网络，通过使用故障树结构的信息来建立贝叶斯网络结构，利用故障树信息创建贝叶斯网络的参数，获取有关观测信息的系统可靠性，故障分析和诊断 贝叶斯网络的节点从增加包含在故障树中的信息的观测列表中，并将观测节点插入到贝叶斯网络中。 故障树在转换之前被预先处理为中间格式，其中可能包括当故障树处于需要的格式时，从单独的文本文档中添加可靠性值。

公开(公告)号：US07577548B1

公开(公告)日：2009-08-18

申请号：US11713561

申请日：2007-03-01

Applicant: Krzysztof W Przytula ,  Shubha Kadambe ,  Narayan Srinivasa

Inventor： Krzysztof W Przytula ,  Shubha Kadambe ,  Narayan Srinivasa

IPC: G06F19/00 ,  G06F17/40

CPC classification number: G06N7/005

Abstract: Described is a system for diagnosis and prognosis of a component. The system is configured to receive a signal from a component. The signal is representative of a current health observation of the component. The system also computes a present likelihood of the component failure based on the signal. Additionally, the system computes a future likelihood of failure of the component for a given future mission. Through diagnosis, a user can determine the present health of the component, and based on the present health and future mission, determine whether or not the component will fail in the future mission.

Abstract translation: 描述了用于组件的诊断和预后的系统。 系统被配置为从组件接收信号。 该信号代表组件的当前健康观察。 该系统还基于该信号计算组件故障的当前可能性。 此外，该系统计算组件在给定未来任务中的未来可能性。 通过诊断，用户可以确定组件的当前健康状况，并根据当前健康状况和未来任务，确定组件是否在将来的任务中失败。

公开(公告)号：US07328200B2

公开(公告)日：2008-02-05

申请号：US10695529

申请日：2003-10-27

Applicant: Krzysztof W. Przytula

Inventor： Krzysztof W. Przytula

IPC: G06N5/00

CPC classification number: G06N7/005

Abstract: The present invention converts decision flowcharts into decision probabilistic graphs on a data processing system. First, a decision flowchart is received, having evidence nodes, a root evidence node, and outcome nodes. The outcome nodes are related to the evidence nodes by conclusion links. Next, an operation is performed, generating a probabilistic graph based on the flowchart. The graph includes an aggregate outcome node having outcome states, with each outcome state representing an outcome node of the flowchart; a plurality of test nodes, each matching an evidence node in the flowchart, and each test state matching a conclusion link from the evidence node in the flowchart, and causal links between the aggregate outcome node and the evidence nodes. Prior probabilities are calculated for outcome states based on predetermined likelihoods. Conditional probabilities are determined for test states by examining dependencies of conclusion links on the outcome nodes in the decision flowchart.

Abstract translation: 本发明将决策流程图转换为数据处理系统上的决策概率图。 首先，接收到具有证据节点，根证据节点和结果节点的决策流程图。 结果节点通过结论链接与证据节点相关。 接下来，执行操作，基于该流程图生成概率图。 该图包括具有结果状态的聚合结果节点，每个结果状态表示流程图的结果节点; 多个测试节点，每个测试节点与流程图中的证据节点相匹配，每个测试状态与流程图中的证据节点匹配一个结论链接，以及聚合结果节点和证据节点之间的因果链接。 基于预定的可能性计算结果状态的先验概率。 通过在决策流程图中检查结论链接对结果节点的依赖关系，确定测试状态的条件概率。

公开(公告)号：US5832291A

公开(公告)日：1998-11-03

申请号：US572932

申请日：1995-12-15

Applicant: Philip G. Rosen ,  Krzysztof W. Przytula ,  David A. Schwartz

Inventor： Philip G. Rosen ,  Krzysztof W. Przytula ,  David A. Schwartz

IPC: G06F15/80 ,  G06F13/00

CPC classification number: G06F15/8023

Abstract: A data processor intended for a single instruction, multiple data mode operation includes memory that is external to the processor array, and a controller that dynamically and selectably interconnects multiple edges of the processor array with the memory and with I/O ports. A separate controller module is provided for each memory channel, and interconnects with corresponding edge processing elements of the processor array. The controller modules for the different channels are independent of each other, as are the channel memories. In the case of a rectangular processor array, each channel memory can be implemented with only three memory stores that are interconnected with the four edges of the processing array and the I/O ports through the channel controller module, yet for most algorithms provide a throughput that is comparable to that resulting from the use of four memory stores.

