Predicting the mechanical properties of Cu–Al2O3 nanocomposites using machine learning and finite element simulation of indentation experiments

Micromechanics model, finite element (FE) simulation of microindentation and machine learning were deployed to predict the mechanical properties of Cu–Al₂O₃ nanocomposites. The micromechanical model was developed based on the rule of mixture and grain and grain boundary sizes evolution to predict the elastic modulus of the produced nanocomposites. Then, a FE model was developed to simulate the microindentation test. The input for the FE model was the elastic modulus that was computed using the micromechanics model and wide range of yield and tangent stresses values. Finally, the output load-displacement response from the FE model, the elastic modulus, the yield and tangent strengths used for the FE simulations, and the residual indentation depth were used to train the machine learning model (Random vector functional link network) for the prediction of the yield and tangent stresses of the produced nanocomposites. Cu–Al₂O₃ nanocomposites with different Al₂O₃ concentration were manufactured using insitu chemical method to validate the proposed model. After training the model, the microindentation experimental load-displacement curve for Cu–Al₂O₃ nanocomposites was fed to the machine learning model and the mechanical properties were obtained. The obtained mechanical properties were in very good agreement with the experimental ones achieving 0.99 coefficient of determination R2 for the yield strength.     

TSL:predicting popularity of Facebook content based on tie strength

The rapid development of online social networks leads to an explosion of information,however,there are great differences in the popularity of different messages,and accurate prediction is always a great difficulty is the current study.Popularity prediction of online content aims to predict the popularity in the future based on its early diffusion status.Existing models for popularity prediction were mostly based on discovering network features or fitting the equation into a varying time function that the accuracy of current popularity prediction model was not high enough.Therefore,with the help of the weak ties theory in sociology,the concept of tie strength was introduced and a multilinear regression equation was constructed combined with the early popularity.A TSL model to predict the popularity of Facebook’s well-known pages was proposed.The main contribution of this article was to solve the problem and few or no work based on sociology.A high linear correlation between the proportion of faithful fans was existed in Facebook homepage with frequent shares in the early and the future popularity.Compared with other baseline models,an experimental study of Facebook (including 1.54 million shares) illustrates the effectiveness of the proposed TSL model,and the performance is better than the existing similar methods.     

基于边图的线性流重叠社区发现算法

重叠网络的社区发现是复杂网络研究中的重要问题。为了提高网络中重叠社区发现的时间效率，提出一种基于边图的线性流重叠社区发现算法LBSA。算法首先对于边图网络中的边进行随机的依次处理，完成节点的初步社区划分，再将其中重叠小社区合并到相似度最大的其他大社区中得到最终的社区。通过以上步骤，算法能够以接近线性的时间复杂度得到网络的重叠结构。从最终的实验结果来看，与其他算法相比，该算法能够在更短的时间有质量地发现网络中的重叠社区。     

改进ILoG算子的故障检测方法

针对强背景噪声干扰下轮对轴承故障特征微弱、难以准确检测的问题,提出了一种自适应改进高斯拉普拉斯(improved Laplacian of Gaussian,简称ILoG)算子的微弱故障检测方法。ILoG算子滤波器具有优良的信号突变特征检测能力,将其用于轮对轴承故障信号的冲击特征检测,同时利用水循环算法(water cycle algorithm,简称WCA)的寻优特性,并行搜寻筛选最佳的ILoG算子影响参数,通过对参数优化后ILoG算子滤波后信号做进一步包络解调分析,提取出轮对轴承微弱的故障特征信息。对实际轮对轴承外圈和内圈故障信号分析的结果表明,该方法可以有效检测出轴承微弱故障特征频率,故障检测效果优于小波阈值和多尺度形态学差值滤波方法。     

基于链接相似度和作弊系数的Spam网页识别算法

Spam网页主要通过链接作弊手段达到提高搜索排名而获利的目的,根据链接作弊的特征,引入链接相似度和作弊系数两个指标来判定网页作弊的可能性。借鉴BadRank算法思想,从Spam网页种子集合通过迭代计算链接相似度和作弊系数,并根据与种子集合的链接指向关系设置权重,将待判定的网页进行度量。最后选取Anti-Trust Rank等算法作对比实验,结果验证了本文算法在准确率和适应性方面优于对比算法。     

Weak Delocalization in Graphene on a Ferromagnetic Insulating Film

Graphene has been predicted to develop a magnetic moment by proximity effect when placed on a ferromagnetic film, a promise that could open exciting possibilities in the fields of spintronics and magnetic data recording. In this work, the interplay between the magnetoresistance of graphene and the magnetization of an underlying ferromagnetic insulating film is studied in detail. A clear correlation between both magnitudes is observed but through a careful modeling of the magnetization and the weak localization measurements, that such correspondence can be explained by the effects of the magnetic stray fields arising from the ferromagnetic insulator is found. The results emphasize the complexity arising at the interface between magnetic and 2D materials.     

低速电机驱动翻板机构的设计与校核

介绍一种低速电机驱动连杆机构来实现接水翻板的运行,连杆机构设计为反平行四边形机构,由一台低速电机作为动力源、连杆作为执行机构、推动翻板打开与合拢,实现翻板接水工作,从而保证压滤机各工艺的正常。此方案结构简单,成本低廉,运行稳定可靠。     

Improved error performance for generalised spatial modulation with enhanced spectral efficiency

The existing analytical average bit error rate (ABER) expression of conventional generalised spatial modulation (CGSM) does not agree well with the Monte Carlo simulation results in the low signal‐to‐noise ratio (SNR) region. Hence, the first contribution of this paper is to derive a new and easy way to evaluate analytical ABER expression that improves the validation of the simulation results at low SNRs. Secondly, a novel system termed CGSM with enhanced spectral efficiency (CGSM‐ESE) is presented. This system is realised by applying a rotation angle to one of the two active transmit antennas. As a result, the overall spectral efficiency is increased by 1 bit/s/Hz when compared with the equivalent CGSM system. In order to validate the simulation results of CGSM‐ESE, the third contribution is to derive an analytical ABER expression. Finally, to improve the ABER performance of CGSM‐ESE, three link adaptation algorithms are developed. By assuming full knowledge of the channel at the receiver, the proposed algorithms select a subset of channel gain vector (CGV) pairs based on the Euclidean distance between all CGV pairs, CGV splitting, CGV amplitudes, or a combination of these.     

Three-phased clustered topology formation for Aeronautical Ad-Hoc Networks

In-Flight Internet Connectivity (IFC) has become one of the crucial needs of passengers with technological improvements. The Aeronautical Ad-hoc Networks (AANETs) have been proposed by establishing air-to-air (A2A) links between aircraft to satisfy this need. However, the unstructured aircraft topology caused by their ultra-dynamic characteristics reduces the longevity of A2A links in AANET. This shorter longevity decreases the stability of AANET by accelerating the connection establishment/termination procedures between aircraft. Additionally, the shorter link longevity affects the packet transfer delay and the delivery ratio of AANET. To the best of our knowledge, these three challenges are not investigated simultaneously under one topology management model. This paper proposes a three-phased topology formation model for AANETs to increase the stability and packet delivery ratio in AANET with a shorter packet transfer delay. The first phase corresponds to the aircraft clustering formation, and here, we aim to increase the AANET stability by creating spatially correlated clusters. The second phase consists of the A2A link determination for reducing the packet transfer delay. Finally, the cluster head selection increases the packet delivery ratio in AANET. According to our simulations, we see that the stability and packet delivery ratio of AANET topology are roughly improved 35% and 31% with a 28% delay reduction compared to the methods in the literature.