研究岩石破裂等弱化行为的力－位移组合控制方法

本文介绍了一种用力和位移的线性组合控制岩石试验的新方法。该方法已在一台自制的伺服实验系统上和一台MTS实验系统上得到了实现。用这种方法能够简便地求出通常难以求出的具有Ⅱ类特性岩石的全应力-应变曲线,而且能够变化出如广义应力松驰等其它实验类型,从而能够对时间依赖性进行深入的研究。由于其易于采用的特点,它在研究岩石破裂乃至摩擦等弱化过程方面有着广泛的应用前景。     

地震作用下基于ADMF和系统参数组合的最优MTMD

多重调谐质量阻尼器 ( MTMD)是由许多频率成线性分布的调谐质量阻尼器组成。可能的系统参数组合形成 5种 MTMD,即 MTMD-1～MTMD-5。基于在基底加速度作用下具有一般 MTMD时结构的加速度传递函数 ,建立了 MTMD-1～ MTMD-5加速度动力放大系数 ( ADMF)的统一模式。利用 ADMF和数值寻优技术进行了详细的最优参数研究。最优参数包括 :最优频率间隔、最优阻尼比、最优调谐频率比。大量的数值比较表明 :在MTMD地震反应控制工程中 ,应优先选择 MTMD-1。     

西安地铁二号线沿线地裂缝未来位错量估算及工程分级

地裂缝在地铁设计使用期内的最大垂直位错量是西安地铁二号线穿越地裂缝结构设计的一个十分重要的参数。本文以历史水准监测资料为基础,分析了西安地铁二号线沿线各地裂缝在不同历史阶段的活动特征与活动原因,对各地裂缝的未来活动趋势进行了预测。然后通过基于不同时间段地裂缝活动速率的最大垂直位错量估算结果的对比分析,得出了地铁设计使用期内各条地裂缝与地铁交汇点处地裂缝的最大垂直位错量,并以此为依据,将西安地铁二号线沿线地裂缝分为Ⅰ、Ⅱ、Ⅲ和Ⅳ级等4个工程级别。     

基于U-Net的盐体识别方法

卷积神经网络在计算机视觉领域取得重大突破，利用其强大的图像处理能力，将地下沉积盐体的识别问题转化为图像语义分割问题，应用深度卷积神经网络实现盐体地震图像的像素级语义分割.本文在U-Net基础上，增加网络深度并同时引入批归一化和Dropout处理，使得神经网络模型具有更高的可信度和更强的泛化能力.通过实验发现，在卷积层之后引入批归一化处理，并在池化层和叠加层之后引入Dropout可以稳定提升模型对盐体图像的分割性能.     

Sparse Multicategory Generalized Distance Weighted Discrimination in Ultra-High Dimensions

Distance weighted discrimination (DWD) is an appealing classification method that is capable of overcoming data piling problems in high-dimensional settings. Especially when various sparsity structures are assumed in these settings, variable selection in multicategory classification poses great challenges. In this paper, we propose a multicategory generalized DWD (MgDWD) method that maintains intrinsic variable group structures during selection using a sparse group lasso penalty. Theoretically, we derive minimizer uniqueness for the penalized MgDWD loss function and consistency properties for the proposed classifier. We further develop an efficient algorithm based on the proximal operator to solve the optimization problem. The performance of MgDWD is evaluated using finite sample simulations and miRNA data from an HIV study.     

Some birationality criteria on 3-folds with p_g>1 Dedicated to the memory of Professor XIAO Gang

We give some birationality criteria for m(m = 4,5,6,7) on general type 3-folds with pg 2 by means of an intensive classification. In particular,we show that 7 is not birational if and only if pg(X) = 2and X admits a genus 2 curve family of canonical degree23. When the canonical volume is large,we also characterize the birationality of 4,5 and 6.     

