Test area of the SAGE survey

Sky surveys represent one of the most important efforts to improve developments in astrophysics,especially when using new photometric bands. We are performing the Stellar Abundance and Galactic Evolution(SAGE) survey with a self-designed SAGE photometric system, which is composed of eight photometric bands. The project mainly aims to study the stellar atmospheric parameters of ～0.5 billion stars in ～12 000 deg2 of the northern sky, which mainly focuses on Galactic astronomy, as well as some aspects of extragalactic astronomy. This work introduces the detailed data reduction process of the test field NGC 6791, including the data reduction of single-exposure images and stacked multi-exposure images, and properties of the final catalog.     

A PCA approach to stellar abundances I. testing of the method validity

The derivation of element abundances of stars is a key step in detailed spectroscopic analysis. A spectroscopic method may suffer from errors associated with model simplifications. We have developed a new method of deriving the various element abundances of stars based on the calibration established from a group of standard stars. We perform principal component analysis(PCA) on a homogeneous library of stellar spectra, and then use machine learning to calibrate the relationship between principal components and element abundances. By testing with spectral libraries S4 N and MILES, we find that our procedure provides good consistency when spectra from a homogeneous set of observations are used, and it could be expanded to stars with quite a wide range of stellar parameters, with both dwarfs and giants. Moreover, we discuss the four key factors that have a significant impact on the results of derived element abundances,including the resolution of the spectra, wavelength range, the signal-to-noise ratio(S/N) of spectra and the number of principal components adopted.     

Low-mass and high-mass supermassive black holes in radio-loud AGNs are spun-up in different evolution paths

How supermassive black holes(SMBHs) are spun-up is a key issue in modern astrophysics. As an extension to the study in Wang et al., here we address the issue by comparing the host galaxy properties of nearby(z 0.05) radio-selected Seyfert 2 galaxies. With the two-dimensional bulge+disk decompositions for the SDSS r-band images, we identify a dichotomy in various host galaxy properties for radio-loud SMBHs. By assuming that radio emission from the jet reflects a high SMBH spin, which stems from the well-known Blandford-Znajek mechanism of jet production, high-mass SMBHs(i.e., MBH 107.9 M⊙)have a preference for being spun-up in classical bulges, and low-mass SMBHs(i.e., MBH = 106-107 M⊙)in pseudo-bulges. This dichotomy suggests and confirms that high-mass and low-mass SMBHs are spun-up in different ways, i.e., a major "dry" merger and a secular evolution respectively.     

X-ray activity from different types of stars

X-ray emission is an important indicator of stellar activity. In this paper, we study stellar Xray activity using the XMM-Newton and LAMOST data for different types of stars. We provide a sample including 1259 X-ray-emitting stars, of which 1090 have accurate stellar parameter estimations. Our sample size is much larger than those used in previous works. We find a bimodal distribution of the X-ray to optical flux ratio(log(fX/fV)) for G and K stars. We interpret that this bimodality is due to two subpopulations with different coronal heating rates. Furthermore, using the full widths at half maxima calculated from Hα and Hβ lines, we show that these stars in the inactive peaks have smaller rotational velocities. This is consistent with the magnetic dynamo theory that presumes stars with low rotational velocities have low levels of stellar activity. We also examine the correlation between log(fX/fV) and luminosity of the excess emission in the Hα line, and find a tight relation between the coronal and chromospheric activity indicators.     

深度卷积神经网络支持下的遥感影像飞机检测

针对YOLOv3算法对小目标检测较差及出现较多漏检的问题,本文提出了一种优化的YOLOv3算法.首先使用K-means算法计算出与数据集相适用的锚框;其次将扩张卷积引入到YOLOv3网络,用来增强网络高层的感受野,改善小目标的检测效果;然后使用深度可分离卷积取代YOLOv3网络残差模块中的普通卷积,可减少计算量,从而得到一种新型卷积神经网络结构;最后在数据集上进行对比试验.结果表明,优化的YOLOv3算法能够检测出更多目标,降低漏检率,相比于YOLOv3算法,其召回率提高11.86%,F1-score提高2.99%.     

基于GPS新型L5信号的地表雪深反演研究

利用GPS多路径反射信号测量地表雪深具有全天候和高时空分辨率的特点,因此其可作为一种代替气象站监测雪深的新手段。然而,先前大多数研究仅使用了GPS L1和L2C波段信噪比数据探测积雪深度。为验证新型的L5信号在雪深反演方面的优越性,本文阐述了GPS-R技术反演雪深的原理,利用Lomb-Scargle周期图法所处理的受积雪表层影响的信噪比数据计算了频谱振幅强度,通过获取频谱特征值与天线高度的关系求解雪深值,最后分别与L1反演结果和实测雪深数据进行了对比。试验结果表明：与现有的GPS-R测量雪深结果相比,利用新型的L5反射信号反演地表雪深的精度更佳;采用GPS-R技术探测雪深对把握测站区域内的雪深变化情况和淡水资源储量具有重要价值。     

一种基于GA-BP-MC神经网络的高铁桥墩沉降预测模型

提出一种基于马尔科夫链修正的遗传BP神经网络预测模型（GA-BP-MC）,利用遗传算法的全局寻优能力初始化BP神经网络权值和阈值,初步建立GA-BP神经网络预测模型,结合马尔科夫链的无后效性修正模型预测值,形成高精度GA-BP-MC神经网络变形预测模型。结合高铁桥墩沉降数据,分别与BP神经网络、GA-BP神经网络预测模型进行对比,结果表明,该预测模型精度最高。     

基于磁场解析弹体飞行俯仰角的算法研究

为了解决弹体飞行俯仰角解算问题,对利用地磁传感器解算弹体俯仰角的算法进行研究。 通过分析算法,研究了利用地磁传感器解算俯仰角的原理方程,并对此进行了仿真试验和实物实验。 试验结果表明:利用地磁传感器解算方法可以实现旋转弹体俯仰角的测量,并有效地抑制了误差,实用价值高。     

降低受信仪行程开关返程误差方法的分析

由于受信仪的行程开关返程误差较大,导致了运行、射界范围的控制精度不高。 针对返程误差偏大实际问题,研究了受信仪的行程开关工作原理,建立了其工作过程数学模型,采用复合形法开展了参数优化设计。 依据 PDCA 程序,应用因果图分析方法获得影响误差的主要因素,并逐一采取相应的解决措施。 经过验证,其实现了凸轮行程开关的返程误差减小到 0. 5°以下,提高了发射装置运行、射界范围的控制精度,达到了预期的目标。     

PSD法测量大型射电望远镜副面位姿

指向精度是大型射电望远镜天线具有挑战性的关键技术指标.在望远镜运行中,方位俯仰角的变化、重力以及日照等对副面撑杆的综合影响会引起望远镜的副面位姿改变从而引起指向误差的增加和天线效率的降低.基于天马65 m射电望远镜,使用位置传感器装置(PSD)法构建了副面位置偏移量测定系统,可以实时采集副面的3维位移数据,构建重力模型,将测试结果与射电法构建的现有副面模型进行对比,有较好的一致性.此外,该系统可以分析日照(引起撑腿局部温度效应)引起的副面位移情况,也可以监视风载和瞬时启停对副面位姿的影响.