基于自主学习与SCAD-Net正则化的回归模型

众多基因生物标志物选择方法常因研究样本较少而不能直接用于临床诊断.于是有学者提出整合不同基因表达数据同时保留生物信息完整性的方法.然而,由于存在批量效应,导致直接整合不同基因表达数据可能会增加新的系统误差.针对上述问题,提出一个融合自主学习与SCAD-Net正则化的分析框架.一方面,自主学习方法能够先从低噪声样本中学习出基础模型,然后再通过高噪声样本学习使得模型更加稳健,从而避免批量效应;另一方面,SCAD-Net正则化融合了基因表达数据与基因间的交互信息,可以实现更好的特征选择效果.不同情形下的模拟数据以及在乳腺癌细胞系数据集上的结果表明,基于自主学习与SCAD-Net正则化的回归模型在处理高维复杂网络数据集时具有更好的预测效果.     

Intelligent fault diagnosis for rolling bearings based on graph shift regularization with directed graphs

Graph shift regularization is a new and effective graph-based semi-supervised classification method, but its performance is closely related to the representation graphs. Since directed graphs can convey more information about the relationship between vertices than undirected graphs, an intelligent method called graph shift regularization with directed graphs (GSR-D) is presented for fault diagnosis of rolling bearings. For greatly improving the diagnosis performance of GSR-D, a directed and weighted k-nearest neighbor graph is first constructed by treating each sample (i.e., each vibration signal segment) as a vertex, in which the similarity between samples is measured by cosine distance instead of the commonly used Euclidean distance, and the edge weights are also defined by cosine distance instead of the commonly used heat kernel. Then, the labels of samples are considered as the graph signals indexed by the vertices of the representation graph. Finally, the states of unlabeled samples are predicted by finding a graph signal that has minimal total variation and satisfies the constraint given by labeled samples as much as possible. Experimental results indicate that GSR-D is better and more stable than the standard convolutional neural network and support vector machine in rolling bearing fault diagnosis, and GSR-D only has two tuning parameters with certain robustness.     

双向航道集装箱港口船舶调度优化算法

针对拥有双向航道的集装箱港口中船舶进出港所遇到的会遇和追越等问题,提出了一种重点考虑服务规则的新型船舶调度优化算法。首先,同时考虑双向航道的现实约束和港口夜航的安全规定;然后,构建了以所有船舶在港总等待时间最小为目标的混合整数规划模型来得出最佳的船舶进出港次序;最后,设计了嵌入聚合策略的分支切割算法对模型进行求解。通过数值实验可知,运用嵌入聚合策略的分支切割算法所得结果与下界值的平均相对偏差为2.59%。同时,与模拟退火算法与量子差分进化算法的对比结果表明,所提的分支切割算法所得的目标函数值相较于两个对比算法所得目标函数值分别减少了23.56%和17.17%,验证了该算法的有效性。在用所提算法得到方案的敏感性分析中比较了不同抵港安全时间间隔和船舶类型比例对方案结果的影响,为双向航道集装箱港口的船舶调度优化提供了决策支持。     

基于联合损失胶囊网络的换衣行人重识别

目前的行人重识别（Re-ID）研究主要集中在短时间情形,即一个人的衣着不太可能发生改变的情况。然而现实中更常见的是长时间的情况,这时一个人有很大的机会更换衣服,Re-ID模型应该考虑这种情况。为此,研究了一种基于联合损失胶囊网络的换衣行人重识别方法。所提方法基于换衣行人重识别胶囊网络ReIDCaps,使用与传统的标量神经元相比包含更多信息的矢量胶囊,用其长度表示行人身份信息,用其方向表示行人衣着信息;采用软嵌入注意力（SEA）防止模型过拟合;使用特征稀疏表示（FSR）机制提取具有判别性的特征;增加标签平滑正则化交叉熵损失与Circle Loss的联合损失以提高模型的泛化能力和鲁棒性。在三个换衣行人重识别数据集Celeb-reID、Celeb-reID-light和NKUP上进行实验,实验结果表明所提方法与目前已有的Re-ID方法相比具有一定优势。     

采用自适应字典学习的InSAR降噪方法

We consider the phase noise filtering problem for interferometric synthetic aperture radar (InSAR) based on the dictionary learning technique. Due to the non-convexity of the optimization problem is difficult to solve. By using the splitting technique and employing the augmented Lagrangian framework, we obtain a relaxed nonlinear constraint optimization problem with l₁-norm regularization which can be solved efficiently by the alternating direction method of multipliers (ADMM). Specifically, we firstly train dictionaries from the InSAR complex phase data, and then reconstruct the desired complex phase image from the sparse representation. Simulation results based on simulated and measured data show that this new InSAR phase noise reduction method has a much better performance than several classical phase filtering methods in terms of residual count, mean square error (MSE) and preservation of the fringe completeness.     

