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1. 发明授权

US06527510B2 Stator blade and stator blade cascade for axial-flow compressor 有权
公开(公告)号：US06527510B2
公开(公告)日：2003-03-04
申请号：US09866924
申请日：2001-05-30
申请人： Markus Olhofer , Bernhard Sendhoff , Edgar Körner , Yoshihiro Yamaguchi , Toyotaka Sonoda , Toshiyuki Arima
发明人： Markus Olhofer , Bernhard Sendhoff , Edgar Körner , Yoshihiro Yamaguchi , Toyotaka Sonoda , Toshiyuki Arima
IPC分类号： F04D2944
CPC分类号： F04D29/681 , F04D21/00 , F04D29/544
摘要： It is an object of the present invention to provide a stator blade for an axial-flow compressor, in which the wave drag due to the generation of a shock wave in a transonic speed range can be suppressed to the minimum. For this purpose, the stator blade in the axial-flow compressor has an intrados producing a positive pressure, and an extrados producing a negative pressure. Both of the intrados and the extrados are located on one side of a chord line. A first bulge and a second bulge are formed on the intrados of the stator blade at a location on the side of a leading edge and on the side of a trailing edge, respectively. Thus, the generation of a shock wave on the extrados can be moderated to reduce the wave drag by positively producing the separation of a boundary layer on the intrados by the first bulge. In addition, the boundary layer rendered unstable by the first bulge on the intrados can be stabilized again by the second bulge on the intrados and hence, the increase in frictional drag due to the separation of the boundary layer on the intrados can be suppressed to the minimum.

2. 发明授权

US07308134B2 Pattern recognition with hierarchical networks 有权
标题翻译：分层网络的模式识别
公开(公告)号：US07308134B2
公开(公告)日：2007-12-11
申请号：US10155948
申请日：2002-05-24
申请人： Heiko Wersing , Edgar Körner
发明人： Heiko Wersing , Edgar Körner
IPC分类号： G06K9/62 , G06K9/74 , G05B13/02
CPC分类号： G06K9/4628
摘要： Within the frameworks of hierarchical neural feed-forward architectures for performing real-world 3D invariant object recognition a technique is proposed that shares components like weight-sharing (2), and pooling stages (3, 5) with earlier approaches, but focuses on new methods for determining optimal feature-detecting units in intermediate stages (4) of the hierarchical network. A new approach for training the hierarchical network is proposed which uses statistical means for (incrementally) learning new feature detection stages and significantly reduces the training effort for complex pattern recognition tasks, compared to the prior art. The incremental learning is based on detecting increasingly statistically independent features in higher stages of the processing hierarchy. Since this learning is unsupervised, no teacher signal is necessary and the recognition architecture can be pre-structured for a certain recognition scenario. Only a final classification step must be trained with supervised learning, which reduces significantly the effort for adaptation to a recognition task.
摘要翻译：在用于执行真实世界3D不变对象识别的分层神经前馈架构的框架内，提出了一种采用早期方法共享诸如权重共享（2）和池阶段（3,5）之类的组件的技术，但是关注新的用于确定分级网络的中间阶段（4）中的最佳特征检测单元的方法。提出了一种用于训练分层网络的新方法，与现有技术相比，该方法使用统计方法（增量）学习新的特征检测阶段，并显着减少复杂模式识别任务的训练工作。增量学习是基于在处理层次的更高阶段中检测越来越多的统计学上独立的特征。由于这种学习是无监督的，所以不需要教师信号，并且识别架构可以在某种识别方案中进行预先构造。只有最后的分类步骤必须用监督学习进行培训，这大大降低了适应识别任务的努力。

3. 发明授权

US07644059B2 Integrating visual and object information in a pervasive computing environment 有权
标题翻译：将视觉和对象信息集成到普适计算环境中
公开(公告)号：US07644059B2
公开(公告)日：2010-01-05
申请号：US11067123
申请日：2005-02-25
申请人： Christian Goerick , Edgar Körner , Andreas Richter , Bernard Sendhoff
发明人： Christian Goerick , Edgar Körner , Andreas Richter , Bernard Sendhoff
IPC分类号： G06F15/00
CPC分类号： G06K9/6293
摘要： One embodiment of the present invention provides a method for gathering information from an environment. In a first step, visual information is gathered from the environment. In a second step, information actively transmitted by objects in the environment is received. According to one embodiment, the information actively transmitted by objects in the environment is received wirelessly. In a third step, the visual information is combined with the received information in order to recognize objects.
摘要翻译：本发明的一个实施例提供了一种用于从环境收集信息的方法。第一步，从环境中收集视觉信息。在第二步骤中，接收由环境中的对象主动发送的信息。根据一个实施例，无线地接收由环境中的对象主动发送的信息。在第三步骤中，视觉信息与接收到的信息相结合以识别对象。

