Optical fiber positioning based on four-quadrant detector with Gaussian fitting method

With the development of large-scale spectral surveys, fiber positioning technology has been developing rapidly. Because of the performance advantages of a four-quadrant(4Q) detector, a fiber positioning and real-time monitoring system based on the 4Q detector is proposed. The detection accuracy of this system is directly determined by the precision of the center of the spot. A Gaussian fitting algorithm based on the 4Q detector is studied and applied in the fiber positioning process to improve the calculated accuracy of the spot center. The relationship between the center position of the incident spot and the detector output signal is deduced. An experimental platform is built to complete the simulated experiment. Then we use the Gaussian fitting method to process experimental data, compare the fitting value with the theoretical one and calculate the corresponding error.

An accelerated direct demodulation method for image reconstruction using spherical data from the hard X-ray modulation telescope

The hard X-ray modulation telescope (HXMT) mission is mainly devoted to performing an all-sky survey at 1 250 keV with both high sensitivity and high spatial resolution. The observed data reduction as well as the image reconstruction for HXMT can be achieved by using the direct demodulation method (DDM). However the original DDM is too computationally expensive for multi-dimensional data with high resolution to be employed for HXMT data. We propose an accelerated direct demodulation method especially adapted for data from HXMT. Simulations are also presented to demonstrate this method.

Data processing and initial results of Chang'e-3 lunar penetrating radar

To improve our understanding of the formation and evolution of the Moon,one of the payloads onboard the Chang'e-3(CE-3) rover is Lunar Penetrating Radar(LPR). This investigation is the first attempt to explore the lunar subsurface structure by using ground penetrating radar with high resolution. We have probed the subsurface to a depth of several hundred meters using LPR. In-orbit testing, data processing and the preliminary results are presented. These observations have revealed the configuration of regolith where the thickness of regolith varies from about 4 m to 6 m.In addition, one layer of lunar rock, which is about 330 m deep and might have been accumulated during the depositional hiatus of mare basalts, was detected.

Data processing and initial results from the CE-3 Extreme Ultraviolet Camera

The Extreme Ultraviolet Camera(EUVC) onboard the Chang'e-3(CE-3)lander is used to observe the structure and dynamics of Earth's plasmasphere from the Moon. By detecting the resonance line emission of helium ions(He+) at 30.4 nm, the EUVC images the entire plasmasphere with a time resolution of 10 min and a spatial resolution of about 0.1 Earth radius(RE) in a single frame. We first present details about the data processing from EUVC and the data acquisition in the commissioning phase, and then report some initial results, which reflect the basic features of the plasmasphere well. The photon count and emission intensity of EUVC are consistent with previous observations and models, which indicate that the EUVC works normally and can provide high quality data for future studies.

Data processing and error analysis for the CE-1 Lunar microwave radiometer

The microwave radiometer (MRM) onboard the Chang' E-1 (CE-1) lunar orbiter is a 4-frequency microwave radiometer, and it is mainly used to obtain the brightness temperature (TB) of the lunar surface, from which the thickness, temperature, dielectric constant and other related properties of the lunar regolith can be derived. The working mode of the CE-1 MRM, the ground calibration (including the official calibration coefficients), as well as the acquisition and processing of the raw data are introduced. Our data analysis shows that TB increases with increasing frequency, decreases towards the lunar poles and is significantly affected by solar illumination. Our analysis also reveals that the main uncertainty in TB comes from ground calibration.

The Science Operations and Data Center (SODC) of the ASO-S mission

A ground data analysis center is very important to the success of a mission.We introduce the Science Operations and Data Center(SODC)for the ASO-S mission,which consists of a scientific operation subcenter,a data management subcenter,a data analysis subcenter and a user service subcenter.The mission planning process,instrument observation modes and the data volume are presented.We describe the data flow and processing procedures from spacecraft telemetry to high-level science data,and the long-term archival as well.The data policy and distributions are also briefly introduced.

Detecting HI Galaxies with Deep Neural Networks in the Presence of Radio Frequency Interference

In the neutral hydrogen(H I)galaxy survey,a significant challenge is to identify and extract the H I galaxy signal from the observational data contaminated by radio frequency interference(RFI).For a drift-scan survey,or more generally a survey of a spatially continuous region,in the time-ordered spectral data,the H I galaxies and RFI all appear as regions that extend an area in the time-frequency waterfall plot,so the extraction of the H I galaxies and RFI from such data can be regarded as an image segmentation problem,and machine-learning methods can be applied to solve such problems.In this study,we develop a method to effectively detect and extract signals of H I galaxies based on a Mask R-CNN network combined with the PointRend method.By simulating FAST-observed galaxy signals and potential RFI impact,we created a realistic data set for the training and testing of our neural network.We compared five different architectures and selected the best-performing one.This architecture successfully performs instance segmentation of H I galaxy signals in the RFI-contaminated time-ordered data,achieving a precision of 98.64%and a recall of 93.59%.

