会员体验
专利管家(专利管理)
工作空间(专利管理)
风险监控(情报监控)
数据分析(专利分析)
侵权分析(诉讼无效)
联系我们
交流群
官方交流:
QQ群: 891211   
微信请扫码    >>>
现在联系顾问~
热词
    • 16. 发明授权
    • Real-time determination of web tension and control using position sensors
    • 使用位置传感器实时确定纸张张力和控制
    • US06985789B2
    • 2006-01-10
    • US10743206
    • 2003-12-22
    • Daniel H. CarlsonThomas M. ClausenJohn T. Strand
    • Daniel H. CarlsonThomas M. ClausenJohn T. Strand
    • G06F19/00B23Q15/00B23Q16/00B65H26/00B65H43/08
    • B65H23/18B65H23/1888
    • Web tension in web material passing through a web transport system is determined in real time using position sensors coupled to driven rollers that define a beginning and an end of a tension zone. The position sensors on the rollers provide information related to the amount of strained web material that has been added and subtracted from the web material present in the tension zone. The amount of web material added to, subtracted from and present in the tension zone in a sample time period is then converted to an unstrained amount of web material that when combined provides an estimate for the present amount of unstrained web material present in the tension zone. Because the length of the tension zone is both fixed and known, the tension in the web material is determined from the present amount of unstrained web material in the tension zone.
    • 通过纸幅传送系统的纸幅材料中的网张力通过与定义张紧区域的开始和结束的从动辊连接的位置传感器实时确定。 辊子上的位置传感器提供了与从张力区域中存在的纤维网材料中加入和减去的应变幅材材料量相关的信息。 然后将在样品时间段内从拉伸区域中减去和存在的纤维网材料的量转化为无限量的纤维网材料,当其组合时,提供存在于张力区域中的当前量的未应变纤维网材料的估计值 。 由于张力区域的长度是固定的并且是已知的,因此幅材材料中的张力是根据张力区域中的当前无量纲的纤维网材料量确定的。
    • 20. 发明授权
    • Facet classification neural network
    • 方面分类神经网络
    • US6167390A
    • 2000-12-26
    • US163825
    • 1993-12-08
    • Mark J. BradyBelayneh W. MillionJohn T. Strand
    • Mark J. BradyBelayneh W. MillionJohn T. Strand
    • G06F15/18G06K9/62G06K9/64G06N3/00G06N3/04
    • G06K9/6272
    • A classification neural network for piecewise linearly separating an input space to classify input patterns is described. The multilayered neural network comprises an input node, a plurality of difference nodes in a first layer, a minimum node, a plurality of perceptron nodes in a second layer and an output node. In operation, the input node broadcasts the input pattern to all of the difference nodes. The difference nodes, along with the minimum node, identify in which vornoi cell of the piecewise linear separation the input pattern lies. The difference node defining the vornoi cell localizes input pattern to a local coordinate space and sends it to a corresponding perceptron, which produces a class designator for the input pattern.
    • 描述了用于分段线性分离输入空间以分类输入模式的分类神经网络。 多层神经网络包括输入节点,第一层中的多个差分节点,最小节点,第二层中的多个感知节点和输出节点。 在操作中,输入节点将输入模式广播到所有差分节点。 差分节点以及最小节点识别输入模式所在的分段线性分离的哪个单元格。 限定vornoi单元的差异节点将输入模式定位到局部坐标空间,并将其发送到相应的感知器,该感知器产生输入模式的类指示符。