我国堆场磷石膏工程特性分析

磷石膏堆场的渗流及稳定性分析结果的可靠度取决于磷石膏物理力学参数的准确性,因此开展磷石膏工程特性研究十分必要。根据我国不同磷石膏堆场的试验数据,分别对磷石膏的物理性质、击实特性、渗透特性、固结特性和强度特性进行了分析。结果表明:磷石膏强度、压缩、渗透特性均与其压实程度密切相关,相同压实程度不同堆场磷石膏的物理力学性质相近;磷石膏渗透特性与其晶体形态有关;磷石膏在荷载作用下具有长期蠕变变形特性,磷石膏堆场封场后仍会发生较大沉降;深部磷石膏破坏时的应变大于浅层磷石膏;磷石膏剪切破坏时产生负孔隙水压力,实际工程中可通过测试孔压的变化了解堆场坝体的安全程度。     

影响煤层气开发效果的地质因素再认识

地质特征认识对煤层气开发效果起着重要作用。在资源特征相差不大的情况下,发现煤层气相邻井的产量差异仍较大。排除工程因素后,通过选取8类地质参数,细致比对了保德区块低产井与邻井的参数特征,筛查出其关键因素为煤层微幅构造与顶板封盖条件,其中以微幅构造为主。据此,重新认识并划分出区块新的次生褶皱背斜单元、向斜单元和斜坡单元,获得了不同次生褶皱构造单元的开发特征。结果表明,高、低产井分布与次生褶皱背斜、向斜相关性高达92%。其中:高产井主要分布在次生褶皱背斜变化较缓、呈隆起状的“平台”,且煤层顶板以泥岩、碳质泥岩为主,封盖性较好;低产井主要分布在次生褶皱向斜,同一井台各井开发效果差异表现为从向斜条带轴部—向斜条带内—向斜条带外的煤层气井平均单井产量不断增加,到向斜轴部的距离大于向斜曲率半径73.5%的范围为主力产气区,小于向斜曲率半径40.0%范围为产水主力区。这对煤层气新井部署、生产管理、开发调整等,具有一定的指导意义。     

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.