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类星体自动识别方法研究
周虹
学位类型工学硕士
导师罗曼丽
1999-03-01
学位授予单位中国科学院自动化研究所
学位授予地点中国科学院自动化研究所
学位专业模式识别与智能系统
关键词类星体观测光谱 发射峰 红移 Hough变换 神经网络
摘要类星体自动识别是一个新兴的研究领域,它将充分运用天文、模式识别、 人工智能、信号和图像处理等多方面的知识,协助天文物理学家识别类星体, 解决宇宙奥秘。本论文的长远目标是配合国家LAMOST工程,建立一个天文 观测光谱自动分析和数据处理软件包,充分发挥LAMOST的潜力;近期目标 是根据观测的天文光谱,识别宇宙中的类星体,类星体观测光谱中的发射峰, 和确定类星体的红移。 为实现类星体自动识别的近期目标,本文对类星体自动识别涉及到的一些 关键问题,如识别真假发射峰的分类问题,发射峰的匹配问题,Hough变换方 法、神经网络学习和训练方法的应用,以及红移验证等问题进行了深入的研究, 提出了一些创新的思想和方法,设计并实现了基于Hough变换方法和神经网络 方法的类星体自动识别系统。 本论文的主要工作内容包括以下几点: 1.本文根据现有的关于类星体发射谱的知识,用高斯轮廓方法,模拟了类星体 静止和红移后的发射谱。通过分析这些模拟的发射谱,得到了类星体发射谱 随红移变化的规律。为以后的研究打下了良好的基础。 2.根据天文光谱、天文专家经验,和类星体发射峰的特点,本文系统分析和研 究了识别类星体发射峰的特性,提出了发射峰模板匹配法,发射峰分类法、 前馈神经网络分类法和由红移值反推发射峰法等识别类星体发射峰的方法, 并对每种方法的具体解决方案进行了总结和评价。 3.由于红移是识别类星体的重要因素,本文系统分析了确认类星体红移的方法。 根据天文光谱信噪比低,有关知识缺乏等特点,提出了基于红移公式的公式 法和基于Hough变换的概率统计法两种确认红移的方法。此外,在确定红 移可靠性研究方面,根据不确定性推理和相似度度量等原理,提出了静止发 射谱验证法和红移后发射谱验证法。 4.基于上述所提出的方法,本文设计并实现了基于Hough变换方法和神经网 络方法的类星体识别系统。该系统由预处理、特征提取、真假发射峰识别、 Hough变换、发射峰匹配、红移验证,统计和学习等模块组成。通过对1 7 0 幅观测光谱的测试,本系统的识别率为8 3%。实验结果说明,基于Hough变 换方法和神经网络方法的类星体自动识别系统具有简单、鲁棒性好、识别率 高、易扩展等优点,尤其是在本系统中基于神经网络方法的真假发射峰识别 模块的引入,大大提高了只基于Hough变换类星体识别系统的识别率。 此外,目前从国内外的文献资料中,还没有看到
其他摘要Quasar automatic recognition is a new and interdisciplinary problem on astronomy, pattern recognition, artificial intelligence, signal and image processing. The research on quasar automatic recognition is to assist astrophysicists to explore the mystery of the universe, uncover the features of quasars. Its long-term goal is to build a software package for automatic astronomical spectrum analysis and data processing as required by the LAMOST project. Its short-term objective is to identify quasars from observed astronomical spectrums, to recognize the emission peaks in quasar spectrums, and to determine the red shifts of quasars. To achieve this short-term objective, we thoroughly studied the critical problems in quasar recognition, such as the classification problem of true/false emission peaks in observed spectrums, the matching problem of emission peaks, Hough transform, the learning and training ways in neural networks and the verification problem of red shifts. During this process, we introduced some novel ideas and techniques, proposed and implemented a stratified approach for quasar recognition based on Hough transform and neural networks. The main points of our work are as follows. 1. We collect current knowledge on quasars and emission spectrums, simulate quasar's still emission spectrum and its red shifted emission spectrums. From these emission spectrums, we further clarify the nature of quasar emission spectrums changing with the red shifts. The work provides a sound foundation for further study on quasar recognition. 2. On the basis of the quasar spectrums and astronomical expert knowledge, we propose several ways to recognize emission peaks through analyzing the natures of quasar emission peaks. The ways include emission peaks matching, classifying, back- propagation neural networks and identifying red shifts at first. For each one of the above four ways, we explore and evaluate several specific techniques for its implementation. 3. Due to the low ratio of quasar signals to noise, and lack of expert knowledge, etc. we introduce two approaches to recognize the red shifts, one based on the red shift formula, the other based on Hough transform. Concerning the study of the reliability of red shifts, we propose two verification ways based on still emission spectrum and red shifted emission spectrums respectively. 4. Finally, we present an automatic quasar recognition system by combining artificial neural networks with Hough transform. The system is composed of several functional units, namely preprocessing, peak extraction, true/false peak identification, peaks mapping, Hough transform, red shift determination, final system verification, statistics and learning etc. The system's recognition rate is up to 83% in experiments with 170 observed spectrums. The experiments' results show that the quasar recognition system based on Hough transform has the advantages of simpleness, robustness, efficiency and
馆藏号XWLW514
其他标识符514
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/7261
专题毕业生_硕士学位论文
推荐引用方式
GB/T 7714
周虹. 类星体自动识别方法研究[D]. 中国科学院自动化研究所. 中国科学院自动化研究所,1999.
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