开关磁阻直线电机的设计及其推力优化

设计了一种开关磁阻直线电机,使用JMAG软件对其进行了仿真和优化。进行了推力公式的推导,研究了导通顺序和初始位置对电机推力的影响,并分析了三种典型情况的磁力线分布。对电机的定子和动子的齿部和轭部高度、宽度及铁耗进行仿真优化,得出了最优参数。将有取向硅钢应用于该电机,研究了推力与轧制角的关系,并与使用普通硅钢的开关磁阻直线电机进行了对比,推力有一定提升。提出在动子齿部开切向槽的方案,结果表明,开切向槽对推力的影响较小,并能显著减小推力波动。     

超声波探伤在铝合金中厚板检测中的应用

文章介绍了铝合金中厚板内部的常见缺陷,比较了超声波探伤铝合金中厚板的常用方法,展望了铝板超声波探伤未来的发展。     

子母车系统效率分析与验证

在子母车系统中,研究一台母车配合两台子车在同层进行复合作业的效率问题,提高系统作业效率计算的准确性,优化系统整体布局和物流设备的选择。首先分析一台母车配合两台子车在同层的循环复合作业过程;然后结合子母车的运动特性得到其作业效率计算模型;最后采用MATLAB工具计算效率模型分别在随机组合作业对和FEM9.851标准作业对的作业效率,通过对比验证,两种循环作业的效率绝对偏差符合工程实际。在工程实际中可以参照FEM9.851标准作业对代替随机组合作业对进行子母车系统的效率计算。     

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.     

A Novel Beam Search to Improve Neural Machine Translation for English-Chinese

Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation, overcoming the weaknesses of conventional phrase-based translation systems. Although NMT based systems have gained their popularity in commercial translation applications, there is still plenty of room for improvement. Being the most popular search algorithm in NMT, beam search is vital to the translation result. However, traditional beam search can produce duplicate or missing translation due to its target sequence selection strategy. Aiming to alleviate this problem, this paper proposed neural machine translation improvements based on a novel beam search evaluation function. And we use reinforcement learning to train a translation evaluation system to select better candidate words for generating translations. In the experiments, we conducted extensive experiments to evaluate our methods. CASIA corpus and the 1,000,000 pairs of bilingual corpora of NiuTrans are used in our experiments. The experiment results prove that the proposed methods can effectively improve the English to Chinese translation quality.