排序方式： 共有2条查询结果，搜索用时 0 毫秒
1
1.
图像相似度学习是指通过网络学习图像内容信息来预测两张图像是否匹配。迄今为止，基于卷积神经网络改进的变体网络有效提升了学习效率，但由于提取特征比较单一无法准确描述图像特征，导致相似度学习效率较低。为此，本文提出一种基于卷积神经网络结构的双分支网络。该网络为左右分支网络结构相同，但权值不共享，网络输入为双分支输入。首先由左右分支网络分别提取单通道图像特征；然后通过特征融合层进行特征融合；最后将融合特征直接输入全连接层进行相似度学习，既改善了提取的图像特征多样性，又加快了模型训练速度。在实验室工业相机拍摄的芯片卡槽图像数据集上进行对比试验，结果表明，相比其他模型，本文提出的模型具有较强的网络学习能力和模型泛化能力，准确率高达97.96%。 相似文献
2.
Two branches of Tangjiagou rock avalanche were triggered by Lushan earthquake in Sichuan Province, China on April 20th, 2013. The rock avalanche has transported about 1 500 000 m3 of sandstone from the source area. Based on discrete element modeling, this study simulates the deformation, failure and movement process of the rock avalanche. Under seismic loading, the mechanism and process of deformation, failure, and runout of the two branches are similar. In detail, the stress concentration occur firstly on the top of the mountain ridge, and accordingly, the tensile deformation appears. With the increase of seismic loading, the strain concentration zone extends in the forward and backward directions along the slipping surface, forming a locking segment. As a result, the slipping surface penetrates and the slide mass begin to slide down with high speed. Finally, the avalanche accumulates in the downstream and forms a small barrier lake. Modeling shows that a number of rocks on the surface exhibit patterns of horizontal throwing and vertical jumping under strong ground shaking. We suggest that the movement of the rock avalanche is a complicated process with multiple stages, including formation of the two branches, high-speed sliding, transformation into debris flows, further movement and collision, accumulation, and the final steady state. Topographic amplification effects are also revealed based on acceleration and velocity of special monitoring points. The horizontal and vertical runout distances of the surface materials are much greater than those of the internal materials. Besides, the sliding duration is also longer than that of the internal rock mass. 相似文献
1