基于强度场的八球万向节内套结构与静强度设计匹配研究

为了对八沟道等速万向节内套进行结构和静强度设计,从钢球选型出发,对八沟道等速万向节内套进行了结构参数正向设计.对内套危险截面进行了全场静强度设计,以理想静强度场为目标,定量匹配了危险截面静强度的材料、热处理,并对全场静强度设计进行了定量评价.使用MTS静扭转试验机对等速万向节总成进行了静强度扭转试验,并用显微硬度计对内套进行了显微硬度试验以验证理论,结果表明按照基于强度场的机械结构和零部件的静强度设计与匹配方法设计的八沟道等速万向节星形套能够满足强度和功能需求.     

大洋勘探船压载水系统设计

分析和研究最新生效的几种常用压载水管理公约规范,调研和对比目前市场上常用的几种压载水处理措施,综合对比分析各种压载水处理技术并结合大洋勘探船的特点选择合适的压载水处理技术,设计研究一型经济和环保的压载水系统,并进行压载水处理装置设备选型、系统设计和相关设备布置等。     

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.     

Passive target detection and tracking from electromagnetic field measurements

This paper presents a novel approach to the localization of moving targets in a complex environment based on the measurement of the perturbations induced by the target presence on an independently‐generated time‐varying electromagnetic field. Field perturbations are measured via a set of sensors deployed over the domain of interest and used to detect and track a possible target by resorting to a particle Bernoulli filter (PBF). To comply with real‐time operation, the PBF works along with an artificial neural network (ANN) model of the environment trained offline via finite elements (FEs). The performance of the proposed algorithm is assessed via simulation experiments.