4. 发明授权

US07831418B1 Autonomous experimental design optimization 有权
标题翻译：自主实验设计优化
公开(公告)号：US07831418B1
公开(公告)日：2010-11-09
申请号：US10456179
申请日：2003-06-05
申请人： Bernhard A. Sendhoff , Edgar Körner , Andreas Richter
发明人： Bernhard A. Sendhoff , Edgar Körner , Andreas Richter
IPC分类号： G06G7/48 , G05B13/02 , G06F19/00
CPC分类号： G06F17/5095 , G06F2217/08 , G06F2217/16 , Y02T10/82
摘要： Iterative (nondeterministic) optimization of aerodynamic and hydrodynamic surface structures can be accomplished with a computer software program and a system using a combination of a variable encoding length optimization algorithm based on an evolution strategy and an experimental hardware set-up that allows to automatically change the surface properties of the applied material, starting with the overall shape and proceeding via more detailed modifications in local surface areas. The optimization of surface structures may be done with a computing device for calculating optimized parameters of at least one (virtual) surface structure, an experimental hardware set-up for measuring dynamic properties of a specific surface structure, and an interface for feeding calculated parameters from the computing device to the experimental set-up and for feeding measured results back to the computing device as quality values for the next cycle of the optimizing step.
摘要翻译：空气动力学和流体动力学表面结构的迭代（非确定性）优化可以通过计算机软件程序和使用基于进化策略的可变编码长度优化算法和实验硬件设置的组合来实现，该实验硬件设置允许自动改变应用材料的表面性质，从总体形状开始，并通过局部表面积的更详细的修改进行。可以用用于计算至少一个（虚拟）表面结构的优化参数的计算装置，用于测量特定表面结构的动态特性的实验硬件设置以及用于从计算设备到实验设置并将测量结果馈送回计算设备作为优化步骤的下一循环的质量值。

5. 发明授权

US07356185B2 Preparation of a digital image with subsequent edge detection 失效
标题翻译：随后的边缘检测准备数字图像
公开(公告)号：US07356185B2
公开(公告)日：2008-04-08
申请号：US10456209
申请日：2003-06-05
申请人： Marc-Oliver Gewaltig , Edgar Körner , Ursula Körner
发明人： Marc-Oliver Gewaltig , Edgar Körner , Ursula Körner
IPC分类号： G06K9/48
CPC分类号： G06T5/002 , G06K9/4609 , G06T5/009 , G06T5/20 , G06T7/13 , G06T7/136
摘要： For object recognition, an image is segmented into areas of similar homogeneity at a coarse scale, which are then interpreted as surfaces. Information from different spatial scales and different image features is simultaneously evaluated by exploiting statistical dependencies on their joint appearance. Thereby, the local standard deviation of specific gray levels in the close environment of an observed pixel serves as a measure for local image homogeneity that is used to get an estimate of dominant global object contours. This information is then used to mask the original image. Thus, a fine-detailed edge detection is only applied to those parts of an image where global contours exist. After that, said edges are subject to an orientation detection. Moreover, noise and small details can be suppressed, thereby contributing to the robustness of object recognition.
摘要翻译：对于物体识别，将图像分割成粗略尺度的相似同质性的区域，然后将其解释为表面。通过利用统计依赖关系对其联合外观进行同时评估来自不同空间尺度和不同图像特征的信息。因此，在观察像素的密闭环境中的特定灰度级的局部标准偏差用作用于获得主要全局对象轮廓的估计的局部图像均匀度的度量。然后将该信息用于屏蔽原始图像。因此，精细细节边缘检测仅适用于存在全局轮廓的图像的那些部分。之后，所述边缘进行取向检测。此外，可以抑制噪声和细节，从而有助于物体识别的鲁棒性。

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