基于一致性算法的地下流体观测数据融合处理方法

传统地下流体观测数据融合处理方法以观测数据高差处理为主,在数据处理过程中存在冗余问题,为此设计基于一致性算法的地下流体观测数据融合处理方法。首先,融合地下流体观测数据维度,分析各个尺度数据在观测位置上的差异,并结合重力场参数,处理观测误差。其次,采用一致性算法处理地下流体观测冗余数据,将地下流体观测数据定义为集群数据,均衡数据融合处理负载,确保地下流体观测数据的有效性。最后,进行实验分析。实验结果表明,该方法的吞吐性能与可用性能均高于对照组,数据融合处理效果更佳。

废气在线比对监测中的常见问题分析

废气在线比对监测技术是一种可靠、高效、实时的环境监测方法,被广泛应用于工业、能源、化工、交通等领域。然而,废气在线比对监测中常常存在仪器误差、采样系统问题、数据处理和比对问题等常见问题。本文对废气在线比对监测技术进行了介绍,详细分析了其中的常见问题及其解决方法。通过研究发现,正确选择应用仪器、采样系统、数据处理和比对方法是保证废气在线比对监测技术精度和可靠性的关键。本文最后对该技术的应用前景和发展趋势进行了展望,并提出了相应的建议。

定点倾斜观测映震能力综述

搜集整理了我国定点倾斜观测近30年来已发表的观测成果,归纳出常用的倾斜资料处理方法,通过统计在历次地震发生前、震中附近一定范围内的定点倾斜观测是否出现异常以及震前异常的幅度,得到了倾斜前兆异常量级、倾斜异常与震中和震级的统计关系,对倾斜台网未来的发展和台网布局具有一定的参考意义。

接触式非球面轮廓测量的数据处理模型

在分析接触式轮廓测量仪误差的基础上,以最小二乘法为基本数学理论,提出了一种能同时校正由于非球面镜放置时存在的X、Y方向的倾斜、X、Y、Z方向的偏心,曲率半径不准确、以及由于轮廓仪的旋转中心与探测头的零点之间随机的微小偏移所造成的测量误差的数学模型。数学模拟表明,该模型在校正上述误差源上具有极高的精度,对1 cm以下的偏心和0.1°以下的倾斜可无误差地恢复,对曲率半径的恢复也极为有效。实际工程应用的结果表明,模型是可靠有效的,为接触式非球面轮廓测量提供了一个宽松的镜子放置条件及自动拟合最佳曲率半径的功能。特别是自动校正由于轮廓仪的旋转中心与探测头的零点之间的随机微小偏移所造成的测量误差的功能提高了测量仪的测量精度和测量结果的重复性。

用时间序列分析法进行建筑物沉降观测数据处理的研究

本文将时间序列分析法用于建筑物沉降观测数据处理,给出了用此法进行沉降观测数据处理时模型阶数确定的统计检验方法、模型参数估计以及预报分析的方法,以某建筑物沉降观测第30期的测量成果为例说明应用此法的全过程,并对计算结果进行了分析比较。

遥测系统降雨观测粗差修正研究

通过对遥测系统雨量观测资料中粗差特点和产生方式的分析 ,提出了借鉴曲面拟合算法的思路 ,利用抗差最小二乘估计和雨量空间分布函数修正粗差的方法。对闽江七里街流域 43个雨量站 46场暴雨 2 6万余条记录 ,用生成的误差系列 ,进行模拟估计 ,分析了此方法的有效性和合理性 ,获得了较好的结果。并且把修正过的降雨资料应用于新安江模型 ,对计算的流量和实测流量进行比较分析 ,达到了较满意的效果 ,表明本方法有较好的实用性。

1.2米地平式望远镜指向模型的研究(1.全天指向模型的建立)

介绍了云南天文台1.2米地平式望远镜用于天文观测和图像采集处理的方法,建立了新的、独特的全天指向模型,大大提高了该望远镜的指向精度,达到1″,并在多年的实际应用中得到验证。

基于小波包的时间序列变点探测算法

基于小波能对信号的各个频率段进行分离的原理,设计了基于小波包分析的变点探测算法,并以此来研究时间序列的突变.通过对长江宜昌站的年径流水文时间序列进行实验分析发现,在过去120a,长江宜昌站的年最小流量序列和年平均流量序列的均值都极有可能存在着突变,而且其变化趋势都是均值明显减小.该方法不需要对时间序列作任何概率分布和相依性的假定,便能方便地通过调节小波包变点探测算法的参数应用于其他领域.

搭载在神州二号飞船留轨舱上的超软X射线探测器——观测结果简介

简要介绍搭载在神州二号飞船留轨舱上的超软X射线探测器的观测结果,它们涉及宇宙γ射线暴,太阳X射线暴,高能带电粒子和软X射线背景辐射,供分析、讨论和研究。

船载设备测量数据处理结果的综合分析方法

数据处理技术包括数据诊断与误差分析技术、数学建模与处理技术以及数值微分与弹（轨）道计算技术等，而测量误差分析与处理技术是数据处理技术的核心。如何分析和处理误差贯穿于数据处理的全过程，直接影响弹／轨道计算的精度。对误差的分析和处理是测量船数据处理工作中的重要内容。本文根据测量船数据处理实际情况介绍了测量船数据分析方法，并结合任务实例介绍了测量船误差分析和处理的详细过程。

一种新的重力潮汐数据预处理和分析方法

较系统地介绍了由比利时皇家天文台Vauterin博士研制的Tsoft重力潮汐数据预处理方法和由日本国立天文台Tamura博士研制的Baytap G潮汐数据分析方法。作为应用 ,利用武汉九峰动力大地测量学观测站超导重力仪 1999年观测数据 ,先实施预处理 ,再作数据分析 ,获得地球潮汐常数。数值结果说明 ,利用Tsoft方法可有效消除各种干扰信号 ,由Baytap

空间碎片跟踪图像序列的目标识别方法

自动化观测已成为目前空间碎片光学观测的发展趋势,相应的完全无人工干预的目标自动识别成为迫切需要研究的课题.开环跟踪依据历史根数引导望远镜进行碎片的跟踪观测,是一种实现简单并且很稳健的观测方法.针对开环跟踪获取的观测图像,开展了目标在像素空间内的点列特征分析,提出使用像点簇识别算法来进行目标的自动识别,并对3种不同的簇识别算法实现进行了比较.