Static stress analysis of carbon nano-tube reinforced composite (CNTRC) cylinder under non-axisymmetric thermo-mechanical loads and uniform electro-magnetic fields

Static stresses analysis of carbon nano-tube reinforced composite (CNTRC) cylinder made of poly-vinylidene fluoride (PVDF) is investigated in this study. Non-axisymmetric thermo-mechanical loads are applied on cylinder in presence of uniform longitudinal magnetic field and radial electric field. The surrounded elastic medium is modeled by Pasternak foundation because of its advantages to the Winkler type. Distribution of radial, circumferential and effective stresses, temperature field and electric displacements in CNTRC cylinder are determined based on Mori–Tanaka theory. The detailed parametric study is conducted, focusing on the remarkable effects of magnetic field intensity, elastic medium, angle orientation and volume fraction of carbon nano-tubes (CNTs) on distribution of effective stress. Results demonstrated that fatigue life of CNTRC cylinder will be significantly dependent on magnetic intensity, angle orientation and volume fraction of CNTs. Results of this research can be used for optimum design of thick-walled cylinders under multi-physical fields.     

A proposed expert system for word sense disambiguation: deductive ambiguity resolution based on data mining and forward chaining

One of the major issues in the process of machine translation is the problem of choosing the proper translation for a multi‐sense word referred to as word sense disambiguation (WSD). Two commonly used approaches to this problem are statistical and example‐based methods. In statistical methods, ambiguity resolution is mostly carried out by making use of some statistics extracted from previously translated documents or dual corpora of source and target languages. Example‐based methods follow a similar approach as they also make use of bilingual corpora. However, they perform the task of matching at run‐time (i.e. online matching). In this paper, by looking at the WSD problem from a different viewpoint, we propose a system, which consists of two main parts. The first part includes a data mining algorithm, which runs offline and extracts some useful knowledge about the co‐occurrences of the words. In this algorithm, each sentence is imagined as a transaction in Market Basket Data Analysis problem, and the words included in a sentence play the role of purchased items. The second part of the system is an expert system whose knowledge base consists of the set of association rules generated by the first part. Moreover, in order to deduce the correct senses of the words, we introduce an efficient algorithm based on forward chaining in order to be used in the inference engine of the proposed expert system. The encouraging performance of the system in terms of precision and recall as well as its efficiency will be analysed and discussed through a set of experiments.     

Structural performance of ballastless track slabs reinforced with BFRP and SFCB

Because of the inductive impedance caused by steel meshes in traditional reinforced ballastless track slabs, the electrical properties, primarily the rail resistance and inductance, of jointless track circuits are affected by electromagnetic induction between the slabs and the electric current in the rail. This problem results in poor transmission performance throughout the track circuit. Insulating sleeves or cards between the steel meshes have been used to improve the insulation capability of steel meshes in slabs; however, they reduce the bonding performance between the steel bars and concrete. Because of the good insulation properties of fiber-reinforced polymer composite bars (FRPs) and steel-fiber reinforced polymer composite bars (SFCBs), these composite materials have shown potential to overcome this insulation problem. However, the structural performance of the ballastless track slabs reinforced by basalt fiber reinforced polymer composite bars (BFRPs) and SFCBs, which play a key role in the structure and transportation safety, needs to be investigated. In this paper, six ballastless track slabs reinforced with BFRPs, SFCBs, and steel bars were constructed and tested. The following results were obtained. (1) Shear failures were observed for all slabs, both the BFRP and SFCB slabs meet the load level requirements, and SFCBs reinforcements have higher strength utilization compared with BFRPs reinforcements. (2) The bond-quality of SFCBs and BFRPs reinforcements proved slightly poorer than that of the steel bars. Because of the good corrosion resistance of the FRP, the maximum crack width limits can be slightly larger than that of the RC slabs. (3) Bischoff’s equation was initially used to calculate the deflection of partially prestressed concrete slabs under service loads. The results demonstrated a good agreement between the theoretical and experimental analysis. (4) Considering the tensile stiffness, the modified ACI equation was used to calculate the slabs’ crack width and the theoretical and experimental results showed a good agreement.     

Automatic brain tissue segmentation in MR images using hybrid atlas forest based on confidence-weighted probability matrix

The segmentation of specific tissues in an MR brain image for quantitative analysis can assist the disease diagnosis and medical research. Therefore, a robust and accurate method for automatic segmentation is necessary. Atlas-based-method is a common and effective method of automatic segmentation where an atlas refers to a pair of image consist of an intensity image and its corresponding label image. Apart from the general multi-atlas-based methods, which propagate labels through the single atlas then fuse them, we proposed a hybrid atlas forest based on confidence-weighted probability matrix to consider the atlases set as a whole and treat each voxel differently. In the framework, we first register the atlas to the image space of target and calculate the confidence of voxels in the registered atlas. Then, a confidence-weighted probability matrix is generated and it augments to the intensity image of the atlas or target for providing spatial information of the target tissue. Third, a hybrid atlas forest is trained to gather the features and correlation information among the atlases in the dataset. Finally, the segmentation of the target tissues is predicted by the trained hybrid atlas forest. The segment performance and the components efficiency of the proposed method are evaluated on the two public datasets. Based on the experiment results and quantitative comparisons, our method can gather spatial information and correlation among the atlases to obtain an accurate segmentation.     

