标志点定向系统几何精度因子的计算方法

为了满足在准备发射导弹时估计精度以及在建立发射阵地时优化标志点布局的需求,借鉴卫星导航定位系统的几何精度因子（GDOP）的定义,提出了适用发射车的标志点定向系统的GDOP的计算方法。根据多变量函数协方差合成公式,建立了定向误差的数学模型,以此为基础结合车体基本水平的特点,设计了该系统定向误差GDOP简化公式。计算结果显示GDOP值在以标志点为顶点的三角形附近区域的分布特点为：三角形内部的值比外部小;正三角形比其他形状的三角形能在更大范围内取得较小的值。数值仿真和外场实验证明了该方法的正确性。     

基于加权几何精度因子(WGDOP)的地基导航系统评价方法

为了评价卫星导航系统中位置估计的性能,GDOP作为一种衡量测量误差和定位误差之间几何关系的评价方法被广泛应用;由于地基导航系统各个单元的测量误差不同,且受用户距离影响不可忽略,需要对与距离有关的评价标准进行研究;因此,提出使用WGDOP代替GDOP作为评判标准,从分析地基导航系统误差方差入手,对WGDOP表达式进行详细分析,通过系统比较两种定位因子,说明采用WGDOP作为地基导航系统基站布设评价标准,准确性高;研究结果表明,采用加权几何精度因子作为评价标准,可以更适应地基导航系统的实际使用环境,对系统定位性能进行更精确的评价,对精度因子评估有明显改善。     

Sensor planning with Bayesian decision theory

In this paper ongoing work on an approach for planning sensing actions and controlling intelligent, purposive robotic systems is presented. The method uses Bayesian decision analysis (BDA) for deciding what sensing actions should be performed. This offers a probabilistic framework that provides a more dynamic and modular behaviour than traditional rule based planners. Experiments show that the Bayesian sensor planning strategy is capable of controlling an autonomous mobile robot operating in partly known environments.     

Bayesian estimation of a decision using information theory

The problem of modeling the revision of the information of a decision maker based on the information of the expert sources is considered. The basic model assumes that the information of the decision maker and expert sources is in the form of the probability mass functions. The modeling approach is Bayesian estimation, which relies on Kullback entropy and Shannon entropy for information measurement, and produces a unique solution. Modeling of the problem not only considers information about the statistical dependence of the expert sources, but also uses information to measure the quality and importance of the individual expert sources in the form of rank ordering. The outcome shows that the effects of the dependence and rank ordering of the expert sources on the final decision cannot be isolated, In a special case where this isolation is possible, the effect of rank ordering decreases with the increase in the value of the correlation coefficient from -1 to +1, and the effect of the correlation never exceeds the effect of rank ordering. Sensitivity analysis is performed to explore other properties of the model related to the influence of the decision maker and expert sources. Extensions of the basic modeling to group decision making, group consensus, and mean value information are presented     

基于贝叶斯决策的运动目标检测方法

传统混合高斯背景建模存在难以解决背景复杂以及阴影等因素影响视频运动目标检测效果的问题,为此提出了一种基于贝叶斯决策的运动目标检测方法。该方法利用帧间差分进行像素变化检测,将像素粗分为变化像素和非变化像素;对于变化像素中的运动点和静止点,通过统计确立有效的数据结构,其中显著颜色分布统计量用来描述静止点,而显著颜色同现统计量描述运动点;从数据结构中提取颜色特征矢量,将特征矢量中的静止点和运动点按照贝叶斯决策规则进一步分类为背景点、前景点和颜色相似点。对颜色相似点进行局部加权处理以达到正确检测的目的;通过融合静止点集、运动点集和加权后的颜色相似点集结果提取出前景运动目标。仿真实验表明,该方法能够在不同复杂背景下较准确地检测出视频中的运动目标,相比传统算法具有较强的鲁棒性。     

Applications of Bayesian decision theory to intelligent tutoring systems

The purpose of this paper is to consider some applications of Bayesian decision theory to intelligent tutoring systems. In particular, it will be indicated how the problem of adapting the appropriate amount of instruction to the changing nature of student's capabilities during the learning process can be situated within the general framework of Bayesian decision theory. Two basic elements of this approach will be used to improve instructional decision making in intelligent tutoring systems. First, it is argued that in many decision-making situations the linear loss model is a realistic representation of the losses actually incurred. Second, it is shown that the psychometric model relating observed test scores to the true level of functioning can be represented by Kelley's regression line from classical test theory. Optimal decision rules will be derived using these two features.     

