Bayesian分类方法在微小型飞行器视觉导航中的应用

在微小型飞行器的视觉导航中,图像分割的基本工作是将天地两部分分割开.这是形成整个视觉导航系统闭环控制的基础.而欲实现天地分割,则以特征线的提取为前提.本文分别从自底向上和自顶向下两种思路出发,利用广义Hough变换和基于多尺度最小方差的方法,进行了全局特征线的提取.然后,采用Bayesian分类的方法,实现了对微小型飞行器视觉导航至关重要的天地分割.通过具有典型意义的仿真实验,比较了两种方法所获得的结果,揭示了两种方法应用于微小型飞行器视觉导航的优点和缺点.     

一种基于证据距离的多分类器差异性度量

 多分类器系统因其能够显著提升分类精度而引发了广泛关注。多分类器系统中各子分类器间的差异性是提升融合分类精度的先决条件。提出了一种基于证据距离的分类器系统差异性度量,同时基于该度量提出一种多分类器系统构造方法。综合了既有差异性度量、所提新差异性度量以及在训练样本集上的分类性能等多个指标,实现了多分类器系统的有效构造。实验结果表明,所提差异性度量及多分类器系统构造方法是合理的,能有效提升融合分类精度。     

一种基于FPGA的航空发动机独立超转保护系统

针对航空发动机超转故障,提出一种基于现场可编程逻辑门阵列(FPGA)的独立超转保护系统.介绍了系统的总体设计方案、超转判断方法及故障诊断方式.该系统采用双双余度结构,通过线性分类器和时间窗内加权表决判断方法,提高超转保护功能可靠性.通过3层级机内自测试(BIT)监视静默失效,满足适航要求.硬件在回路仿真平台试验结果表明:该系统超转判断准确可靠,并能在连续判断超转后进入锁定保护状态,有效避免超转事故的发生.目前,研究成果已应用于某型发动机全权限数字控制(FADEC)系统中.      

基于多分类器决策融合的印鉴真伪鉴别方法

印鉴的自动识别对于实验银行印鉴数字化及计算机管理，实现银行对公业务的通存通兑有重大意义，符合票据法的要求，是银行业发展的需要，本文研究了模式识别领域中印鉴识别这一问题，提出了特征抽取和分类的新方法，基于差图像的成分统计特征抽取方法，发展了骨架图像的结构特征抽取方法，提出了特征点距离特征和笔划曲率特征内两种新的有效特征，本文提出了基于支持向量机（support vector machine,SVM)多分类器融合印鉴鉴别的方法，试验结果表明，本文的方法大大提高了印鉴的鉴别能力和可靠性，效果令人满意。     

Crater detection via genetic search methods to reduce image features

Recent approaches to crater detection have been inspired by face detection’s use of gray-scale texture features. Using gray-scale texture features for supervised machine learning crater detection algorithms provides better classification of craters in planetary images than previous methods. When using Haar features it is typical to generate thousands of numerical values from each candidate crater image. This magnitude of image features to extract and consider can spell disaster when the application is an entire planetary surface. One solution is to reduce the number of features extracted and considered in order to increase accuracy as well as speed. Feature subset selection provides the operational classifiers with a concise and denoised set of features by reducing irrelevant and redundant features. Feature subset selection is known to be NP-hard. To provide an efficient suboptimal solution, four genetic algorithms are proposed to use greedy selection, weighted random selection, and simulated annealing to distinguish discriminate features from indiscriminate features. Inspired by analysis regarding the relationship between subset size and accuracy, a squeezing algorithm is presented to shrink the genetic algorithm’s chromosome cardinality during the genetic iterations. A significant increase in the classification performance of a Bayesian classifier in crater detection using image texture features is observed.