双向自支撑叠合板受力破坏机理及试验研究

随着我国新型建筑工业化的推进,作为工业化主要构件的叠合板得到了大力的发展,但是,多数叠合板拼缝处的钢筋构造复杂,生产、施工较为困难,为此,针对一种简单拼缝构造方式的双向自支撑叠合板进行了全过程受力机理研究,同时对该种叠合板进行了足尺试验,并与同样大小的双向现浇板进行了试验对比,结果表明,该种叠合板的破坏形态与现浇板基本相同。在此基础上,结合数值模拟技术,着重研究了该种叠合板的双向受力机理,裂缝开展及分布,拼缝处钢筋的粘结滑移及传力性能。并系统地对该种叠合板在多种情况下的破坏机理进行了分析,为该种叠合板的设计和应用提供了很多有益的参考。     

Two-way Markov random walk transductive learning algorithm简

Researchers face many class prediction challenges stemming from a small size of training data vis-a-vis a large number of unlabeled samples to be predicted. Transductive learning is proposed to utilize information about unlabeled data to estimate labels of the unlabeled data for this condition. This work presents a new transductive learning method called two-way Markov random walk （TMRW） algorithm. The algorithm uses information about labeled and unlabeled data to predict the labels of the unlabeled data by taking random walks between the labeled and unlabeled data where data points are viewed as nodes of a graph. The labeled points correlate to unlabeled points and vice versa according to a transition probability matrix. We can get the predicted labels of unlabeled samples by combining the results of the two-way walks. Finally, ensemble learning is combined with transductive learning, and Adboost.MH is taken as the study framework to improve the performance of TMRW, which is the basic learner. Experiments show that this algorithm can predict labels of unlabeled data well.     

叠合式双向板试验与数值模拟对比分析

针对传统双向板与两拼、三拼叠合式双向板用ABAQUS进行有限元非线性模拟分析,对比这三种双向板的挠度、钢筋应变以及裂缝开展等方面差别。结果表明与传统双向板比较这种两拼叠合式双向板在受力性能上基本相同,最后总结两拼叠合式双向板在受力时的状态,并提出改进建议以及还需解决的问题,从而为叠合式剪力墙板结构技术规程的完善提供理论依据,为其能够广泛应用于高层建筑的施工建设提供技术支持和理论支撑。     

CFRP 布加固钢筋混凝土双向板抗弯性能分析

选取实际工程中CFRP布加固双向板的实例,利用ANSYS有限元软件建立加固模型,通过逐级递增施加楼面活荷载,对比分析了对比板与加固板的跨中挠度、钢筋应力及承载力,研究了在CFRP布加固方式不同、加固厚度不同及加固量不同时加固板力学性能的规律。结果表明：与对比板相比,CFRP布加固的双向板能有效提高抗弯承载力,减小挠度;贴布面积相同时,分散“＃”字型铺贴加固板效果优于密集“＃”字型和“＋”字型的;适当增加CFRP布粘贴厚度,在原有加固方式基础上增加加固量,均有利于提高板的抗弯承载力;CFRP布宽度增加,有利于提高板的抗弯承载力,但不利于CFRP布强度的发挥。     

双向纤维增强复合材料拉伸试验研究

纤维增强复合材料具有轻质、高强、耐腐蚀等优点,广泛应用于工程结构加固中。针对双向纤维增强复合材料的特点设计单轴拉伸试验装置,对双向纤维增强复合材料进行拉伸试验,研究破坏特征、抗拉强度、弹性模量及伸长率等,分析双向纤维增强复合材料力学性能的影响因素。试验表明,纤维原丝的抗拉强度不可直接作为编织纤维增强复合材料抗拉强度的设计值用于加固设计,其破坏表现出明显的脆性断裂,而且纤维材料的种类、纤维材料的制作工艺对纤维增强复合材料的力学性能具有一定的影响。