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105 条记录 (耗时 0.037 秒)

    CONSTRUCTION OF ENTROPY-BASED PRIOR AND POSTERIOR PROBABILITY DISTRIBUTIONS WITH PARTIAL INFORMATION FOR FATIGUE DAMAGE PROGNOSTICS
    91.
    发明申请
    CONSTRUCTION OF ENTROPY-BASED PRIOR AND POSTERIOR PROBABILITY DISTRIBUTIONS WITH PARTIAL INFORMATION FOR FATIGUE DAMAGE PROGNOSTICS 有权
    基于熵的前期和后期概率分布的构建，具有部分信息用于疲劳损伤预处理

    公开(公告)号：US20140100827A1

    公开(公告)日：2014-04-10

    申请号：US14015084

    申请日：2013-08-30

    申请人： Xuefei Guan ,  Jingdan Zhang ,  Shaohua Kevin Zhou

    发明人： Xuefei Guan ,  Jingdan Zhang ,  Shaohua Kevin Zhou

    IPC分类号： G06F17/50

    CPC分类号： G06F17/5009 ,  G06F2217/10 ,  G06F2217/76

    摘要： A method for predicting fatigue crack growth in materials includes providing a prior distribution obtained using response measures from one or more target components using a fatigue crack growth model as a constraint function, receiving new crack length measurements, providing a posterior distribution obtained using the new crack length measurements, and sampling the posterior distribution to obtain crack length measurement predictions.

    摘要翻译： 用于预测材料中的疲劳裂纹扩展的方法包括使用疲劳裂纹扩展模型作为约束函数提供使用来自一个或多个目标部件的响应措施获得的先验分布，接收新的裂纹长度测量值，提供使用新裂纹获得的后验分布 长度测量，并对后验分布进行采样，以获得裂纹长度测量预测。

    Reconstruction of Phased Array Data
    94.
    发明申请
    Reconstruction of Phased Array Data 失效
    相控阵数据重建

    公开(公告)号：US20120128266A1

    公开(公告)日：2012-05-24

    申请号：US13245003

    申请日：2011-09-26

    申请人： Jingdan Zhang ,  Moritz Michael Knorr ,  Guozhen Li ,  Shaohua Kevin Zhou ,  El Mahjoub Rasselkorde ,  Waheed A. Abbasi ,  Michael J. Metala

    发明人： Jingdan Zhang ,  Moritz Michael Knorr ,  Guozhen Li ,  Shaohua Kevin Zhou ,  El Mahjoub Rasselkorde ,  Waheed A. Abbasi ,  Michael J. Metala

    IPC分类号： G06K9/36

    CPC分类号： G06T17/00

    摘要： An image reconstruction method includes receiving volume data comprising a plurality of sampling points, determining a first conditioning of the sampling points suppressing low amplitudes and conserving maximum amplitudes, determining a second conditioning of the sampling points wherein an influence of a sampling point depends on its distance to a grid point in a sampling grid, determining a kernel comprising a plurality of weighting functions for the first conditioning and the second conditioning to determine an energy spread of each of the plurality of sampling points without determining a shape or size of the kernel, and outputting a reconstructed volume according to the energy spread of each of the plurality of sampling points.

    摘要翻译： 图像重建方法包括：接收包含多个采样点的音量数据，确定抑制低振幅的采样点和节省最大振幅的第一调节，确定采样点的影响取决于其距离的采样点的第二调节 到采样网格中的网格点，确定包含用于所述第一调节的多个加权函数的内核和所述第二调节，以确定所述多个采样点中的每一个的能量扩展，而不确定所述内核的形状或大小，以及 根据所述多个采样点中的每一个的能量扩展输出重构的音量。

    Method and System for On-Site Learning of Landmark Detection Models for End User-Specific Diagnostic Medical Image Reading
    99.
    发明申请
    Method and System for On-Site Learning of Landmark Detection Models for End User-Specific Diagnostic Medical Image Reading 有权
    用于特定于用户的诊断医学图像阅读的地标检测模型的现场学习方法和系统

