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Ming Wanzhi
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[发明]
【中文】显示装置 【EN】Display device
申请号:
201911017221.9
公开号:CN111105708A 主分类号:G09F9/30
申请人:
【中文】三星显示有限公司【EN】Samsung Display Co.,Ltd.
申请日:2019.10.24 公开日:2020.05.05
发明人:
【中文】朴正镐
;
明万植
;
朴在春
;
刘正一【EN】Pu Zhenggao
;
Ming Wanzhi
;
Pu Zaichun
;
Liu Zhengyi
摘要:【中文】公开了显示装置。显示装置包括柔性模块、粘合膜、支承板、第一抗粘合膜图案和第二抗粘合膜图案,柔性模块包括显示面板;粘合膜布置在柔性模块的一个表面上;支承板布置在粘合膜上;第一抗粘合膜图案布置在支承板中的每个与粘合膜之间;并且第二抗粘合膜图案布置在支承板中的每个与粘合膜之间,并且与第一抗粘合膜图案间隔开。第一抗粘合膜图案和第二抗粘合膜图案中的每个包括金属材料,并且第一抗粘合膜图案和第二抗粘合膜图案中的每个具有在100nm至1000nm的范围内的厚度。 【EN】A display device is disclosed. The display device includes a flexible module, an adhesive film, a support plate, a first anti-adhesive film pattern, and a second anti-adhesive film pattern, the flexible module including a display panel; an adhesive film disposed on one surface of the flexible module; the support plate is disposed on the adhesive film; a first anti-adhesive film pattern is disposed between each of the support plates and the adhesive film; and a second anti-adhesive film pattern is disposed between each of the support plates and the adhesive film, and spaced apart from the first anti-adhesive film pattern. Each of the first and second anti-adhesive film patterns includes a metal material, and each of the first and second anti-adhesive film patterns has a thickness in a range of 100nm to 1000 nm.
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2:
[发明]
【中文】用于支撑显示面板的装置 【EN】Apparatus for supporting display panel
申请号:
202010082229.X
公开号:CN111273736A 主分类号:G06F1/16
申请人:
【中文】三星显示有限公司【EN】Samsung Display Co.,Ltd.
申请日:2015.11.11 公开日:2020.06.12
发明人:
【中文】李相月
;
明万植
;
朴象一
;
李相郁
;
李相徹【EN】Li Xiangyue
;
Ming Wanzhi
;
Pu Xiangyi
;
Li Xiangyu
;
Li Xiangche
摘要:【中文】本发明涉及一种用于支撑显示面板的装置,该用于支撑显示面板的装置包括:被配置为支撑显示面板的第一支撑件和第二支撑件,第一支撑件和第二支撑件可绕旋转轴线旋转;被联接到第一支撑件的侧部并被配置为与第一支撑件旋转的第一框架;以及被联接到第二支撑件的侧部并被配置为与第二支撑件旋转的第二框架,其中第一支撑件包括:具有被联接到第一框架的端部并平行于旋转轴线的第一支撑销;在第一支撑销的相对侧的第一支撑板和第二支撑板,其中第一支撑板被可枢轴转动地联接到第一支撑销。 【EN】The present invention relates to an apparatus for supporting a display panel, the apparatus for supporting a display panel comprising: a first support and a second support configured to support the display panel, the first support and the second support being rotatable about a rotation axis; a first frame coupled to a side of the first support and configured to rotate with the first support; and a second frame coupled to a side of the second support and configured to rotate with the second support, wherein the first support includes: a first support pin having an end coupled to the first frame and parallel to the rotation axis; a first support plate and a second support plate on opposite sides of the first support pin, wherein the first support plate is pivotably coupled to the first support pin.
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3:
[发明]
【中文】一种用于视网膜病变分类的卷积神经网络权值优化方法 【EN】Convolutional neural network weight optimization method for retinopathy classification
申请号:
201911127264.2
公开号:CN110929775A 主分类号:G06K9/62
申请人:
【中文】南通大学【EN】NANTONG University
申请日:2019.11.18 公开日:2020.03.27
发明人:
【中文】丁卫平
;
任龙杰
;
孙颖
;
鞠恒荣
;
丁帅荣
;
曹金鑫
;
张毅
;
冯志豪
;
李铭
;
文万志
;
胡彬
;
赵理莉【EN】Ding Weiping
;
Ren Longjie
;
Sun Ying
;
Ju Hengrong
;
Ding Shuairong
;
Cao Jinxin
;
Zhang Yi
;
Feng Zhihao
;
Li Ming
;
Wen Wanzhi
;
Hu Bin
;
Zhao Lili
摘要:【中文】本发明涉及到医学信息智能处理领域,具体来说涉及一种用于视网膜病变分类的卷积神经网络权值优化方法。该方法首先获取眼底图像训练集、及其对应的多病变标签;通过单种群蛙跳算法寻找最优初始权值,然后构建卷积神经网络中的卷积层、池化层和全连接层,将最优初始权值作为第一次前向传播计算的参数;将视网膜中四种病变的四个预测值分别与真实值进行交叉熵损失计算并求和得到损失值,判断损失值是否异常,如果异常则围绕前一次前向传播的权值生成蛙群,寻找最优蛙更新网络权值;否则采用梯度下降算法更新网络权值;最后对最终权值进行优化。本发明能有效提高眼底图像多病变检测的准确率,对视网膜疾病和辅助治疗具有较强应用价值。 【EN】The invention relates to the field of medical information intelligent processing, in particular to a convolutional neural network weight optimization method for retinopathy classification. Firstly, acquiring a fundus image training set and a multi-lesion label corresponding to the fundus image training set; searching an optimal initial weight through a single swarm leaping algorithm, then constructing a convolution layer, a pooling layer and a full-link layer in a convolutional neural network, and taking the optimal initial weight as a parameter for the first forward propagation calculation; respectively carrying out cross entropy loss calculation on four predicted values of four pathological changes in retina and a true value, summing to obtain a loss value, judging whether the loss value is abnormal, if so, generating a frog group around a previous forward propagation weight, and searching for an optimal frog updating network weight; otherwise, updating the network weight by adopting a gradient descent algorithm; and finally optimizing the final weight. The invention can effectively improve the accuracy of fundus image multi-lesion detection and has stronger application value to retinal diseases and adjuvant therapy.
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