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.     

碳含量对激光熔覆CoCrFeMnNiCx高熵合金涂层摩擦磨损和耐蚀性能的影响

采用激光熔覆技术在 45 钢基体上制备了不同碳含量(等摩尔比)的 CoCrFeMnNiC_x( x = 0,0. 03,0. 06,0. 09, 0. 12,0. 15)高熵合金涂层。 通过 X 射线衍射(XRD)、扫描电镜( SEM)、HVS-1000A 型显微硬度计、RST5000 型电化学工作站、UMT-2 型摩擦磨损试验机等表征和测试手段研究了不同碳含量对激光熔覆 CoCrFeMnNiC_x 高熵合金涂层物相结构、显微硬度、摩擦磨损及耐腐蚀性能的影响。 结果表明,当碳含量 x 由 0 逐渐增加至 0. 09 时,高熵合金相结构由 FCC 固溶体转变为 FCC 固溶体和 M23C6 相共存,合金微观组织变得细小;熔覆层硬度由 183. 20 HV_{0. 2} 增加至 223. 48 HV_{0. 2} ; 涂层的摩擦因数降低,耐磨性能变强;腐蚀电位由-469 mV 增大至-348 mV,腐蚀电流密度由 14. 95 μA·cm^-2 减小为 2. 29 μA·cm^-2 ,耐腐蚀性增强。 当碳含量 x 由 0. 09 逐渐增加至 0. 15 时,合金相结构再次转变为 FCC 固溶体,且合金微观组织恢复粗大状态;熔覆层硬度与耐腐蚀性降低,但耐磨性能却先减弱后增强。 合金在碳含量为 0. 09 时,硬度最高且耐腐蚀性能最强;在碳含量为 0. 15 时,耐磨性最强。     

乌拉根锌矿排土场稳定性分析

排土场的稳定性研究是露天矿山安全性管理的重点，也是矿山可持续发展的关键。以乌拉根锌矿排土场为例，在岩土工程勘察的基础上，利用Geo slope软件，运用极限平衡法和有限元法对排土场边坡危险剖面在正常工况下及降雨入渗工况下的稳定性进行数值模拟。计算结果表明，正常工况下，边坡处于基本稳定状态；降雨入渗后，边坡安全系数降低3.9%，处于欠稳定状态，应加强排土场降雨预报和监测管理。     

轮胎安全相关法规标准的分析与研究

介绍我国产品和汽车轮胎的相关法规和标准现状，分析中国标准、国际标准以及欧盟、美国、印度和印度尼西亚轮胎安全相关法规标准的区别，评价项目包括轮胎的高速性能、耐久性能、脱圈性能和强度性能，这有助于了解各类轮胎法规标准和认证要求，为我国轮胎产业发展提供借鉴。     

自保护药芯焊丝明弧堆焊Fe-Cr-C-B-W合金的组织及性能

采用自保护药芯焊丝明弧堆焊技术制备五组不同钨含量的Fe-Cr-C-B-W合金. 借助金相显微镜、扫描电子显微镜、X射线衍射仪、洛氏硬度计和磨损试验机分析堆焊合金的组织及性能. 结果表明，合金的显微组织由马氏体、残余奥氏体、M₇(C,B)₃，M₃(C,B)，Fe₃W₃C和WC组成. 大部分钨元素被迁移到晶界生成了比WC稳定性更好的Fe₃W₃C缺碳复合相，堆焊层中没有典型的初生WC硬质相颗粒生成. 随着钨添加量的增多，共晶硬质相M₇(C,B)₃，M₃(C,B)和Fe₃W₃C随之增多，间距减小，呈连续网状均匀分布. 当钨的添加量为12%时，堆焊层的耐磨性达到最佳.