Grape stalk fibers as reinforcing filler for polymer composites with a polystyrene matrix

Lignocellulosic fibers have been one of the most used reinforcements for different types of composites. In this context, grape stalks—often discarded in landfills—can be a potential reinforcement for composites. The present study aimed to incorporate in natura and alkali treated grape stalks into the polystyrene (PS) matrix. Initially, the grape stalks were treated with NaOH and 10, 20, and 30 wt % of stalks were added into the PS matrix. The alkaline treatment of the grape stalk fibers promoted the removal of hemicellulose and the partial removal of lignin, thus increasing the surface roughness and crystallinity index of the fibers. An improvement in the mechanical and dynamic-mechanical properties and an increase in the maximum degradation temperature of the composites were observed. In conclusion, the use of grape stalks is a promising alternative for reinforcing composites, and, in addition, a more proper destination can be given to this type of residue. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 47427.     

基于双向位图的CSR大规模图存储优化

大数据时代,Graph500是评测超级计算机处理数据密集型应用能力的重要工具,E级验证系统的图遍历处理能力主要受限于内存空间和访存带宽,尤其是内存空间利用率直接决定了图的测试规模和测试性能.针对天河E级验证系统小内存特征,提出了基于双向位图的大规模图数据压缩存储方法(bidirectional-bitmap based CSR,Bi-CSR),Bi-CSR在CSR矩阵压缩的基础上引入行方向位图和列方向位图协同完成稀疏矩阵压缩存储,行方向位图主要负责行方向位图的压缩存储与索引,列方向位图除了进一步压缩图存储空间,还负责为顶点遍历向量并行优化提供加速空间.Bi-CSR大幅度减少了稀疏矩阵存储空间.面向天河E级验证系统,当图输入规模为237时,Graph500的图存储空间节约效率接近70%,全系统稳定测试性能为2.131E+12TEPS,性能最大加速比超过100倍.     

基于聚类的数据加权优化在犯罪预测中的应用

近年来，我国传统暴力犯罪与成年人犯罪呈下降态势，但是，犯罪案由层出不穷。为有效提升公安实践工作中犯罪预测能力，打击各类违法犯罪事件,本文针对犯罪数据，提出一种新型犯罪预测模型。利用密度聚类分析方法将犯罪数据分类，然后进行数据降维提取关键属性生成特征数据，继而对特征数据进行加权优化并采用机器学习的方式对特征数据进行学习，从而预测犯罪案由。实验结果表明，与传统方法相比，本文方法具有更好的预测效果，为公安实践工作中类似案件的侦破和预防，提供新的路径支撑。     

一种基于压缩感知的运动目标检测技术

运动目标检测是计算机视觉、图像处理等相关领域的研究热点，其核心就是对视频图像中的每一帧图像进行相应的研究和处理。本文主要研究思路是从压缩感知技术采样信号的角度出发，将每一帧的二维图像压缩采样成具有少量信息的一维信号，再通过信号重构用少量数据量将图像重构出来，最后通过目标检测技术对每一帧的图像进行运动目标提取。仿真实验表明该方法是可行和有效的，同时可以大大减少目标检测中所记录的数据量，解决海量数据的存储与传输问题。     

长期演进系统基于加权平均时延的下行资源调度

针对长期演进（LTE）移动通信系统下行链路传输中多用户的实时（RT）与非实时（NRT）业务传输性能需求问题，提出一种基于用户加权平均时延的改进型的最大加权延时优先（MLWDF）资源调度算法。该算法在考虑信道感知与用户服务质量（QoS）感知的基础上引入反映用户缓冲区状态的加权平均时延因子，该因子通过用户缓冲区中待传输数据与已发送数据的平均时延均衡得到，使具有较大时延和业务量的实时业务优先调度，提升了用户的性能体验。理论分析与链路仿真表明，提出算法在保证各业务时延及公平性的基础上，提升了实时业务的QoS性能，在用户数量达到50的条件下，对比MLWDF算法实时业务的丢包率降低了53.2%，其用户平均吞吐量提升了44.7%，虽牺牲了非实时业务的吞吐量，但仍优于VT-MLWDF算法。实验结果表明，所提算法在多用户多业务传输条件下提升了实时业务的传输性能，并在QoS性能上明显优于对比算法。     

结构化加权稀疏低秩恢复算法在人脸识别中的应用

针对训练样本或测试样本存在污损的情况，提出一种结构化加权稀疏低秩恢复算法（structured and weighted-sparse low rank representation，SWLRR）。SWLRR对低秩表示进行加权稀疏约束和结构化约束，使得低秩表示系数更加趋近于块对角结构，进而可获得具有判别性的低秩表示。SWLRR将训练样本恢复成干净训练样本后，再根据原始训练样本和恢复后的训练样本学习到低秩投影矩阵，把测试样本投影到相应的低秩子空间，即可有效地去除测试样本中的污损部分。在几个人脸数据库上的实验结果验证了SWLRR在不同情况下的有效性和鲁棒性。