Enhanced relative permittivity in niobium and europium co-doped TiO2 ceramics

The appearance of colossal permittivity materials broadened the choice of materials for energy-storage applications. In this work, colossal permittivity in ceramics of TiO₂ co-doped with niobium and europium ions ((Eu_0.5Nb_0.5)_xTi_1-xO₂ ceramics) was reported. A large permittivity (ε_r ～ 2.01?×?10⁵) and a low dielectric loss (tanδ ～ 0.095) were observed for (Eu_0.5Nb_0.5)_xTi_1-xO₂ (x?=?1%) ceramics at 1?kHz. Moreover, two significant relaxations were observed in the temperature dependence of dielectric properties for (Eu, Nb) co-doped TiO₂ ceramics, which originated from defect dipoles and electron hopping, respectively. The low dielectric loss and high relative permittivity were ascribed to the electron-pinned defect-dipoles and electrons hopping. The (Eu_0.5Nb_0.5)_xTi_1-xO₂ ceramic with great colossal permittivity is one of the most promising candidates for high-energy density storage applications.     

GK1C型内燃机车频繁停车现象探讨与解决方案

运用过程中各类相关因子的变化,会使得机车柴油机配气相位出现一定程度变化,采取某种措施调整或是处理可以解决动力不足的问题。     

A computer aided design method for car form and its application based on shape parameters

In the early design stage, automotive modeling should both meet the requirements of aesthetics and engineering. Therefore, a vehicle CAD (computer aided design) model that can be easily adjusted by feedbacks is necessary. Based on CE-Bézier surface, this paper presents a set of algorithms for parametric segmentation and fairing surface generation in a car model. This model is defined by a simplified automotive template and relevant control points, shape parameters and segmentation parameters, which can be modified to alter the car form efficiently. With this model and the corresponding adjustment method, more than fifty various vehicle models are established in this research according to different parameters. And two methods for calculating similarity index between car models are constructed, which are suitable for brand design trend analysis and modelling design decisionmaking.     

Image and Feature Space Based Domain Adaptation for Vehicle Detection

The application of deep learning in the field of object detection has experienced much progress. However, due to the domain shift problem, applying an off-the-shelf detector to another domain leads to a significant performance drop. A large number of ground truth labels are required when using another domain to train models, demanding a large amount of human and financial resources. In order to avoid excessive resource requirements and performance drop caused by domain shift, this paper proposes a new domain adaptive approach to cross-domain vehicle detection. Our approach improves the cross-domain vehicle detection model from image space and feature space. We employ objectives of the generative adversarial network and cycle consistency loss for image style transfer in image space. For feature space, we align feature distributions between the source domain and the target domain to improve the detection accuracy. Experiments are carried out using the method with two different datasets, proving that this technique effectively improves the accuracy of vehicle detection in the target domain.     

除尘器新型褶式滤筒的结构优化及系列化设计

以新型褶式滤筒为研究对象,选取滤筒褶数N_2、褶夹角θ、褶高h、过滤风速v为影响因子,除尘器压力损失为响应值,基于响应曲面法分析影响因子对压力损失的影响,建立滤筒除尘器压力损失的预测模型并得到滤筒褶皱结构最优参数。以滤筒内径D、筒体高度D_1为标准尺寸,依据相关国家标准设计滤筒筒体部分;以锥体高度D_2、锥体上圆直径D_3为系列尺寸,利用尺寸变化法设计锥体部分;通过Access软件建立滤筒产品数据库。结果表明:当θ=5.2°,N_2=50,v=0.01 m/s,h=0.035 m时,除尘器压力损失最低;当D_1=1 000 mm,D=320 mm时,D_2和D_3最佳值分别为600,130 mm,故采用D_2/D_1=0.6,D_3/D=0.4的比值设计相应滤筒锥体系列。