旋流对固体燃料冲压发动机燃烧过程的影响

针对旋流数对固体燃料冲压发动机燃烧流场的影响，进行了不同旋流数(s=0,0.2,0.4,0.6)工况下固体燃料冲压发动机直连式试验研究，固体燃料采用高密度聚乙烯（HDPE），并对s=0.6和s=0两个工况进行数值仿真研究.试验和数值仿真结果表明:旋流的引入有助于火焰稳定，对提高补燃室压强和燃速有积极作用.在旋流工况下会使药柱表面热流密度高于无旋流，并使燃烧室中的化学反应更加充分.高强度旋流的引入会使燃烧更加充分，燃烧效率有所提高.      

基于偏最小二乘回归同时测定阿莫西林和多四环素含量

阿莫西林和多四环素在紫外光谱区间吸收峰附近有多重共线现象,紫外分光光度法难以准确测出2种物质各自含量.本文通过L_(16)(4~5)正交实验设计,采集200～250 nm波长混合样品吸收光谱,数据经标准化处理,建立偏最小二乘回归预报数学模型.结果显示,该法计算阿莫西林和多四环素的预测相对误差(RPE)分别为3.13%和6.42%,平均加标回收率分别为100.34%和100.71%.该法简便快捷,准确可靠,样品无需预先分离.     

Gaussian process regression model incorporated with tool wear mechanism

Cutting tool condition directly affects machining quality and efficiency. In order to avoid severely worn tools used during machining process and fully release the remaining useful life in the meanwhile, a reliable evaluation method of remaining useful life of cutting tools is quite necessary. Due to the variation of cutting conditions, it is a challenge to predict remaining useful life of cutting tools by a unified model. In order to address this issue, this paper proposes a method for predicting the remaining useful life of cutting tools in variable cutting conditions based on Gaussian process regression model incorporated with tool wear mechanism, where the predicted value at adjacent moments is constrained to a linear relationship by the covariance matrix of Gaussian model based on the assumption of progressive tool wear process, so the wear process under continuous changing conditions can be modelled. In addition to that, the input feature space and the output of the model are also enhanced by considering the tool wear mechanism for improving prediction accuracy. Machining experiments are performed to verify the proposed method, and the results show that the proposed could improve the prediction of tool remaining useful life significantly.     

全球航空旅客周转量预测及运力分配方法

民机制造商在新飞机可行性论证、公司产品战略制定以及飞机销售中，需要了解航空市场规模和未来航空市场飞机的需求，为可行性论证提供投资规模计算输入，为制造商制定产品战略发展提供依据，为飞机销售计划提供数据支持。预测未来客机市场的需求量，其关键是对航空旅客周转量的预测。采用线性回归预测出航空旅客周转量，除以上座率得到航空市场运力需求，即可供座位数，并将运力需求分配后与各地区机队预测的运力供给相匹配，最终得出所需的飞机需求量。     

Exploring 2D projection and 3D spatial information for aircraft 6D pose

The 6D pose estimation is important for the safe take-off and landing of the aircraft using a single RGB image. Due to the large scene and large depth, the exiting pose estimation methods have unstratified performance on the accuracy. To achieve precise 6D pose estimation of the aircraft, an end-to-end method using an RGB image is proposed. In the proposed method, the 2D and 3D information of the keypoints of the aircraft is used as the intermediate supervision, and 6D pose information of the aircraft in this intermediate information will be explored. Specifically, an off-the-shelf object detector is utilized to detect the Region of the Interest (RoI) of the aircraft to eliminate background distractions. The 2D projection and 3D spatial information of the pre-designed keypoints of the aircraft is predicted by the keypoint coordinate estimator (KpNet). The proposed method is trained in an end-to-end fashion. In addition, to deal with the lack of the related datasets, this paper builds the Aircraft 6D Pose dataset to train and test, which captures the take-off and landing process of three types of aircraft from 11 views. Compared with the latest Wide-Depth-Range method on this dataset, our proposed method improves the average 3D distance of model points metric (ADD) and 5° and 5 m metric by 86.8% and 30.1%, respectively. Furthermore, the proposed method gets 9.30 ms, 61.0% faster than YOLO6D with 23.86 ms.