A vision-based method for weeds identification through the Bayesian decision theory

One of the objectives of precision agriculture is to minimize the volume of herbicides that are applied to the fields through the use of site-specific weed management systems. This paper outlines an automatic computer vision-based approach for the detection and differential spraying of weeds in corn crops. The method is designed for post-emergence herbicide applications where weeds and corn plants display similar spectral signatures and the weeds appear irregularly distributed within the crop's field. The proposed strategy involves two processes: image segmentation and decision making. Image segmentation combines basic suitable image processing techniques in order to extract cells from the image as the low level units. Each cell is described by two area-based measuring relationships between crop and weeds. The decision making determines the cells to be sprayed based on the computation of a posterior probability under a Bayesian framework. The a priori probability in this framework is computed taking into account the dynamic of the physical system (tractor) where the method is embedded. The main contributions of this paper are: (1) the combination of the image segmentation and decision making processes and (2) the decision making itself which exploits a previous knowledge which is mapped as the a priori probability. The performance of the method is illustrated by comparative analysis against some existing strategies.     

基于最小风险的贝叶斯决策理论相关反馈方法*

在对之前的相关反馈技术的问题和不足进行总结的基础上,提出一种基于最小风险的贝叶斯决策理论相关反馈方法,通过一种自适应机制产生记忆,同时应用最小风险来使用户获得满意的检索结果。实验结果表明该方法准确率高、响应速度快。     

Bayesian decision theory for support vector machines: Imbalance measurement and feature optimization

Classification approaches usually present the poor generalization performance with an apparent class imbalance problem. Surely, a measures of the quality of the possible models reflected the remaining uncertainty in the class imbalance on learning. The purpose of our learning method is to lead an attractive pragmatic expansion scheme of the Bayesian approach to assess how well it is aligned with the class imbalance problem. Thus, we propose a method with a model assessment of the interplay between various classification decisions using probability, corresponding decision costs, and quadratic program of optimal margin classifier called: Bayesian Support Vector Machines (BSVMs) learning strategy. In the framework, we did modify in the objects and conditions of primal problem to reproduce an appropriate learning rule for an observation sample. The experiments on several existing data sets showed that BSVMs may appropriately capture the true relationship between the inputs and outputs by experimental evidence.     

ETM+全色波段影像的几何精校正

为确保遥感影像为森林资源调查和监测提供更好的服务,必须对影像进行图像预处理,而图像的几何校正是图像预处理中关键一步.传统的基于TM影像的几何精校正只是采用其地面分辨率为30m的波段的影像,由于分辨率不高,导致校正精度不高.介绍了一种使用ETM 影像的地面分辨率为15m的Pan波段校正,在后续处理中,用其它波段与校正后的Pan波段几何配准,从而达到几何精校正的效果.仿真试验表明,这种先校正分辨率高的影像,再配准分辨率低的影像的方法,在一定程度上提高了校正精度,降低了控制点选取的难度.     

基于改进贝叶斯粗糙集和证据理论的决策信息融合方法

针对决策信息信息表中新对象的分类问题,提出一种基于改进的贝叶斯粗糙集和证据理论的决策信息融合方法.对传统的贝叶斯粗糙集进行改进,扩展到多决策类,定义了支持度的概念以此反映确切分类的对象所占的百分比.利用贝叶斯粗糙集的支持度和置信增益函数作为证据的支持程度,得到各准则下的证据基本概率分配函数,并利用证据合成法则对多个证据进行合成,以此进行决策.将上述方法应用于设备故障的诊断问题中,通过方法的对比验证了该方法实践应用的有效性.     

