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1. 发明授权

US07720267B2 Method and apparatus for classifying tissue using image data 有权
标题翻译：使用图像数据分类组织的方法和装置
公开(公告)号：US07720267B2
公开(公告)日：2010-05-18
申请号：US11426329
申请日：2006-06-26
申请人： Thomas Fuchs , Bernd Wachmann , Claus Neubauer , Jie Cheng
发明人： Thomas Fuchs , Bernd Wachmann , Claus Neubauer , Jie Cheng
IPC分类号： G06K9/00 , A61B5/05
CPC分类号： G06T7/0012 , G06K9/00147 , G06K9/6296 , G06T2207/30024
摘要： 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.
摘要翻译：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。

2. 发明申请

US20070005257A1 Bayesian network frameworks for biomedical data mining 审中-公开
标题翻译：用于生物医学数据挖掘的贝叶斯网络框架
公开(公告)号：US20070005257A1
公开(公告)日：2007-01-04
申请号：US11110496
申请日：2005-07-25
申请人： Jie Cheng , Claus Neubauer
发明人： Jie Cheng , Claus Neubauer
IPC分类号： G06F19/00
CPC分类号： G06K9/6296
摘要： 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.
摘要翻译：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。

3. 发明申请

US20050062599A1 Tool for sensor management and fault visualization in machine condition monitoring 有权
标题翻译：机器状态监测中的传感器管理和故障可视化工具
公开(公告)号：US20050062599A1
公开(公告)日：2005-03-24
申请号：US10932576
申请日：2004-09-02
申请人： Claus Neubauer , Zehra Cataltepe , Chao Yuan , Jie Cheng , Ming Fang , Wesley McCorkle
发明人： Claus Neubauer , Zehra Cataltepe , Chao Yuan , Jie Cheng , Ming Fang , Wesley McCorkle
IPC分类号： G05B23/02 , G08B29/00
CPC分类号： G05B23/0272 , Y04S10/522
摘要： 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.
摘要翻译：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。

4. 发明授权

US07240042B2 System and method for biological data analysis using a bayesian network combined with a support vector machine 失效
标题翻译：使用与支持向量机组合的贝叶斯网络进行生物数据分析的系统和方法
公开(公告)号：US07240042B2
公开(公告)日：2007-07-03
申请号：US11208743
申请日：2005-08-22
申请人： Jie Cheng , Chao Yuan , Bernd Wachmann , Claus Neubauer
发明人： Jie Cheng , Chao Yuan , Bernd Wachmann , Claus Neubauer
IPC分类号： G06N5/00
CPC分类号： G06N7/005 , G06F19/20 , G06F19/24 , G06K9/6269 , G06K9/6296
摘要： A method for analyzing biological data includes classifying a first set of biological data in a first classifier, classifying a second set of biological data in a second classifier, combining the results of the first classifier with the results of the second classifier, and analyzing the results as a function of the similarity measure of the first classifier and the similarity measure of the second classifier.
摘要翻译：一种用于分析生物数据的方法包括对第一分类器中的第一组生物数据进行分类，将第二分类器中的第二组生物数据分类，将第一分类器的结果与第二分类器的结果相结合，并分析结果作为第一分类器的相似性度量和第二分类器的相似性度量的函数。

5. 发明申请

US20070123773A1 Method and Apparatus for Classifying Tissue Using Image Data 有权
标题翻译：使用图像数据分类组织的方法和装置
公开(公告)号：US20070123773A1
公开(公告)日：2007-05-31
申请号：US11426329
申请日：2006-06-26
申请人： Thomas Fuchs , Bernd Wachmann , Claus Neubauer , Jie Cheng
发明人： Thomas Fuchs , Bernd Wachmann , Claus Neubauer , Jie Cheng
IPC分类号： A61B5/05
CPC分类号： G06T7/0012 , G06K9/00147 , G06K9/6296 , G06T2207/30024
摘要： 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.
摘要翻译：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。

6. 发明申请

US20060045207A1 Peak detection in mass spectroscopy data analysis 审中-公开
标题翻译：质谱数据分析中的峰值检测
公开(公告)号：US20060045207A1
公开(公告)日：2006-03-02
申请号：US11180342
申请日：2005-07-13
申请人： Jie Cheng , Claus Neubauer
发明人： Jie Cheng , Claus Neubauer
IPC分类号： H04L25/06
CPC分类号： H01J49/0036
摘要： A computer-implemented method for extracting peak information including providing a data spectrum, normalizing the data spectrum, binning features for reducing the resolution of the data spectrum and filtering noise from a normalized data spectrum, identifying at least one peak in the normalized data spectrum, performing a baseline correction of the at least one peak, and performing data mining on the at least one peak to determine a pathology.
摘要翻译：一种计算机实现的方法，用于提取峰值信息，包括提供数据频谱，归一化数据频谱，合并特征，用于降低数据频谱的分辨率，并从标准化数据频谱滤除噪声，识别归一化数据频谱中的至少一个峰值，执行所述至少一个峰值的基线校正，以及对所述至少一个峰值执行数据挖掘以确定病理学。

