关于命题公式的主范式

本文讨论了两个命题公式的一次复合的主析(合)取范式与它们的主析(合)取范式之间的关系。     

Face Recognition Based on Support Vector Machine and Nearest Neighbor Classifier

Support vector machine (SVM), as a novel approach in pattern recognition, has demonstrated a success in face detection and face recognition. In this paper, a face recognition approach based on the SVM classifier with the nearest neighbor classifier (NNC) is proposed. The principal component analysis (PCA) is used to reduce the dimension and extract features. Then one-against-all stratedy is used to train the SVM classifiers. At the testing stage, we propose an al-     

天津市各区县经济发展水平的多元统计分析

本文选取了10项适当的主要经济指标对天津市18个区（县）的经济发展水平进行系统聚类分析和主成分分析，综合评价了各区县的经济发展水平．     

地理信息系统中的三维可视化方法

针对地理信息系统中的三维可视化问题,探讨了ArcObjects的技术特点及其在解决地图三维可视化问题中的应用潜力和存在的问题,并采用VB与ArcObjects相结合的办法对地图三维可视化进行了初步实践,取得了成功的应用。通过某城市地形的三维可视化,探索了.ArcObjects在表达较大数据量数字地形的能力,开发了三维场景的飞行仿真系统原型。并对某城市地形进行了飞行仿真。     

上市公司财务状况分析的研究

投资者在进行财务分析时往往面临着众多的财务指标选择，增加了分析的难度。由此，需要对财务指标加以分类汇总。本文从实证角度，利用主成份分析法对上市公司财务状况进行分析研究。     

用催化动力学法由一次实验同时测定铬（Ⅲ）和铬（Ⅵ）

用H2O2氧化邻-联（二）茴香胺的反应作为指示反应可用催化动力学法经一次实验同时测定Cr（Ⅲ）和Cr（Ⅵ），但多元线性回归法不能用于此体系，因为其表现反应级数不是常数.用“速率谱”的概念可用主成分分析处理数据.实验证明对Cr（Ⅲ）和Cr（Ⅵ）的同时测定H2O2和邻-联（二）茴香胺的最佳浓度分别为0.6和6×10-3molL-1，pH为4.0.可测（1～6）×10-5molL-1的Cr（Ⅲ）和2×10-6～1×10-5mol·L-1的Cr（Ⅵ）.误差不超过10％.     

States of ignorance and ignorance of states: Examining the Quantum Principal Principle

Earman (2018) has recently argued that the Principal Principle, a principle of rationality connecting objective chance and credence, is a theorem of quantum probability theory. This paper critiques Earman's argument, while also offering a positive proposal for how to understand the status of the Principal Principle in quantum probability theory.     

一类具有不同Virasoro-向量的顶点算子子代数

根据X_I型Cartan矩阵的一类主子矩阵_J,构造出无扭仿射李代数■的同构于g(A_J)的子代数■_J的导代数■_J,以及■_J-模V_■(l,0).证明了V_■(l,0)不仅是顶点算子代数.而且是V_■(l,0)的具有不同Virasoro-向量的顶点算子子代数.     

Cooperative Sensor Anomaly Detection Using Global Information

Sensor networks are deployed in many application areas nowadays ranging from environment monitoring, industrial monitoring, and agriculture monitoring to military battlefield sensing. The accuracy of sensor readings is without a doubt one of the most important measures to evaluate the quality of a sensor and its network. Therefore, this work is motivated to propose approaches that can detect and repair erroneous (i.e., dirty) data caused by inevitable system problems involving various hardware and software components of sensor networks. As information about a single event of interest in a sensor network is usually reflected in multiple measurement points, the inconsistency among multiple sensor measurements serves as an indicator for data quality problem. The focus of this paper is thus to study methods that can effectively detect and identify erroneous data among inconsistent observations based on the inherent structure of various sensor measurement series from a group of sensors. Particularly, we present three models to characterize the inherent data structures among sensor measurement traces and then apply these models individually to guide the error detection of a sensor network. First, we propose a multivariate Gaussian model which explores the correlated data changes of a group of sensors. Second, we present a Principal Component Analysis (PCA) model which captures the sparse geometric relationship among sensors in a network. The PCA model is motivated by the fact that not all sensor networks have clustered sensor deployment and clear data correlation structure. Further, if the sensor data show non-linear characteristic, a traditional PCA model can not capture the data attributes properly. Therefore, we propose a third model which utilizes kernel functions to map the original data into a high dimensional feature space and then apply PCA model on the mapped linearized data. All these three models serve the purpose of capturing the underlying phenomenon of a sensor network from its global view, and then guide the error detection to discover any anomaly observations. We conducted simulations for each of the proposed models, and evaluated the performance by deriving the Receiver Operating Characteristic (ROC) curves.     

主分量分析在植被演替研究中之作用的探讨

本文用E、S两组主分量分析,对陕西省铜川地区植被次生演替进行分析。其结果表明,主分量分析是一种在自然条件复杂的较大区域内,研究植被次生演替的有效方法,它有助于认识植被次生演替与生态条件的关系及演替过程中群落性质的变化特点。