Information-theoretic Approaches Based on Sequential Monte Carlo to Collaborative Distributed Sensors for Mobile Robot Localization

We consider the Sequential Monte Carlo (SMC) method for Bayesian inference applied to the problem of information-theoretic distributed sensor collaboration in complex environments. The robot kinematics and sensor observation under consideration are described by nonlinear models. The exact solution to this problem is prohibitively complex due to the nonlinear nature of the system. The SMC method is, therefore, employed to track the probabilistic kinematics of the robot and to make the corresponding Bayesian estimates and predictions. To meet the specific requirements inherent in distributed sensors, such as low-communication consumption and collaborative information processing, we propose a novel SMC solution that makes use of the particle filter technique for data fusion, and the density tree representation of the a posterior distribution for information exchange between sensor nodes. Meanwhile, an efficient numerical method is proposed for approximating the information utility in sensor selection. A further experiment, obtained with a real robot in an indoor environment, illustrates that under the SMC framework, the optimal sensor selection and collaboration can be implemented naturally, and significant improvement in localization accuracy is achieved when compared to conventional methods using all sensors.     

AptaCDSS-E: A classifier ensemble-based clinical decision support system for cardiovascular disease level prediction

Conventional clinical decision support systems are generally based on a single classifier or a simple combination of these models, showing moderate performance. In this paper, we propose a classifier ensemble-based method for supporting the diagnosis of cardiovascular disease (CVD) based on aptamer chips. This AptaCDSS-E system overcomes conventional performance limitations by utilizing ensembles of different classifiers. Recent surveys show that CVD is one of the leading causes of death and that significant life savings can be achieved if precise diagnosis can be made. For CVD diagnosis, our system combines a set of four different classifiers with ensembles. Support vector machines and neural networks are adopted as base classifiers. Decision trees and Bayesian networks are also adopted to augment the system. Four aptamer-based biochip data sets including CVD data containing 66 samples were used to train and test the system. Three other supplementary data sets are used to alleviate data insufficiency. We investigated the effectiveness of the ensemble-based system with several different aggregation approaches by comparing the results with single classifier-based models. The prediction performance of the AptaCDSS-E system was assessed with a cross-validation test. The experimental results show that our system achieves high diagnosis accuracy (>94%) and comparably small prediction difference intervals (<6%), proving its usefulness in the clinical decision process of disease diagnosis. Additionally, 10 possible biomarkers are found for further investigation.     

一种基于类支持度的增量贝叶斯学习算法

介绍增量贝叶斯分类器的原理,提出一种基于类支持度的优化增量贝叶斯分类器学习算法。在增量学习过程的样本选择问题上,算法引入一个类支持度因子λ,根据λ的大小逐次从测试样本集中选择样本加入分类器。实验表明,在训练数据集较小的情况下,该算法比原增量贝叶斯分类算法具有更高的精度,能大幅度减少增量学习样本优选的计算时间。     

基于粒子滤波器的人体目标跟踪

提出一种非常有效且具有良好鲁棒性的人体目标跟踪算法。由于传统的卡尔曼滤波不能很好地解决非线性、非高斯问题的跟踪,为此提出了一种新型的粒子滤波器跟踪算法。该算法采用加权的粒子集模型表示状态的分布,用迭代运算跟踪状态的变化,从而有效地解决了数据处理的量大和模型出现高维的问题。实验结果证明,该算法对固定摄像机单一背景下人体目标跟踪是快速且有效的。该算法可广泛应用于航空器位置的跟踪、噪声环境通信信号的估计、人体或车辆的跟踪。     

Web资源质量元数据度量方法研究

该文针对Web信息资源描述现状,将Web资源/信息质量评测模型WebQM理解为Web资源质量元数据模型。按照都柏林核心元数据集DC规范,使用DC元数据对网络资源进行描述,将质量元模型中质量评价准则映射为相对应的质量元数据,并讨论了如何使用质量元数据对质量准则进行度量。     

一种多分类器集成的手写体汉字识别方法

多分类器集成是手写体汉字识别领域的新方向。本文提出的多分类器集成方法通过改进的欧氏距离分类器将待识别汉字分类到某个粗分结果集中,然后根据粗分结果集选择1-N(one-against-rest)的SVM分类器对待识别汉字进行细分,最后用贝叶斯集成两级分类器。实验对国标一级汉字中的1034个手写汉字进行识别,证明了方案的有效性。     

虚拟膝关节镜手术中保持单元质量的体网格形变方法

在虚拟膝关节手术中,需要对膝关节进行大范围形变的实时模拟。本文针对四面体网格的膝关节模型,提出了采用LSD度量建立形变能量,然后将带约束的最优化问题转化为不带约束的最优化问题,最后通过带Armijo线性查找的非精确牛顿法求解最优化问题。在求解过程中,通过预估未知点的位置,减少迭代步数,提高了算法的效率。这种方法具有较好的保体积性,同时保证形变后的四面体网格不出现体元翻转和退化。该方法也能推广应用于其它类似的关节弯曲运动的变形中。     

n值逻辑系统中的随机化研究

利用赋值集的随机化方法,在n值逻辑系统Rn提出了公式的DRn-真度的概念。给出了两公式间的DRn-相似度与伪距离的概念,建立了DRn-逻辑度量空间。     

线面拓扑和度量关系的细分描述和计算方法

由于单独的拓扑关系、度量关系无法区分复杂要素之间的空间关系,在语义描述上存在歧义,提出一种二维空间R~2下空间关系的细分表达模型.该模型将面与线求交后,线面可以表示成n-1个拓扑和度量分量的序列矩阵,在语义上进行更细致的分段描述;采用折线节点微小圆求交的算法,考虑面带洞和目标的不确定性,归纳出线面拓扑关系细分判断的7个规则,以及长度、分割度等度量项的计算方法.最后以土地利用中图斑扣除线状地物计算地类面积为实例,验证了该模型和计算方法对解决实际问题的有效性.     

提取多尺度光照不变量的人脸识别

光照变化条件下的人脸图像识别一直以来都是图像处理中的热点和难点问题,为了提高人脸图像的识别率,提出了一种用于非均匀光照条件下人脸识别的算法.利用对数及二维小波变换的多尺度特性提取出人脸的光照不变量,然后运用PCA+LDA方法进行人脸特征提取,并采用基于欧氏距离的最近邻分类器进行识别.通过Matlab编程实验,在Yale B人脸库中达到了较高的识别率.