垃圾邮件过滤的贝叶斯方法综述

目前,基于内容的垃圾邮件过滤问题是Internet安全技术研究的一个重点问题,将机器学习的相关方法应用于垃圾邮件的搜索和判定是进行大量垃圾邮件处理的有效方法。由于贝叶斯分类方法在垃圾邮件处理上表现出了很高的准确度,因此基于贝叶斯分类的垃圾邮件分类方法受到了广泛的关注。主要介绍了贝叶斯方法的理论依据和实现方法,总结了近几年的贝叶斯分类方法的研究情况和贝叶斯方法在垃圾邮件处理中应用的优点和局限性,并提出了下一步可能的研究方向。     

A Bayesian decision theory approach for the techno-economic analysis of an all-optical router (extended version)

Typically, core networks are provided with both optical and electronic physical layers. However, the interaction between the two layers is at present limited, since most of the traditional transport functionalities, such as traffic engineering, switching and restoration, are carried in the IP/MPLS layer. In the light of this, the research community has paid little attention to the potential benefits of the interaction between layers, multilayer capabilities, on attempts to improve quality of service control.This paper shows when to move incoming label switched paths (LSPs) between layers based on a multilayer mechanism that trades off a QoS metric, such as end-to-end delay, and techno-economic aspects. Such a mechanism follows the Bayesian decision theory, and is tested with a set of representative case scenarios.     

元胞贝叶斯决策路径规划方法

提出了基于贝叶斯决策的元胞自动机局部路径规划方法.以元胞蚁群系统能够求取离散化地图的全局最优路径,但是该方法缺乏对局部环境变化的适应性.将元胞蚁群系统得出的状态先验概率数据,以贝叶斯决策理论求取最小条件风险用于最优路径识别,在此基础上运用提出一个新概念--超级风险,可以处理一类环境改变后的局部路径规划.实验结果显示该方法可行且具有一定的智能化.     

Geometric theory for the singular Roesser model

(A,E,B)-invariant and (E,A ,B)-invariant subspaces for the two-dimensional singular Roesser model are investigated. These subspaces are related to the existence of the solutions when the boundary conditions are in these subspaces. Also, the existence of a solution sequence in certain subspaces derived from the invariant subspaces is shown. The boundary conditions that appear in the solution when some semistates in the solution are restricted to zero are also investigated     

一种GPS接收板及其定位精度分析

介绍了世界最大的GPS公司-GARMIN公司的高性能OEM产品35LP的结构，接口，数据格式及其应用；对观测点进行实验观测，描绘定位点图，确定所测点的真实坐标，分析板子的定位误差范围。在校园内选取了多个观测点进行观察测量和精度分析，给出了有代表性的两点实验数据和点图分布，并对二者进行了比较分析，说明了环境视野因素对测量精度的影响。     

贝叶斯决策在传感器识别中的应用

提出了一种用传感器对环境的识别信息进行局部路径规划的智能方法。针对实际交通中存在的最优解失效问题,将蚁群算法求取的信息素数值作为状态先验概率数据,用贝叶斯决策理论计算最小条件风险进行次优路径的选择,并提出超级风险的概念用于实现智能化的决策变更。实验结果证明：该方法能达到预期效果。     

动态决策系统中的几何挖掘算法——概念格

探讨了一种基于概念格的几何数据挖掘算法，根据不动点理论和伽罗瓦连接原理，在数据库中寻找大于一定支持度的闭项目集，分解闭项目集便可得到数据间的关联关系．实验结果表明，此方法适用于决策系统，并且挖掘效率较高。     

Enhancing positioning accuracy of GPS/INS system during GPS outages utilizing artificial neural network

Integrated global positioning system and inertial navigation system (GPS/INS) have been extensively employed for navigation purposes. However, low-grade GPS/INS systems generate erroneous navigation solutions in the absence of GPS signals and drift very fast. We propose in this paper a novel method to integrate a low-grade GPS/INS with an artificial neural network (ANN) structure. Our method is based on updating the INS in a Kalman filter structure using ANN during GPS outages. This study focuses on the design, implementation and integration of such an ANN employing an optimum multilayer perceptron (MLP) structure with relevant number of layers/perceptrons and an appropriate learning. As a result, a land test is conducted with the proposed ANN + GPS/INS system and we here provide the system performance with the land trials.     

GPS观测数据提高InSAR干涉测量精度的分析

利用地面GPS连续观测资料进行分析不同时间地表形变规律和修正中性大气对InSAR干涉测量相位延迟。