高层大气密度的午夜极大值特征分析与建模

利用CHAMP (CHAllenging Minisatellite Payload)、GRACE-A (Gravity Recovery and Climate Experiment-A)、SWARM-C (The Earth''s Magnetic Field and Environment Explorers-C)等3颗极轨卫星的资料, 研究360—480km高层大气密度在低纬度区域的午夜极大值(Midnight Density Maximum, MDM)现象. MDM一般出现在23:00- 02:00 LT (Local Time)之间,峰值位置在低纬度15°以内,谷值位置在中纬度35°-45°附近,整体略偏向南半球,振幅约为平均密度的26%.随着高度增大以及太阳辐射水平的增强,MDM振幅呈减小趋势;冬至和夏至日附近的季节效应会减弱MDM振幅,在春秋分日的振幅最大.用3个主流大气模型DTM2000 (Drag Temperature Model 2000), NRLMSISE00 (US Naval Research Laboratory, Mass Spectrom-eter and Incoherent Scatter radar)和JB2008 (Jacchia- Bowman 2008 model)对MDM进行模拟,JB2008没有刻画出MDM现象;另两个模型低估了MDM效应,在360km和480km两个高度DTM2000模型的振幅仅为观测的46%和53%, NRLMSISE00模型仅为观测的33%和26%;模型没有准确刻画出MDM与高度、辐射水平和季节的关系.联合3颗卫星的资料,研究了-种基于地理纬度的6阶勒让德多项式,同时融合地方时和高度因素的经验函数,在振幅和相位上可以较好地刻画MDM特征,相关系数达到0.923,可为大气密度模型的修正提供借鉴,服务于低轨道航天器高精度轨道预报.     

大型射电望远镜主反射面调整方法研究

随着毫米波天文学和空间通信的重要性日益提高, 对天线性能提出了越来越高的要求, 而天线性能往往受到其反射器表面精度的限制. 微波全息技术是一种快速有效的检测反射面天线表面轮廓的测量技术. 通过微波全息测量得到天线口径场, 计算天马65m射电望远镜反射面与理想抛物面的偏差. 天马65m射电望远镜的主反射面板是放射状的, 有14圈. 面板的每个角都固定在面板下方促动器的螺栓上进行上下移动, 且相邻面板交点处的拐角共用一个促动器. 采用平面拟合的方法可以计算各块面板拐角处的调整值, 但是同一个促动器会得到4个不同的调整量. 通过平面拟合, 同时以天线照明函数为权重的平差计算方法得到相邻面板拐角的一个平差值, 即天马65m射电望远镜1104个促动器的最佳调整值. 通过多次调整和新算法的应用, 天马65m射电望远镜反射面的面形精度逐渐提高到了0.24mm.     

结构首次穿越失效研究的新进展

在结构首次穿越失效分析中,初始条件、成群穿越、结构的非线性及抗力退化等问题影响着分析方法的复杂性和分析结果的精确性。结合近年来的研究进展,本文讨论和分析了解决上述问题的新思路、新技术和新方法,同时,归纳了几个新的研究热点,以便为结构首次穿越失效的进一步理论和应用研究提供有益的参考。     

The Basic Shape Classification of Space Debris with Light Curves

We study the machine learning method for classifying the basic shape of space debris in both simulated and observed data experiments, where light curves are used as the input features. In the dataset for training and testing, simulated light curves are derived from four types of debris within different shapes and materials. Observed light curves are extracted from Mini-Mega TORTORA (MMT) database which is a publicly accessible source of space object photometric records. The experiments employ the deep convolutional neural network, make comparisons with other machine learning algorithms, and the results show CNN (Convolutional Neural Network) is better. In simulational experiments, both types of cylinder can be distinguished perfectly, and two other types of satellite have around 90% probability to be classified. Rockets and defunct satellites can achieve 99% success rate in binary classification, but in further sub-classes classifications, the rate becomes relatively lower.     

