METHOD AND DEVICE FOR CONTROLLING AN ENERGY-GENERATING SYSTEM WHICH CAN BE OPERATED WITH A RENEWABLE ENERGY SOURCE
    1.
    发明申请
    METHOD AND DEVICE FOR CONTROLLING AN ENERGY-GENERATING SYSTEM WHICH CAN BE OPERATED WITH A RENEWABLE ENERGY SOURCE 有权
    用于控制可再生能源的能源发电系统的方法和装置

    公开(公告)号:US20150381103A1

    公开(公告)日:2015-12-31

    申请号:US14764102

    申请日:2013-12-03

    IPC分类号: H01L31/04 G05B13/02

    摘要: A method and a device for controlling an energy-generating system are operated with a renewable energy source. In the method, a prediction about an energy yield of the energy-generating system is made for a predefined prediction time period, and a predefined area, using a learning system with an input vector and an output vector. The output vector includes operating variables for a multiplicity of successive future times of the time period. The input vector includes variables, influencing the operating variables, for a point in time from a multiplicity of points in time of a predefined observation time period. The input variables include at least three items of information for the observation time period and the predefined area. The energy-generating system is controlled on the basis of the generated prediction such that weather-conditioned fluctuations in the energy yield of the energy-generating system are reduced.

    摘要翻译: 一种用于控制能量产生系统的方法和装置用可再生能源操作。 在该方法中,使用具有输入向量和输出向量的学习系统,对预定的预测时间周期和预定义区域进行关于能量产生系统的能量收益的预测。 输出向量包括该时间段的多个连续未来时间的操作变量。 输入向量包括从预定观察时间段的多个时间点起的时间点影响操作变量的变量。 输入变量包括用于观察时间段和预定义区域的至少三个信息项。 基于生成的预测来控制能量产生系统,使得能量产生系统的能量产出的气候条件波动减小。

    Method and System for Tracking Catheters in 2D X-Ray Fluoroscopy Using a Graphics Processing Unit
    3.
    发明申请
    Method and System for Tracking Catheters in 2D X-Ray Fluoroscopy Using a Graphics Processing Unit 有权
    使用图形处理单元在2D X射线荧光透视中跟踪导管的方法和系统

    公开(公告)号:US20130072788A1

    公开(公告)日:2013-03-21

    申请号:US13622426

    申请日:2012-09-19

    IPC分类号: A61M25/095

    摘要: A method and system for detecting and tracking multiple catheters in a fluoroscopic image sequence in an integrated central processing unit and graphics processing unit framework is disclosed. A catheter electrode model is initialized in a first frame of the fluoroscopic image sequence. The catheter landmark candidates are detected, by a graphics processing unit, in the first frame of the fluoroscopic image sequence. The catheter electrode model is tracked, by a central processing unit, and is detected by the graphics processing unit, in subsequent frames of the fluoroscopic image sequence by detecting catheter landmark candidates in the subsequent frames of the fluoroscopic image sequence using at least one trained catheter landmark detector, and outputting the catheter model tracking and landmark detection results of for each frame of the fluoroscopic image sequence.

    摘要翻译: 公开了一种用于在集成中央处理单元和图形处理单元框架中的荧光图像序列中检测和跟踪多个导管的方法和系统。 在荧光镜图像序列的第一帧中初始化导管电极模型。 通过图形处理单元在透视图像序列的第一帧中检测导管标记候选物。 通过中央处理单元跟踪导管电极模型,并且通过使用至少一个经过训练的导管在荧光镜图像序列的后续帧中检测导管标记候选物,在透视图像序列的后续帧中由图形处理单元检测 并且输出针对荧光透视图像序列的每个帧的导管模型跟踪和地标检测结果。

    DEEP-LEARNING BASED FEATURE MINING FOR 2.5D SENSING IMAGE SEARCH

    公开(公告)号:US20190130603A1

    公开(公告)日:2019-05-02

    申请号:US16082920

    申请日:2017-03-09

    IPC分类号: G06T7/73 G06K9/62 G06K9/46

    摘要: Systems, methods, and computer-readable media are disclosed for determining feature representations of 2.5D image data using deep learning techniques. The 2.5D image data may be synthetic image data generated from 3D simulated model data such as 3D CAD data. The 2.5D image data may be indicative of any number of pose estimations/camera poses representing virtual or actual viewing perspectives of an object modeled by the 3D CAD data. A neural network such as a convolution neural network (CNN) may be trained using the 2.5D image data as training data to obtain corresponding feature representations. The pose estimations/camera poses may be stored in a data repository in association with the corresponding feature representations. The learnt CNN may then be used to determine an input feature representation from an input 2.5D image and index the input feature representation against the data repository to determine matching pose estimation(s).