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    • 37. 发明申请
    • SELECTING METHOD OF LIGHT GUIDE PLATE OF BACKLIGHT MODULE
    • 背光模组光导板的选择方法
    • US20130158956A1
    • 2013-06-20
    • US13380889
    • 2011-12-15
    • Chechang HuKuangyao ChangLei SunWei Fan
    • Chechang HuKuangyao ChangLei SunWei Fan
    • G06F17/50
    • G02B6/0043G02B6/0051G02B6/0053G02B6/0065G02F1/133615
    • A selecting method of light guide plate of backlight module is described. The selecting method includes the steps of: calculating a plurality of mura indexes (MI) corresponding to a plurality of mura statuses of a plurality of first light guide plate (LGP) types, respectively; defining a plurality of film structures, wherein each of the film structures corresponds to each of mura indexes for mapping the mura indexes (MI) of the first LGP types with the film structures to construct a mapping database; and selecting one of the film structures and one of the mura indexes (MI) correspondingly from the mapping database for determining a critical dot dimension (CDD) of a second LGP type of the selected film structure. The selecting method avoids the mura, speed up the research and development procedure of the backlight module, labor cost and manufacturing cost when the LGP is assembled with the film structure.
    • 描述背光模块的导光板的选择方法。 所述选择方法包括以下步骤:分别计算与多个第一导光板(LGP)类型的多个mura状态对应的多个mura索引(MI); 定义多个胶片结构,其中每个胶片结构对应于每个mura索引,用于将第一LGP类型的mura索引(MI)与胶片结构映射以构建映射数据库; 以及从所述映射数据库相应地选择所述胶片结构之一和所述mura索引(MI)中的一个,以确定所选择的胶片结构的第二LGP类型的临界点尺寸(CDD)。 选择方法避免了光环,加快了背光模块的研发过程,人造成本和制造成本,当LGP与胶片结构组装时。
    • 39. 发明授权
    • System and method for adaptive pruning
    • 自适应修剪的系统和方法
    • US08301584B2
    • 2012-10-30
    • US10737123
    • 2003-12-16
    • Wei FanHaixun WangPhilip S. Yu
    • Wei FanHaixun WangPhilip S. Yu
    • G06F7/00G06F3/00
    • G06F17/30539G06F17/30598
    • Disclosed in a method and structure for searching data in databases using an ensemble of models. First the invention performs training. This training orders models within the ensemble in order of prediction accuracy and joins different numbers of models together to form sub-ensembles. The models are joined together in the sub-ensemble in the order of prediction accuracy. Next in the training process, the invention calculates confidence values of each of the sub-ensembles. The confidence is a measure of how closely results form the sub-ensemble will match results from the ensemble. The size of each of the sub-ensembles is variable depending upon the level of confidence, while, to the contrary, the size of the ensemble is fixed. After the training, the invention can make a prediction. First, the invention selects a sub-ensemble that meets a given level of confidence. As the level of confidence is raised, a sub-ensemble that has more models will be selected and as the level of confidence is lowered, a sub-ensemble that has fewer models will be selected. Finally, the invention applies the selected sub-ensemble, in place of the ensemble, to an example to make a prediction.
    • 公开了一种使用模型集合在数据库中搜索数据的方法和结构。 首先,发明执行训练。 这种训练按照预测精度的顺序对集合内的模型进行排序,并将不同数量的模型结合在一起形成子集合。 这些模型以预测精度的顺序连接在子集合中。 接下来在训练过程中,本发明计算每个子集合的置信度值。 信心是衡量子系统的结果与合奏结果相符的结果。 每个子集合的大小根据置信水平而变化,而相反,整体的大小是固定的。 训练后,本发明可以进行预测。 首先,本发明选择满足给定的置信水平的子集合。 随着信心的提高,将选择具有更多模型的子集合,并且随着置信度的降低,将选择具有较少模型的子集合。 最后,本发明将选择的子集合代替集合应用于一个例子进行预测。