    公开(公告)号：US20140219548A1

    公开(公告)日：2014-08-07

    申请号：US13761263

    申请日：2013-02-07

    申请人： Michael Wels ,  Michael Kelm ,  Michael Suehling ,  Shaohua Kevin Zhou

    发明人： Michael Wels ,  Michael Kelm ,  Michael Suehling ,  Shaohua Kevin Zhou

    IPC分类号： A61B5/00

    CPC分类号： A61B5/0033 ,  A61B6/00 ,  A61B6/025 ,  A61B6/502 ,  G06F19/00 ,  G06F19/321 ,  G06K9/00 ,  G06K9/6255 ,  G06K2209/05

    摘要： A method and system for on-line learning of landmark detection models for end-user specific diagnostic image reading is disclosed. A selection of a landmark to be detected in a 3D medical image is received. A current landmark detection result for the selected landmark in the 3D medical image is determined by automatically detecting the selected landmark in the 3D medical image using a stored landmark detection model corresponding to the selected landmark or by receiving a manual annotation of the selected landmark in the 3D medical image. The stored landmark detection model corresponding to the selected landmark is then updated based on the current landmark detection result for the selected landmark in the 3D medical image. The landmark selected in the 3D medical image can be a set of landmarks defining a custom view of the 3D medical image.

    摘要翻译： 公开了一种用于在线学习用于最终用户特定诊断图像读取的地标检测模型的方法和系统。 接收在3D医疗图像中检测的地标的选择。 通过使用与所选择的地标相对应的存储的地标检测模型自动检测3D医学图像中的所选地标来确定3D医学图像中所选择的地标的当前地标检测结果，或者通过接收所选地标的手动注释 3D医学图像。 然后基于3D医学图像中所选地标的当前地标检测结果来更新对应于所选地标的所存储的地标检测模型。 在3D医学图像中选择的地标可以是定义3D医学图像的定制视图的一组地标。

    PROBABILISTIC FATIGUE LIFE PREDICTION USING ULTRASONIC INSPECTION DATA CONSIDERING EIFS UNCERTAINTY
    100.
    发明申请
    PROBABILISTIC FATIGUE LIFE PREDICTION USING ULTRASONIC INSPECTION DATA CONSIDERING EIFS UNCERTAINTY 审中-公开
    使用超声波检查数据考虑EIFS不确定度的概率疲劳寿命预测

    公开(公告)号：US20130268214A1

    公开(公告)日：2013-10-10

    申请号：US13855130

    申请日：2013-04-02

    申请人： Xuefei Guan ,  Jingdan Zhang ,  Kai Kadau ,  Shaohua Kevin Zhou

    发明人： Xuefei Guan ,  Jingdan Zhang ,  Kai Kadau ,  Shaohua Kevin Zhou

    IPC分类号： G01N29/44

    CPC分类号： G01N29/4418 ,  G01N17/00 ,  G01N29/4472 ,  G01N2203/0218 ,  G01N2291/0258

    摘要： A method for probabilistically predicting fatigue life in materials includes sampling a random variable for an actual equivalent initial flaw size (EIFS), generating random variables for parameters (ln C, m) of a fatigue crack growth equation  a  N = C  ( Δ   K ) m from a multivariate distribution, and solving the fatigue crack growth equation using these random variables. The reported EIFS data is obtained by ultrasonically scanning a target object, recording echo signals from the target object, and converting echo signal amplitudes to equivalent reflector sizes using previously recorded values from a scanned calibration block. The equivalent reflector sizes comprise the reported EIFS data.

    摘要翻译： 一种用于概率预测材料疲劳寿命的方法包括对实际等效初始缺陷尺寸（EIFS）采样随机变量，为疲劳裂纹扩展方程的参数（ln C，m）生成随机变量a N = C Delta多普勒K）m，并使用这些随机变量求解疲劳裂纹扩展方程。 报告的EIFS数据是通过超声扫描目标物体，记录来自目标物体的回波信号，并使用先前记录的来自扫描校准块的值将回波信号幅度转换为等效反射镜尺寸而获得的。 等效的反射器尺寸包括报告的EIFS数据。