Dynamic analysis of traction motor in a locomotive considering surface waviness on races of a motor bearing

The traction motor is the power source of the locomotive. If the surface waviness occurs on the races of the motor bearing, it will cause abnormal vibration and noise, accelerate fatigue and wear, and seriously affect the stability and safety of the traction power transmission. In this paper, an excitation model coupling the time-varying displacement and contact stiffness excitations is adopted to investigate the effect of the surface waviness of the motor bearing on the traction motor under the excitation from the locomotive-track coupled system. The detailed mechanical power transmission path and the internal/external excitations (e.g., wheel–rail interaction, gear mesh, and internal interactions of the rolling bearing) of the locomotive are comprehensively considered to provide accurate dynamic loads for the traction motor. Effects of the wavenumber and amplitude of the surface waviness on the traction motor and its neighbor components of the locomotive are investigated. The results indicate that controlling the amplitude of the waviness and avoiding the wavenumber being an integer multiple of the number of the rollers are helpful for reducing the abnormal vibration and noise of the traction motor.

     

Moderate temperature sintering of BaZr0.8Y0.2O3-δ protonic ceramics by A novel cold sintering pretreatment

The state-of-the-art protonic ceramic conductor BaZr_0.8Y_0.2O_3-δ (BZY20) requires an extremely high sintering temperature (≥1700 °C) to achieve the desired relative density and microstructure necessary to function as a proton conducting electrolyte. In this work, we developed a cold sintering pretreatment assisted moderate-temperature sintering method for the fabrication of high-quality pure BZY20 pellets. BZY20 pellets with high relative density of ~94% were fabricated with a final sintering temperature of 1500 °C (200 °C lower than the traditional sintering temperature). A comparison with BZY20 control samples indicated that the proper amount of BaCO₃ introduced on the BZY20 particle surface and the high green density achieved by cold sintering pretreatment were the main drivers for lowering the sintering temperature. The electrical conductivity measurement by electrochemical impedance spectroscopy showed that the as-prepared BZY20 pellets have a proton conductivity comparable to the state-of-the-art values. The cold sintering pretreatment outlined in this work has the potential to lower the sintering temperatures for similar types of protonic ceramic materials under consideration for a wide range of energy conversion and storage applications.     

FireNet-MLstm for classifying liver lesions by using deep features in CT images

Multimedia Tools and Applications - Nowadays working in the medical imaging domain remains a big challenge, since the collecting such datasets is so complex, deep learning techniques have...     

水泥乳化沥青砂浆峰值应变的试验研究

采用万能材料试验机对不同配合比的水泥乳化沥青砂浆(CA砂浆)进行了单轴压缩试验,研究了结构组成对CA砂浆峰值应变的影响,并进一步建立了峰值应变与抗压强度的定量关系.结果表明,峰值应变随A/C、W/C的减小而减小,A/C的变化改变了CA砂浆的结构特性,W/C的不同则影响了内部能量的释放过程.CA砂浆的峰值应变与抗压强度的平方根成正比,且符合林大炎方程,结合峰值应变的理论计算及CA砂浆充填层的实际受力状态,估算了充填层发生伤损破坏时的几何尺寸变化.     

Sparse non-convex Lp regularization for cone-beam X-ray luminescence computed tomography

Cone-beam X-ray luminescence computed tomography (CB-XLCT) is an attractive hybrid imaging modality, and it has the potential of monitoring the metabolic processes of nanophosphors-based drugs in vivo. However, the XLCT imaging suffers from a severe ill-posed problem. In this work, a sparse nonconvex L_p (0?p?L₁ regularization. Further, an iteratively reweighted split augmented lagrangian shrinkage algorithm (IRW_SALSA-L_p) was proposed to efficiently solve the non-convex L_p (0?p?p-values (1/16, 1/8, 1/4, 3/8, 1/2, 5/8, 3/4, 7/8) in both 3D digital mouse experiments and in vivo experiments. The results demonstrate that the proposed non-convex methods outperform L₂ and L₁ regularization in accurately recovering sparse targets in CB-XLCT. And among all the non-convex p-values, our L_p(1/4?p?相似文献   