7. 发明授权

US07183905B2 Tool for sensor management and fault visualization in machine condition monitoring 有权
标题翻译：机器状态监测中的传感器管理和故障可视化工具
公开(公告)号：US07183905B2
公开(公告)日：2007-02-27
申请号：US10932576
申请日：2004-09-02
申请人： Claus Neubauer , Zehra Cataltepe , Chao Yuan , Jie Cheng , Ming Fang , Wesley McCorkle
发明人： Claus Neubauer , Zehra Cataltepe , Chao Yuan , Jie Cheng , Ming Fang , Wesley McCorkle
IPC分类号： G08B29/00
CPC分类号： G05B23/0272 , Y04S10/522
摘要： 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.
摘要翻译：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。

8. 发明申请

US20060059112A1 Machine learning with robust estimation, bayesian classification and model stacking 审中-公开
标题翻译：机器学习与鲁棒估计，贝叶斯分类和模型堆叠
公开(公告)号：US20060059112A1
公开(公告)日：2006-03-16
申请号：US11208988
申请日：2005-08-22
申请人： Jie Cheng , Bernd Wachmann , Claus Neubauer
发明人： Jie Cheng , Bernd Wachmann , Claus Neubauer
IPC分类号： G06F15/18
CPC分类号： G06N7/005 , G06K9/623 , G06K9/6296
摘要： 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.
摘要翻译：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。

9. 发明申请

US20060047616A1 System and method for biological data analysis using a bayesian network combined with a support vector machine 失效
标题翻译：使用与支持向量机组合的贝叶斯网络进行生物数据分析的系统和方法
公开(公告)号：US20060047616A1
公开(公告)日：2006-03-02
申请号：US11208743
申请日：2005-08-22
申请人： Jie Cheng , Chao Yuan , Bernd Wachmann , Claus Neubauer
发明人： Jie Cheng , Chao Yuan , Bernd Wachmann , Claus Neubauer
IPC分类号： G06F19/00 , G06N5/00 , G06N7/02 , G01N33/48 , G06F17/00 , G06F9/44 , G01N33/50 , G06N7/06
CPC分类号： G06N7/005 , G06F19/20 , G06F19/24 , G06K9/6269 , G06K9/6296
摘要： A method for analyzing biological data includes classifying a first set of biological data in a first classifier, classifying a second set of biological data in a second classifier, combining the results of the first classifier with the results of the second classifier, and analyzing the results as a function of the similarity measure of the first classifier and the similarity measure of the second classifier.
摘要翻译：一种用于分析生物数据的方法包括对第一分类器中的第一组生物数据进行分类，将第二分类器中的第二组生物数据分类，将第一分类器的结果与第二分类器的结果相结合，并分析结果作为第一分类器的相似性度量和第二分类器的相似性度量的函数。

10. 发明授权

US08005771B2 Segment-based change detection method in multivariate data stream 有权
标题翻译：多变量数据流中基于段的变化检测方法
公开(公告)号：US08005771B2
公开(公告)日：2011-08-23
申请号：US12236587
申请日：2008-09-24
申请人： Terrence Chen , Chao Yuan , Abdul Saboor Sheikh , Claus Neubauer
发明人： Terrence Chen , Chao Yuan , Abdul Saboor Sheikh , Claus Neubauer
IPC分类号： G06F15/18
CPC分类号： G06K9/00536 , G06K9/6284
摘要： A method and framework are described for detecting changes in a multivariate data stream. A training set is formed by sampling time windows in a data stream containing data reflecting normal conditions. A histogram is created to summarize each window of data, and data within the histograms are clustered to form test distribution representatives to minimize the bulk of training data. Test data is then summarized using histograms representing time windows of data and data within the test histograms are clustered. The test histograms are compared to the training histograms using nearest neighbor techniques on the clustered data. Distances from the test histograms to the test distribution representatives are compared to a threshold to identify anomalies.
摘要翻译：描述了用于检测多变量数据流中的变化的方法和框架。通过在包含反映正常条件的数据的数据流中采样时间窗口来形成训练集。创建直方图以总结每个数据窗口，并且将直方图中的数据进行聚类以形成测试分发代表以最小化训练数据的大部分。然后使用表示数据的时间窗口的直方图来汇总测试数据，并且将测试直方图中的数据聚类。将测试直方图与使用最近邻技术的聚类数据的训练直方图进行比较。将测试直方图与测试分布代表的距离与阈值进行比较以识别异常。

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