不同饲养方式对乌鳢(Ophiocephalus argus)肠道微生物群落结构差异及种类多样性的影响

以乌鳢(Ophiocephalus argus)为研究对象,采用不同日粮饲喂,试验W1组饲喂冰鲜鱼,试验W2组饲喂畜禽内脏。12周后,取肠道内容物,利用16S rDNA技术研究投喂不同饲粮的乌鳢肠道生物群落结构差异和微生物多样性,分别获得W1组和W2组乌鳢肠道细菌有效序列51124和39298条。结果分析表明;W1组和W2组物种注释(operational taxonomic unit,OTU)数目分别为2631和4114条,分类地位明确的细菌种类分别隶属5个属和49个属,W1组和W2组乌鳢肠道细菌种类差异很大。W1组乌鳢肠道细菌的优势类群是邻单胞菌属(Plesiomonas)、狭义梭菌属(Clostridium sensu stricto)、鲸蜡菌属(Cetobacterium);W2组乌鳢肠道细菌的优势类群是狭义梭菌属(Clostridium sensu stricto)、邻单胞菌属(Plesiomonas)、Spartobacteria、甲基副球菌属(Methyloparacoccus)、杆菌属(Alsobacter)、Saccharibacteria、分支杆菌属(Mycobacterium)。W2组乌鳢肠道细菌香农指数高于W1组乌鳢肠道细菌,表明前者肠道细菌多样性大于后者。本实验揭示了畜禽内脏饲喂乌鳢肠道细菌的群落种类多样性、OTU丰富度和物种总数均高于冰鲜鱼饲喂乌鳢;W1组肠道有益菌OTU丰富度大于W2组,且W2组还存在一些潜在致病菌。本研究为乌鳢的健康养殖和后续肠道微生物资源的挖掘提供了理论依据。     

A multiple shooting approach for the numerical treatment of stellar structure and evolution

We present a new numerical method for solving the system of partial differential equations describing the structure and evolution of a spherically symmetric star. As usual, we employ the transversal method of lines in order to split the equations into a coupled spatial and temporal part. The novel features of the algorithm are the following: (a) Instead of using the Lagrangian picture we formulate the system of partial differential equations in the Eulerian picture. (b) We reformulate the equations of stellar structure as a multipoint boundary-value problem. By means of this reformulation the rather clumsy iterative matching procedure of stellar atmosphere and interior is avoided. (c) The multipoint boundary-value problem is solved by the multiple shooting method. This approach not only ensures a high accuracy of the stellar models calculated at each time step but also allows the free boundaries inside the star due to different energy transport mechanisms to be located exactly. (d) The time derivatives involved in the stellar-structure equations are discretized implicitly to second order accuracy. Moreover, at each time step, the chemical abundances are determined by using a sophisticated update procedure. In this way, a high accuracy is achieved with respect to the integration in time. The algorithm has turned out to be exceedingly reliable and numerically accurate. This is shown by the evolution of a 1 M_⊙ star up to the hydrogen-shell burning phase. In this example, the virial theorem, the law of mass conservation, and the law of energy conservation is fulfilled to a hitherto unattainable degree of accuracy. Since the multiple shooting method, which is at the heart of our approach, is a perfect example of a parallel algorithm, the computational speed of the algorithm might be substantially improved provided easy-to-program, high-performance parallel computers with sufficiently many processors become available in the near future.     

基于Stacking集成学习的恒星/星系分类研究

机器学习在当今诸多领域已经取得了巨大的成功,但是机器学习的预测效果往往依赖于具体问题.集成学习通过综合多个基分类器来预测结果,因此,其适应各种场景的能力较强,分类准确率较高.基于斯隆数字巡天(Sloan Digital Sky Survey,SDSS)计划恒星/星系中最暗源星等集分类正确率低的问题,提出一种基于Stacking集成学习的恒星/星系分类算法.从SDSS-DR7(SDSS Data Release 7)中获取完整的测光数据集,并根据星等值划分为亮源星等集、暗源星等集和最暗源星等集.仅针对分类较为复杂且困难的最暗源星等集展开分类研究.首先,对最暗源星等集使用10折嵌套交叉验证,然后使用支持向量机(Support Vector Machine,SVM)、随机森林(Random Forest,RF)、XGBoost(eXtreme Gradient Boosting)等算法建立基分类器模型;使用梯度提升树(Gradient Boosting Decision Tree,GBDT)作为元分类器模型.最后,使用基于星系的分类正确率等指标,与功能树(Function Tree,FT)、SVM、RF、GBDT、XGBoost、堆叠降噪自编码(Stacked Denoising AutoEncoders,SDAE)、深度置信网络(Deep Belief Network,DBN)、深度感知决策树(Deep Perception Decision Tree,DPDT)等模型进行分类结果对比分析.实验结果表明,Stacking集成学习模型在最暗源星等集分类中要比FT算法的星系分类正确率提高了将近10%.同其他传统的机器学习算法、较强的提升算法、深度学习算法相比,Stacking集成学习模型也有较大的提升.     

