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    • 2. 发明授权
    • Tool for sensor management and fault visualization in machine condition monitoring
    • 机器状态监测中的传感器管理和故障可视化工具
    • US07183905B2
    • 2007-02-27
    • US10932576
    • 2004-09-02
    • Claus NeubauerZehra CataltepeChao YuanJie ChengMing FangWesley McCorkle
    • Claus NeubauerZehra CataltepeChao YuanJie ChengMing FangWesley McCorkle
    • G08B29/00
    • G05B23/0272Y04S10/522
    • A tool for sensor management and fault visualization in machine condition monitoring. The method and system are able to monitor a plurality of sensors at one time. The sensors may be used in a power plant system monitoring system. The method and system may display a fault status for each sensor in the plurality of sensors in a single display, wherein the fault status for each sensor is displayed over time. The method and system also provide a mechanism that permits a user to examine details of each sensor in the plurality of sensors at any given time. In addition, the method and system are capable of categorizing each fault in the fault status using one or more properties or categorizing criteria. The method and system also permit sensors to be tested such that different operating models may be examined by utilizing different sensors.
    • 机器状态监测中的传感器管理和故障可视化工具。 该方法和系统能够一次监视多个传感器。 传感器可用于发电厂系统监控系统。 该方法和系统可以在单个显示器中的多个传感器中的每个传感器显示故障状态,其中每个传感器的故障状态随时间显示。 该方法和系统还提供了允许用户在任何给定时间检查多个传感器中的每个传感器的细节的机构。 此外,该方法和系统能够使用一个或多个属性或分类标准对故障状态中的每个故障进行分类。 该方法和系统还允许测试传感器,使得可以通过利用不同的传感器检查不同的操作模型。
    • 3. 发明申请
    • Machine learning with robust estimation, bayesian classification and model stacking
    • 机器学习与鲁棒估计,贝叶斯分类和模型堆叠
    • US20060059112A1
    • 2006-03-16
    • US11208988
    • 2005-08-22
    • Jie ChengBernd WachmannClaus Neubauer
    • Jie ChengBernd WachmannClaus Neubauer
    • G06F15/18
    • G06N7/005G06K9/623G06K9/6296
    • A system and method for machine learning are provided, the system including a processor, an adapter for receiving instances for two different classes where each instance has a vector of feature values, a filtering unit for estimating distances between two corresponding instances of the two different classes for each of a plurality of estimators, a selection unit for calculating a corresponding p-value for each distance where the p-value is the statistical significance that the two feature vectors of the corresponding instances have different origins, and an evaluation unit for combining the different estimators by choosing the highest calculated p-value; and the method including receiving instances for two different classes, each instance having a vector of feature values, estimating distances between two corresponding instances of the two different classes for each of several of estimators, calculating a corresponding p-value for each distance, where the p-value is the statistical significance that the two feature vectors of the corresponding instances have different origins, and combining the different estimators by choosing the highest calculated p-value.
    • 提供了一种用于机器学习的系统和方法,所述系统包括处理器,用于接收两个不同类别的实例的适配器,每个实例具有特征值向量,滤波单元,用于估计两个不同类别的两个对应实例之间的距离 对于多个估计器中的每一个,选择单元,用于计算每个距离的相应p值,其中p值是相应实例的两个特征向量具有不同来源的统计重要性;以及评估单元, 通过选择最高计算的p值来估计不同的估计值; 并且所述方法包括接收两个不同类别的实例,每个实例具有特征值向量,估计几个估计器中的每一个的两个不同类别的两个对应实例之间的距离,计算每个距离的相应p值, p值是相应实例的两个特征向量具有不同来源的统计意义,并且通过选择最高计算的p值来组合不同的估计器。
    • 5. 发明申请
    • Method and Apparatus for Classifying Tissue Using Image Data
    • 使用图像数据分类组织的方法和装置
    • US20070123773A1
    • 2007-05-31
    • US11426329
    • 2006-06-26
    • Thomas FuchsBernd WachmannClaus NeubauerJie Cheng
    • Thomas FuchsBernd WachmannClaus NeubauerJie Cheng
    • A61B5/05
    • G06T7/0012G06K9/00147G06K9/6296G06T2207/30024
    • Disclosed is a technique for classifying tissue based on image data. A plurality of tissue parameters are extracted from image data (e.g., magnetic resonance image data) to be classified. The parameters are preprocessed, and the tissue is classified using a classification algorithm and the preprocessed parameters. In one embodiment, the parameters are preprocessed by discretization of the parameters. The classification algorithm may use a decision model for the classification of the tissue, and the decision model may be generated by performing a machine learning algorithm using preprocessed tissue parameters in a training set of data. In one embodiment, the machine learning algorithm generates a Bayesian network. The image data used may be magnetic resonance image data that was obtained before and after the intravenous administration of lymphotropic superparamagnetic nanoparticles.
