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| 油指纹鉴别中主成分分析法的实现与可视化表达 | |
| 引用本文： | 王春晓,杨晓梅,苏奋振,季民,王鑫平.油指纹鉴别中主成分分析法的实现与可视化表达[J].山东科技大学学报(自然科学版),2009,28(6):100-106. |
| 作者姓名： | 王春晓 杨晓梅 苏奋振 季民 王鑫平 |
| 作者单位： | 1. 中国科学院,地理科学与资源环境研究所,北京,100101;山东科技大学,测绘科学与工程学院,山东,青岛,266510 2. 中国科学院,地理科学与资源环境研究所,北京,100101 3. 山东科技大学,测绘科学与工程学院,山东,青岛,266510 4. 国家海洋局,北海环境监测中心,山东,青岛,266033 |
| 基金项目： | 中国科学院知识创新重要方向项目 |
| 摘 要： | 基于Oracle的油指纹数据库管理系统,提出了油指纹鉴别方法的软件设计架构,并讨论了.NET环境下使用C语言实现主成分分析算法以及点图绘制技术。结合主成分分析方法在溢油鉴别中的应用,从面向对象的思想出发,进行接口和类的设计,实现了主成分分析的算法以及可视化表达,基于此架构可以设计出更多的油指纹鉴别方法,具有灵活方便的扩展性。此方法能够针对大量油样数据进行分析并能够快速缩小可疑溢油源的范围,提高溢油鉴别的效率。 |
| 关 键 词： | 主成分分析 油指纹 可视化 |
Realization and Visualized Representation of Principal Component Analysis in Oil Fingerprinting Techniques |
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| WANG Chun-xiao,YANG Xiao-mei,SU Fen-zhen,JI Min,WANG Xin-ping.Realization and Visualized Representation of Principal Component Analysis in Oil Fingerprinting Techniques[J].Journal of Shandong Univ of Sci and Technol: Nat Sci,2009,28(6):100-106. | |
| Authors: | WANG Chun-xiao YANG Xiao-mei SU Fen-zhen JI Min WANG Xin-ping |
| Institution: | WANG Chun-xiao,YANG Xiao-mei, SU Fen-zhen,JI Min,WANG Xin-ping ( 1. Institute of Geographic Sciences and Resources Environment, CAS, Beijing 100 t01, China 2. Geomatics College, SUST, Qingdao, Shandong 266510, China 3. The Center of North Sea Environment Monitoring,State Oceanic Administration, Qingdao, Shandong 266033, China) |
| Abstract: | Based on Oracle oil fingerprinting database management system, the authors put forward the software design framework of oil fingerprinting methods and then, discussed the methods of realizing the algorithm of principalcomponent analysis and drawing technique of point graphics by using C Language. From the applications of principal component analysis in oil fingerprinting identification, the interfaces and classes were designed with the object-oriented thinking and, the algorithm and visualization were realized with flexible and convenient expansion ability. This method is able to analyze a large number of oil sample data and narrow the scope of the suspected oil sources intuitively and quickly, so, the efficiency of the spilled oil identification is improved. |
| Keywords: | principal component analysis oil fingerprinting techniques visualization |
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