Mechanical properties of graphene films enhanced by homo-telechelic functionalized polymer fillers via π–π stacking interactions

Most researches on graphene/polymer composites are focusing on improving the mechanical and electrical properties of polymers at low graphene content instead of paying attention to constructing graphene’s macroscopic structures. In current study the homo-telechelic functionalized polyethylene glycols (FPEGs) were tailored with π-orbital-rich groups (namely phenyl, pyrene and di-pyrene) via esterification reactions, which enhanced the interaction between polyethylene glycol (PEG) molecules and chemical reduced graphene oxide (RGO) sheets. The π–π stacking interactions between graphene sheets and π-orbital-rich groups endowed the composite films with enhanced tensile strength and tunable electrical conductivity. The formation of graphene network structure mediated by the FPEGs fillers via π–π stacking non-covalent interactions should account for the experimental results. The experimental investigations were also complemented with theoretical calculation using a density functional theory. Atomic force microscope (AFM), scanning electron microscope (SEM), X-ray diffraction (XRD), nuclear magnetic resonance (NMR), thermal gravimetric analysis (TGA), UV–vis and fluorescence spectroscopy were used to monitor the step-wise preparation of graphene composite films.     

利用二进制数算法寻找5个点的Ramsey图

用二进制数来表示完全图的各种组合,借助计算机编程可从完全图的各种组合中寻找出ramsey图。5个顶点的Ramsey图是最基本的Ramsey图,这种基本方法为寻找更复杂的Ramsey图提供了基础。     

Ensemble deep transfer learning driven by multisensor signals for the fault diagnosis of bevel-gear cross-operation conditions

The existing intelligent fault diagnosis techniques of bevel gear focus on single-sensor signal analysis under the steady operation condition. In this study, a new method is proposed based on ensemble deep transfer learning and multisensor signals to enhance the fault diagnosis adaptability and reliability of bevel gear under various operation conditions. First, a novel stacked autoencoder(NSAE) is constructed using a denoising autoencoder, batch normalization, and the Swish activation function. Second, a series of source-domain NSAEs with multisensor vibration signals is pretrained. Third, the good model parameters provided by the source-domain NSAEs are transferred to initialize the corresponding target-domain NSAEs. Finally, a modified voting fusion strategy is designed to obtain a comprehensive result. The multisensor signals collected under the different operation conditions of bevel gear are used to verify the proposed method. The comparison results show that the proposed method can diagnose different faults in an accurate and stable manner using only one target-domain sample, thereby outperforming the existing methods.     

基于CAE技术的汽车前保险杠成型工艺优化研究

介绍了利用CAE技术优化汽车保险杠的浇口设置,在优化方案的基础上,结合经验设计冷却系统,通过正交试验的方式确定保险杠成型工艺参数对翘曲变形的影响程度,得出最佳的工艺参数组合,用以指导生产实践。     

基于WordNet的无导词义消歧方法

有导词义消歧机器学习方法由于需要大量人力进行词义标注,难以适用于大规模词义消歧任务.提出一种避免人工词义标注的无导消歧方法.该方法综合利用WordNet知识库中的多种知识源（包括：词义定义描述、使用实例、结构化语义关系、领域属性等）描述歧义词的词义信息,生成词义的“代表词汇集”和“领域代表词汇集”,结合词汇的词频分布信息和所处的上下文环境进行词义判定.利用通用测试集Senseval 3对6个典型的无导词义消歧方法进行开放实验,该方法取得平均正确率为49.93％的消歧结果.     

基于Keras手写数字识别模型的改进

针对结构设计不合理的卷积神经网络导致MNIST识别的准确率低、收敛速度慢和训练参数多等问题,提出卷积神经网络结构的改进模型.改进的模型采用2次卷积、2次池化和3次全连接、采用Relu激活函数和Softmax回顾函数相结合,加入Dropout层防止过拟合,加入Flatten层优化结构.为了缩减代码量,采用API功能强大的Keras模型替代Tensorflow.对MNIST的训练集和测试集数据的准确率进行仿真实验,实验结果表明:采用改进的结构在MNIST的训练中不仅收敛速度快、训练参数少、损失率低,而且在测试集上的准确率达到99.54％、高于改进前的99.25％,对后续手写数字的研究具有重要意义.     

论我国没收程序的没收对象问题

我国违法所得没收的特别程序规定的没收对象是违法所得及其他涉案财产,我国程序法规定的没收对象与实体法规定的没收对象有所不同,也与国际公约所规定的犯罪所得不一致.在分析违法所得与犯罪所得区别的基础上,认为我国刑事诉讼法新增违法所得没收程序的没收对象应该进一步明确和完善.     

纳秒激光微刻蚀取向硅钢的形貌特征及磁性能

为有效降低取向硅钢铁损、改善其磁性能，采用红外纳秒激光对取向硅钢进行微刻蚀试验。利用3D共聚焦显微镜、扫描电镜与能谱仪研究典型工艺参数下的硅钢表面烧蚀形貌特征及表面质量，利用铁损仪测试不同刻蚀参数下取向硅钢的铁损、相对磁导率等磁性能参数及动态磁滞回线，对比分析刻痕前后磁性能的变化行为、规律及磁滞特性。结果表明：激光刻痕后，硅钢的铁损、相对磁导率、矫顽力及剩磁等性能得到明显改善，铁损的改善表现在高磁感强度下、相对磁导率的改善表现在低磁场强度下，回线特性得到优化。扫描速度为800 mm/s、脉冲能量为0.25 mJ时，刻痕边界呈近似规则的“波浪线”，且刻痕表面质量较高，铁损降低高达11.6%、剩磁降低12.8%等；刻痕后相对相对磁导率明显提高，提高约为5.7%~15.16%，最大可达2.598×10⁴。激光频率与扫描速度的耦合关系是影响刻痕边界形状与磁畴细化效果的重要因素。     

基于加权支持向量机的凸轮升程误差拟合方法

针对凸轮磨削加工过程中存在局部升程误差严重超差的问题,提出了加权支持向量机的凸轮升程误差补偿方法。首先根据圆率的符号判断凸轮升程误差值是否连续,然后建立了不连续点与相邻点之间夹角与加权值的关系。通过加权支持向量机对凸轮升程误差值进行拟合。然后将原始凸轮升程值减去经拟合后的凸轮升程误差值,获得新的凸轮升程值。再根据圆率自动调整加权值的系数,使凸轮升程值光滑点数最大,即获得最优的凸轮升程值。对上述算法进行仿真和编程,经YTM8336-16数控凸轮轴磨床进行磨削验证。实验表明,凸轮升程误差由原来的±0.059 mm降至±0.011 mm,表面粗糙度由Ra0.32μm降至Ra0.25μm,获得良好的效果。