Low-rank tensor completion via combined Tucker and Tensor Train for color image recovery

Applied Intelligence - In recent years, low-rank tensor completion has been widely used in color image recovery. Tensor Train (TT), as a balanced tensor rank minimization method, has achieved good...     

发动机进气系统高通量富氧膜的研制

采用交联改性的方法对较低分子量的乙烯基硅橡胶进行了改性研究,制备了高通量的富氧膜,结合单因素实验法和Design-expert正交实验设计,考察了原料配比、交联反应时间、固化温度和固化时间等因素对富氧膜性能的影响,确定了最佳富氧性能膜的制备条件:原料配比4.3,反应时间2.15h,固化温度86.25℃,固化时间1.75h;获得了富氧浓度为28.68%,透气量为4696.33 barrer的富氧膜,该膜的富氧浓度与常规商品化膜相当,透气量为常规商品化膜的2~4倍,有应用于发动机富氧进气系统的前景。通过扫描电镜检测、机械性能检测等手段对复合膜进行了进一步表征。     

滚筒反力式制动检验台制动力测量不确定度评定

本文介绍了滚筒反力式制动检验台制动力的不确定度评定。     

The interlaminar shear failure characteristics of asphalt pavement coupled heating cables

Nowadays, heating cables are used as heat sources for heating pavements in practical engineering. However, there is a contradiction between the snow melting function and the interlaminar stability of heating pavement. In order to solve the contradiction, the interlaminar failure behavior of asphalt mixture coupled heating cables specimen (AMCS) was researched, through experiments and the finite element method. Under the different conditions of heating cables and rolling times, a series of direct shear tests was performed at the interface of AMCS, to compare the interlaminar stability of three different AMCS. Meanwhile, based on the bilinear cohesive zone model and coulomb friction model a 2D finite element model was established, to simulate this shear failure processes and make up for the limitations of the experiment. According to above test and simulation results, the failure mechanism and the weakest interface in AMCS were found, and the influence of the heating cable’s diameter and embedded spacing on the interlaminar shear strength were found. Then, a modified coulomb theorem model was proposed to predict the shear strength of the AMCS. This research enriches the design theory of the heating pavement and it has great significance for its structural design of heating asphalt pavement.     

MHSACAE-CNN在噪声下的电机轴承故障诊断

电机的运行情况复杂,实际运行工况下会有大量的噪声,导致其轴承故障诊断精度下降。为了改善这一问题,提出了一种基于多头自注意力机制的一维全卷积自编码网络（One-dimensional Fully Convolutional Autoencoding Network Basedon Multi-head Self-attention,MHSACAE）与卷积神经网络（Convolutional Neural Network,CNN）结合的轴承故障诊断方法。该方法先采用MHSACAE网络进行降噪,再通过CNN进行故障诊断。其中MHSACAE去噪网络采用无监督训练的方式,充分考虑了实际工况和序列数据内在联系,在实现对噪声的滤除效果的同时,最大限度地保留下了原始的故障信息,使得CNN可以实现在噪声情况下对电机轴承故障的高精度诊断。通过与其他轴承故障诊断方法在噪声情况下进行对比,证明提出的方法具有更好的效果。