ASO-S卫星工程科学应用系统的数据库设计

先进天基太阳天文台(Advanced Space-based Solar Observatory, ASO-S)卫星是我国首颗太阳观测卫星, 主要观测太阳耀斑和日冕物质抛射以及产生它们的磁场结构. ASO-S卫星的科学应用系统是科学卫星工程的6大系统之一, 它连接科学用户和卫星数据, 为将卫星的科学数据转化为科学成果提供保障. 科学应用系统的数据库是连接软件与海量数据的枢纽, 为科学数据生产和用户服务及运行提供数据层的支撑. 介绍了科学应用系统的数据库架构设计、数据库的选择以及数据库性能优化和表样例. 这里的数据库包括观测计划、工程参数、运维日志、科学数据、定标数据和特征事件识别等数据库. 这些数据库的建设将为ASO-S卫星工程科学应用系统的顺利运行提供数据支撑, 也可以为未来其他科学卫星类似数据库的搭建提供参考和借鉴.     

An Automatic Method for Detecting Transients and Variable Sources in AST3 Survey Based on Image Subtraction and Random Forest

AST3-2 (the second Antarctic Survey Telescope) is located in Antarctic Dome A, the loftiest ice dome on the Antarctic Plateau. It produces a huge amount of observational data which require a more efficient data reduction program to be developed. Also the data transmission in Antarctica is much difficult, thus it is necessary to perform data reduction and detect variable and transient sources remotely and automatically in Antarctica, but this attempt is restricted by the unsatisfactory performance of the low power consumption computer in Antarctica. For realizing this purpose, to develop a new method based on the existing image subtraction method and random forest algorithm, taking the AST3-2 2016 dataset as the test sample, becomes an alternative choice. This method performs image subtraction on the dataset, then applies the principle component analysis to extract the features of residual images. Random forest is used as a machine learning classifier, and in the test a recall rate of 97% is resulted for the positive sample. Our work has verified the feasibility and accuracy of this method, and finally found out a batch of candidates for variable stars in the AST3-2 2016 dataset.     

城镇化与生态环境耦合的动态模拟——研究进展与展望

Urbanization and eco-environment coupling is a research hotspot.Dynamic simulation of urbanization and eco-environment coupling needs to be improved because the processes of coupling are complex and statistical methods are limited.Systems science and cross-scale coupling allow us to define the coupled urbanization and eco-environment system as an open complex giant system with multiple feedback loops.We review the current state of dynamic simulation of urbanization and eco-environment coupling and find that:(1)The use of dynamic simulation is an increasing trend,the relevant theory is being developed,and modeling processes are being improved;(2)Dynamic simulation technology has become diversified,refined,intelligent and integrated;(3)Simulation is mainly performed for three aspects of the coupling,multiple regions and multiple elements,local coupling and telecoupling,and regional synergy.However,we also found some shortcomings:(1)Basic theories are inadequately developed and insufficiently integrated;(2)The methods of unifying systems and sharing data are behind the times;(3)Coupling relations and the dynamic characteristics of the main driving elements are not fully understood or completely identified.Additionally,simulation of telecoupling does not quantify parameters and is not systemically unified,and therefore cannot be used to represent spatial synergy.In the future,we must promote communication between research networks,technology integration and data sharing to identify the processes governing change in coupled relations and in the main driving elements in urban agglomerations.Finally,we must build decision support systems to plan and ensure regional sustainable urbanization.