Abstract translation: 旨在用于单个指令的数据处理器，多数据模式操作包括处理器阵列外部的存储器以及动态地和可选地将处理器阵列的多个边缘与存储器和I / O端口互连的控制器。 为每个存储器通道提供单独的控制器模块，并且与处理器阵列的相应边缘处理元件互连。 不同通道的控制器模块彼此独立，通道存储器也是相互独立的。 在矩形处理器阵列的情况下，每个通道存储器可以仅通过通过通道控制器模块与处理阵列的四个边缘和I / O端口互连的三个存储器存储器实现，但是对于大多数算法提供吞吐量 这与使用四个内存存储产生的相当。

公开(公告)号：US20120323826A1

公开(公告)日：2012-12-20

申请号：US13159759

申请日：2011-06-14

Applicant: Krzysztof W. Przytula ,  Rashmi N. Sundareswara ,  Steven B. Seida

Inventor： Krzysztof W. Przytula ,  Rashmi N. Sundareswara ,  Steven B. Seida

IPC: G06F15/18 ,  G06N5/04

CPC classification number: G06N7/005

Abstract: Disclosed is a system and method for predicting political instability. This instability is predicted for specific countries or geographic regions. In one embodiment, the prediction is carried out on a basis of a probabilistic model, such as a Bayesian-network. The model is comprised of various notes corresponding to dependent and independent variables. The independent variables, in turn, correspond to factors relating to historical political instability. The dependent variable corresponds to the prediction of instability. By populating the independent variables with current data, future political instability can be predicted.

Abstract translation: 披露了一种预测政治不稳定的制度和方法。 这种不稳定性是针对特定国家或地理区域预测的。 在一个实施例中，基于诸如贝叶斯网络的概率模型来执行预测。 该模型由对应于依赖和独立变量的各种笔记组成。 反过来，这些独立变量也与历史政治不稳定有关。 因变量对应于不稳定性的预测。 通过使用当前数据填充自变量，可以预测未来的政治不稳定性。

公开(公告)号：US07650272B2

公开(公告)日：2010-01-19

申请号：US10692697

申请日：2003-10-23

Applicant: Krzysztof W. Przytula ,  Denver Dash

Inventor： Krzysztof W. Przytula ,  Denver Dash

IPC: G06F9/44 ,  G01B5/28 ,  G01R31/00 ,  G06F17/00 ,  G06N7/00 ,  G06F11/00

CPC classification number: G06N7/005 ,  G06Q10/04

Abstract: A method, apparatus, and computer program product are presented for automatically evaluating Bayesian network models. Operations performed comprise receiving a Bayesian Network (BN) model including evidence nodes and conclusion nodes that are linked with the evidence nodes by causal dependency links, and where the evidence nodes have evidence states and the conclusion nodes have conclusion states. The states of conclusion nodes are set to desired conclusion states and corresponding probabilities of occurrence of evidence states are determined by propagating these states down the causal dependency links. Thus, samples of most likely states of the evidence nodes are generated. Then, states of the evidence nodes are set corresponding to the samples of the evidence states. These states are propagated back up the causal dependency links to obtain probabilities of the resulting states of the conclusion nodes. Finally, a representation is outputted for the probabilities of the states of the conclusion nodes.

