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101.
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103.
为研究古建筑木结构透榫节点的M-θ力学模型,在分析透榫节点构造特征与受力机理的基础上,建立其数值模型,用透榫节点的试验数据验证了该数值模型的正确性,并分析了节点缝隙、木材横纹弹性模量和大榫头长度对透榫节点受弯承载力的影响。根据受力分析结果,建立以弹性点、屈服点与极限点为特征点的三折线多参数M-θ力学模型,其结果与多数的试验结果基本吻合,并将该力学模型应用于木构架的受力分析。研究结果表明:透榫节点的滞回耗能能力强,节点的变形主要集中在榫头处。当榫头与卯口之间的缝隙增大时,节点的受弯承载力降低。随木材横纹弹性模量的提高和大榫头长度的增加,节点的受弯承载力有一定提高。文章建立的M-θ力学模型能较好反映透榫节点的受力过程,适用于木构架的受力分析,其荷载 位移骨架曲线与试验结果基本吻合。研究成果可为古建筑木结构的维修与保护提供参考。 相似文献
104.
105.
WeiJia Luo LingXia Li Shihui Yu Qianyu Guo Bowen Zhang Zheng Sun 《Ceramics International》2018,44(11):12414-12419
Low-loss (Zn1-xNix)ZrNbTaO8 (0.02?≤?x?≤?0.10) ceramics possessing single wolframite structure are initiatively synthesized by solid-state route. Based on the results of Rietveld refinement, complex chemical bond theory is used to establish the correlation between structural characteristics and microwave performance in this ceramic system. A small amount of Ni2+ (x?=?0.06) in A-site with the fixed substitution of Ta5+ in B-site can effectually raise the Q?×?f value of ZnZrNb2O8 ceramic, embodying a dense microstructure and high lattice energy. The dielectric constant and τf are mainly affected by bond ionicity and the average octahedral distortion. The (Zn0.94Ni0.06)ZrNbTaO8 ceramic sample sintered at 1150?°C for 3?h exhibits an outstanding combination of microwave dielectric properties: εr =?27.88, Q?×?f?=?128,951?GHz, τf =?–39.9?ppm/°C. Thus, it is considered to be a candidate material for the communication device applications at high frequency. 相似文献
106.
The application of deep learning in the field of object detection has
experienced much progress. However, due to the domain shift problem, applying an
off-the-shelf detector to another domain leads to a significant performance drop. A
large number of ground truth labels are required when using another domain to train
models, demanding a large amount of human and financial resources. In order to avoid
excessive resource requirements and performance drop caused by domain shift, this
paper proposes a new domain adaptive approach to cross-domain vehicle detection. Our
approach improves the cross-domain vehicle detection model from image space and
feature space. We employ objectives of the generative adversarial network and cycle
consistency loss for image style transfer in image space. For feature space, we align
feature distributions between the source domain and the target domain to improve the
detection accuracy. Experiments are carried out using the method with two different
datasets, proving that this technique effectively improves the accuracy of vehicle
detection in the target domain. 相似文献
108.
Neural Machine Translation (NMT) is an end-to-end learning approach for
automated translation, overcoming the weaknesses of conventional phrase-based translation
systems. Although NMT based systems have gained their popularity in commercial
translation applications, there is still plenty of room for improvement. Being the most
popular search algorithm in NMT, beam search is vital to the translation result. However,
traditional beam search can produce duplicate or missing translation due to its target
sequence selection strategy. Aiming to alleviate this problem, this paper proposed neural
machine translation improvements based on a novel beam search evaluation function. And
we use reinforcement learning to train a translation evaluation system to select better
candidate words for generating translations. In the experiments, we conducted extensive
experiments to evaluate our methods. CASIA corpus and the 1,000,000 pairs of bilingual
corpora of NiuTrans are used in our experiments. The experiment results prove that the
proposed methods can effectively improve the English to Chinese translation quality. 相似文献
109.