NSSC OpenIR
基于梯度提升决策树的量子科学实验卫星光学实验预测
Alternative TitleOptical Experiments Prediction of the Quantum Science Experiment Satellite Based on Gradient Boosting Decision Tree
罗中凯; 李虎; 胡钛
Source Publication空间科学学报
2020
Volume40Issue:1Pages:126-133; AR:0254-6124(2020)40:1<126:JYTDTS>2.0.TX;2-T
ISSN0254-6124
Language中文
Keyword光学实验 遥测数据 机器学习 阈值 Optical experiment Telemetry data Machine learning Thresholds value
Abstract量子科学实验卫星在轨运行期间完成4种光学实验,地面监测人员通过遥测参数阈值判断卫星是否进行光学实验、实验类型及实验结果.这种方法需要预先设定大量阈值,并且这些阈值需要根据在轨卫星重新设定,可扩展性较差.针对以上问题,提出一种基于机器学习的光学实验判别方法,将量子科学实验卫星的光学实验监测任务抽象为机器学习中的多元分类问题,构建分类模型,利用量子科学实验卫星的真实历史遥测数据对模型进行训练,并通过真实实验计划对训练得到的模型进行验证.实验结果表明,本文提出的方法在没有专家先验知识的前提下,判别准确率达到99%,可用于量子科学实验卫星光学实验的实时监测任务.提出的基于机器学习的判别方法具有较强的可扩展性,可应用于卫星在轨运行的其他监测任务.
Other AbstractThe quantum science experimental satellite mainly carry out four kinds of optical experiments during the orbital operation.The ground monitoring personnel mainly judged whether the satellite carried out optical experiments,experimental types and experimental results through the telemetry parameter threshold.This method requires a large number of thresholds to be set in advance,which requires a lot of manpower,and these thresholds need to be reset according to the on-orbit satellite,and the scalability is poor.Aiming at the above problems,this paper proposes an optical experiment discriminating method based on machine learning.Firstly,the optical experiment monitoring task of quantum science experimental satellite is abstracted into a multi-classification problem in machine learning.A classification model is constructed,and then the quantum science experimental satellite is used.The real historical telemetry data is used to train the model,and finally the trained model is verified by the real experimental plan.The experimental results show that the proposed method can achieve 99% accurate accuracy without the expert prior knowledge,and can be used for real-time monitoring tasks of quantum science experimental satellite optical experiments.The machine learning-based discriminant method proposed in this paper has strong scalability and can be widely extended to other monitoring tasks of satellite orbit operation.
Indexed ByCSCD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.nssc.ac.cn/handle/122/7444
Collection中国科学院国家空间科学中心
Affiliation1.罗中凯, 中国科学院国家空间科学中心
2.中国科学院大学, 北京
3.北京 100190
4.100049, 中国.
5.李虎, 中国科学院国家空间科学中心
6.中国科学院大学, 北京
7.北京 100190
8.100049, 中国.
9.胡钛, 中国科学院国家空间科学中心, 北京 100190, 中国.
10.Luo Zhongkai, National Space Science Center,Chinese Academy of Sciences
11.University of Chinese Academy of Sciences, Beijing
12.Beijing 100190
13.100049.
14.Li Hu, National Space Science Center,Chinese Academy of Sciences
15.University of Chinese Academy of Sciences, Beijing
16.Beijing 100190
17.100049.
18.Hu Tai, National Space Science Center,Chinese Academy of Sciences, Beijing 100190, China.
19.925216298@qq.com
Recommended Citation
GB/T 7714
罗中凯,李虎,胡钛. 基于梯度提升决策树的量子科学实验卫星光学实验预测[J]. 空间科学学报,2020,40(1):126-133; AR:0254-6124(2020)40:1<126:JYTDTS>2.0.TX;2-T.
APA 罗中凯,李虎,&胡钛.(2020).基于梯度提升决策树的量子科学实验卫星光学实验预测.空间科学学报,40(1),126-133; AR:0254-6124(2020)40:1<126:JYTDTS>2.0.TX;2-T.
MLA 罗中凯,et al."基于梯度提升决策树的量子科学实验卫星光学实验预测".空间科学学报 40.1(2020):126-133; AR:0254-6124(2020)40:1<126:JYTDTS>2.0.TX;2-T.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[罗中凯]'s Articles
[李虎]'s Articles
[胡钛]'s Articles
Baidu academic
Similar articles in Baidu academic
[罗中凯]'s Articles
[李虎]'s Articles
[胡钛]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[罗中凯]'s Articles
[李虎]'s Articles
[胡钛]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.