Abstract translation: 提出了一种自动评估贝叶斯网络模型的方法，装置和计算机程序产品。 执行的操作包括接收贝叶斯网络（BN）模型，其包括通过因果依赖性链接与证据节点相关联的证据节点和结论节点，以及证据节点具有证据状态并且结论节点具有结论状态。 将结论节点的状态设置为期望的结论状态，并通过将因果依赖关系传播到这些状态来确定证据状态发生的相应概率。 因此，生成证据节点的最可能状态的样本。 然后，根据证据状态样本设置证据结点状态。 这些状态被传播给因果依赖链接以获得结论节点的结果状态的概率。 最后，输出结论节点状态概率的表示。

公开(公告)号：US20090222398A1

公开(公告)日：2009-09-03

申请号：US12364697

申请日：2009-02-03

Applicant: David L. Allen ,  Steven B. Seida ,  Krzysztof W. Przytula

Inventor： David L. Allen ,  Steven B. Seida ,  Krzysztof W. Przytula

IPC: G06N5/00

CPC classification number: G06N5/045

Abstract: In accordance with a particular embodiment of the invention, a method for explaining a recommendation produced by a decision support tool is disclosed. The method comprises submitting a list of observation inputs to the decision support tool and producing a recommendation. The list of inputs is then reordered according to an observation metric. The method further comprises quantifying how each input impacts the probability of the recommendation produced. The inputs may then be ranked by comparing the associated changes in probability of the recommendation produced.

Abstract translation: 根据本发明的具体实施例，公开了一种用于解释由决策支持工具产生的推荐的方法。 该方法包括向决策支持工具提交观察投入清单并提出建议。 然后根据观测指标重新排列输入列表。 该方法还包括量化每个输入如何影响产生的推荐概率。 然后可以通过比较所产生的推荐概率的相关变化来对输入进行排名。

公开(公告)号：US07272587B1

公开(公告)日：2007-09-18

申请号：US11045707

申请日：2005-01-28

Applicant: Krzysztof W. Przytula

Inventor： Krzysztof W. Przytula

IPC: G06F9/44 ,  G06N7/02 ,  G06N7/06

CPC classification number: G06N7/005

Abstract: A method, apparatus and computer program product for conversion of decision trees into probabilistic models such as Bayesian networks. Decisions trees are converted into probabilistic models without loss of information stored in the decision tree implicitly or explicitly. As a result, the probabilistic model is usable to reproduce the paths of the original tree. An inference algorithm can be used to reproduce the paths of the original tree from the probabilistic model.

Abstract translation: 一种用于将决策树转换为诸如贝叶斯网络之类的概率模型的方法，装置和计算机程序产品。 决策树被转换为概率模型，而不会隐含或明确地丢失存储在决策树中的信息。 因此，概率模型可用于重现原始树的路径。 推理算法可用于从概率模型中再现原始树的路径。

公开(公告)号：US08156069B2

公开(公告)日：2012-04-10

申请号：US12364697

申请日：2009-02-03

Applicant: David L. Allen ,  Steven B. Seida ,  Krzysztof W. Przytula

Inventor： David L. Allen ,  Steven B. Seida ,  Krzysztof W. Przytula

IPC: G06F9/44 ,  G06N7/02 ,  G06N7/06

CPC classification number: G06N5/045

Abstract: In accordance with a particular embodiment of the invention, a method for explaining a recommendation produced by a decision support tool is disclosed. The method comprises submitting a list of observation inputs to the decision support tool and producing a recommendation. The list of inputs is then reordered according to an observation metric. The method further comprises quantifying how each input impacts the probability of the recommendation produced. The inputs may then be ranked by comparing the associated changes in probability of the recommendation produced.

Abstract translation: 根据本发明的具体实施例，公开了一种用于解释由决策支持工具产生的推荐的方法。 该方法包括向决策支持工具提交观察投入清单并提出建议。 然后根据观测指标重新排列输入列表。 该方法还包括量化每个输入如何影响产生的推荐概率。 然后可以通过比较所产生的推荐概率的相关变化来对输入进行排名。