Automatic hierarchy classification in venation networks using directional morphological filtering for hierarchical structure traits extraction

The extraction of vein traits from venation networks is of great significance to the development of a variety of research fields, such as evolutionary biology. However, traditional studies normally target to the extraction of reticulate structure traits (ReSTs), which is not sufficient enough to distinguish the difference between vein orders. For hierarchical structure traits (HiSTs), only a few tools have made attempts with human assistance, and obviously are not practical for large-scale traits extraction. Thus, there is a necessity to develop the method of automated vein hierarchy classification, raising a new challenge yet to be addressed. We propose a novel vein hierarchy classification method based on directional morphological filtering to automatically classify vein orders. Different from traditional methods, our method classify vein orders from highly dense venation networks for the extraction of traits with ecological significance. To the best of our knowledge, this is the first attempt to automatically classify vein hierarchy. To evaluate the performance of our method, we prepare a soybean transmission image dataset (STID) composed of 1200 soybean leaf images and the vein orders of these leaves are manually coarsely annotated by experts as ground truth. We apply our method to classify vein orders of each leaf in the dataset. Compared with ground truth, the proposed method achieves great performance, while the average deviation on major vein is less than 5 pixels and the average completeness on second-order veins reaches 54.28%.     

Versatile cobalt complexes for initiating immortal ring‐opening polymerization (ROP) of lactide (LA), mediating living radical polymerization of t‐butyl acrylate (tBA) and catalyzing copolymerization of LA and tBA by combination of ROP and organometallic‐mediated radical polymerization

Low‐cost, highly active and versatile amino‐bis(phenolate) cobalt complexes are developed. The cobalt complexes can control living polymerization of different categories of monomers including lactide (LA) by immortal ring‐opening polymerization in argon and even in air and acrylate via living radical polymerization (LRP). The cobalt‐based catalysts were used for copolymerization of LA and acrylate. The immortal polymerization of LA using the cobalt complexes as initiators proceeds in argon and even in air and without the requirement for extensive drying techniques or inert atmosphere whilst retaining end‐group fidelity. The cobalt complexes are used to mediate LRP of t‐butyl acrylate (tBA) in methanol. The block copolymerization of LA and tBA catalyzed by single‐site cobalt organometallic catalyst is also reported for the first time. This cobalt system offers a versatile and green way to produce homopolymers and block copolymers.     

A combined approach of Kullback–Leibler divergence and background subtraction for moving object detection in thermal video

In this work, a robust method for moving object detection in thermal video frames has been proposed by including Kullback–Leibler divergence (KLD) based threshold and background subtraction (BGS) technique. A trimmed-mean based background model has been developed that is capable enough to reduce noise or dynamic component of the background. This work assumed that each pixel has normally distributed. The KLD has computed between background pixel and a current pixel with the help of Gaussian mixture model. The proposed threshold is useful enough to classify the state of each pixel. The post-processing step uses morphological tool for edge linking, and then the flood-fill algorithm has applied for hole-filling, and finally the silhouette of targeted object has generated. The proposed methods run faster and have validated over various real-time based problematic thermal video sequences. In the experimental results, the average value of F₁-score, area under the curve, the percentage of correct classification, Matthew’s correlation coefficient show higher values whereas total error and percentage of the wrong classification show minimum values. Moreover, the proposed-1 method achieved higher accuracy and execution speed with minimum false alarm rate that has been compared with proposed-2 as well as considered peer methods in the real-time thermal video.     

SINGLE AND DOUBLE EDGE INTERFACE CRACK SOLUTIONS FOR ARBITRARY FORMS OF MATERIAL COMBINATION

In this paper interfacial edge crack problems are considered by the application of the finite element method. The stress intensity factors are accurately determined from the ratio of crack-tip-stress value between the target given unknown and reference problems. The reference problem is chosen to produce the singular stress fields proportional to those of the given unknown problem. Here the original proportional method is improved through utilizing very refined meshes and post-processing technique of linear extrapolation. The results for a double-edge interface crack in a bonded strip are newly obtained and compared with those of a single-edge interface crack for different forms of combination of material. It is found that the stress intensity factors should be compared in the three different zones of relative crack lengths. Different from the case of a cracked homogeneous strip, the results for the double edge interface cracks are found to possibly be bigger than those for a single edge interface crack under the same relative crack length.