    • 公开了一种基于图像数据对组织进行分类的技术。 从要分类的图像数据(例如磁共振图像数据)中提取多个组织参数。 对参数进行预处理,使用分类算法和预处理参数对组织进行分类。 在一个实施例中,参数通过离散化参数进行预处理。 分类算法可以使用用于组织分类的决策模型,并且可以通过在训练数据集中执行使用预处理的组织参数的机器学习算法来生成决策模型。 在一个实施例中,机器学习算法生成贝叶斯网络。 使用的图像数据可以是在静脉内施用嗜热超顺磁性纳米颗粒之前和之后获得的磁共振图像数据。
    • 7. 发明授权
    • Method and apparatus for classifying tissue using image data
    • 使用图像数据分类组织的方法和装置
    • US07720267B2
    • 2010-05-18
    • US11426329
    • 2006-06-26
    • Thomas FuchsBernd WachmannClaus NeubauerJie Cheng
    • Thomas FuchsBernd WachmannClaus NeubauerJie Cheng
    • G06K9/00A61B5/05
    • G06T7/0012G06K9/00147G06K9/6296G06T2207/30024
    • Disclosed is a technique for classifying tissue based on image data. A plurality of tissue parameters are extracted from image data (e.g., magnetic resonance image data) to be classified. The parameters are preprocessed, and the tissue is classified using a classification algorithm and the preprocessed parameters. In one embodiment, the parameters are preprocessed by discretization of the parameters. The classification algorithm may use a decision model for the classification of the tissue, and the decision model may be generated by performing a machine learning algorithm using preprocessed tissue parameters in a training set of data. In one embodiment, the machine learning algorithm generates a Bayesian network. The image data used may be magnetic resonance image data that was obtained before and after the intravenous administration of lymphotropic superparamagnetic nanoparticles.
    • 公开了一种基于图像数据对组织进行分类的技术。 从要分类的图像数据(例如磁共振图像数据)中提取多个组织参数。 对参数进行预处理,使用分类算法和预处理参数对组织进行分类。 在一个实施例中,参数通过离散化参数进行预处理。 分类算法可以使用用于组织分类的决策模型,并且可以通过在训练数据集中执行使用预处理的组织参数的机器学习算法来生成决策模型。 在一个实施例中,机器学习算法生成贝叶斯网络。 使用的图像数据可以是在静脉内施用嗜热超顺磁性纳米颗粒之前和之后获得的磁共振图像数据。
    • 8. 发明申请
    • Bayesian network frameworks for biomedical data mining
    • 用于生物医学数据挖掘的贝叶斯网络框架
    • US20070005257A1
    • 2007-01-04
    • US11110496
    • 2005-07-25
    • Jie ChengClaus Neubauer
    • Jie ChengClaus Neubauer
    • G06F19/00
    • G06K9/6296
    • A system and method for data classification are provided, the system including a processor, an adapter in signal communication with the processor for receiving data, a filtering unit in signal communication with the processor for pre-processing the data and filtering features of the data, a selection unit in signal communication with the processor for learning a Bayesian network (BN) classifier and selecting features responsive to the BN classifier, and an evaluation unit in signal communication with the processor for evaluating a model responsive to the BN classifier; and the method including receiving data, pre-processing the data, filtering features of the data, learning a BN classifier, selecting features responsive to the BN classifier, and evaluating a model responsive to the BN classifier.
    • 提供了一种用于数据分类的系统和方法,该系统包括处理器,与处理器进行信号通信的适配器,用于接收数据;滤波单元,与处理器进行信号通信,用于预处理数据和滤波数据的特征; 与所述处理器进行信号通信的选择单元,用于学习贝叶斯网络(BN)分类器并响应于所述BN分类器选择特征;以及评估单元,用于与所述处理器进行信号通信以评估响应于所述BN分类器的模型; 并且该方法包括接收数据,预处理数据,过滤数据的特征,学习BN分类器,响应于BN分类器选择特征,以及响应于BN分类器来评估模型。