Tailored dielectric tunability of alkali niobate-based antiferroelectric/relaxor-ferroelectric composites

High dielectric tunability, low loss and an appropriate level of dielectric permittivity are basic requirements of ferroelectric materials for tunable microwave devices. In this study, 0.96NaNbO₃-0.04CaZrO₃/0.88(K_0.5Na_0.5)NbO₃-0.12SrZrO₃, antiferroelectric/relaxor-ferroelectric composites were designed to tailor dielectric tunability. By tailoring the microstructure of the composites, a high dielectric tunability of 51.78% with a low loss of 0.015 was achieved at the composition ratio of 15/85. The nonlinear behaviours of the composites were explored by Johnson model; with increasing antiferroelectric phase, the contribution of the polarization to the free energy was increased between the antiferroelectric and relaxor-ferroelectric. Furthermore, the high resistance layer at the grain boundary region greatly inhibited the long-term migration of electrons and defective ions (mainly oxygen vacancies) in the composites. Therefore, the dielectric loss was remarkably decreased, and the excellent tunability was still preserved in the composite ceramics.     

MOOC Learner’s Final Grade Prediction Based on an Improved Random Forests Method

Massive Open Online Course (MOOC) has become a popular way of online learning used across the world by millions of people. Meanwhile, a vast amount of information has been collected from the MOOC learners and institutions. Based on the educational data, a lot of researches have been investigated for the prediction of the MOOC learner’s final grade. However, there are still two problems in this research field. The first problem is how to select the most proper features to improve the prediction accuracy, and the second problem is how to use or modify the data mining algorithms for a better analysis of the MOOC data. In order to solve these two problems, an improved random forests method is proposed in this paper. First, a hybrid indicator is defined to measure the importance of the features, and a rule is further established for the feature selection; then, a Clustering-Synthetic Minority Over-sampling Technique (SMOTE) is embedded into the traditional random forests algorithm to solve the class imbalance problem. In experiment part, we verify the performance of the proposed method by using the Canvas Network Person-Course (CNPC) dataset. Furthermore, four well-known prediction methods have been applied for comparison, where the superiority of our method has been proved.     

0Cr20Mn21Ni2N奥氏体不锈钢锻件组织性能研究

研究了无磁钻铤用0Cr20Mn21Ni2N奥氏体不锈钢圆棒锻件横截面的室温强度、硬度和韧性等力学性能,并从微观角度进行了分析。在圆棒表层区域观察到了锻造变形态晶粒组织,而直径四分之一区域为再结晶组织,平均晶粒尺寸为80μm,芯部再结晶晶粒尺寸粗化为150μm,这些差异是由不同的变形温度和降温速度造成的。这种组织差异导致的室温拉伸性能和冲击韧性也有相应的变化趋势。研究结果表明,在900、1000℃以0.5 s-1的应变速率压缩的0Cr20Mn21Ni2N试验钢样品,其应力应变曲线具有一定的加工硬化特征,其芯部硬度比应变速率为0.1 s-1的样品分别提升了28、15 HB。在700、800℃以20%以上的变形量进行压缩时,峰值抗力显著提高,而在900、1000℃以10%~50%的变形量进行压缩时,峰值抗力均无明显提高,可保证顺利的变形过程。     

Factors impacting university students' online learning experiences during the COVID-19 epidemic

Due to the COVID-19 epidemic crisis, students from higher education institutions around the world are forced to participate in comprehensive online curriculums. In such a scenario, it is worth investigating how students perceived their learning outcomes and satisfaction based on this method of teaching and learning online. This study aims to explore the role of six factors, namely, system quality, course design, learner-learner interaction, learner-instructor interaction, learner-content interaction, and self-discipline, on university students' perceived learning outcomes and their effect on student satisfaction with online curricula during the COVID-19 epidemic. A structural equation modelling technique was used to assess survey questionnaires obtained from 457 validated students at a Public University in China. The results demonstrated that these determinants had a positive effect on satisfaction and learning outcomes, whereas learner-instructor interaction had no significant effect. Furthermore, the strongest determinant that affected not only students' satisfaction but also their learning outcomes was the learner-content interaction.