A Map-Matching Algorithm forGPS/DR Integrated Navigation Systems Basedon Dempster-Shafer Evidence Reasoning

GPS (Global Positioning System) has been widely used in car navigation systems. Most car navigation systems estimate the car position from GPS and DR (dead reckoning). However, the unknown GPS noise characteristic and the unbounded DR accumulation of errors over time make the position information with undesirable position errors. The map matching can improve the position accuracy and availability of the vehicular position system. In this paper, general principle of map matching is investigated according to segmentation and feature extraction, and a map matching algorithm based on D-S (Dempster-Shafer) evidence reasoning for GPS integrated navigation system is proposed, which can find the exact road on which a car moves. For the experiments, a car navigation system is developed with some sensors and the field test demonstrates the effectiveness and applicability of the algorithm for the car location and navigation.

Land Cover Classification with Multi-source Data Using Evidential Reasoning Approach

Land cover classification is the core of converting satellite imagery to available geographic data.However,spectral signatures do not always provide enough information in classification decisions.Thus,the application of multi-source data becomes necessary.This paper presents an evidential reasoning (ER) approach to incorporate Landsat TM imagery,altitude and slope data.Results show that multi-source data contribute to the classification accuracy achieved by the ER method,whereas play a negative role to that derived by maximum likelihood classifier (MLC).In comparison to the results derived based on TM imagery alone,the overall accuracy rate of the ER method increases by 7.66% and that of the MLC method decreases by 8.35% when all data sources (TM plus altitude and slope) are accessible.The ER method is regarded as a better approach for multi-source image classification.In addition,the method produces not only an accurate classification result,but also the uncertainty which presents the inherent difficulty in classification decisions.The uncertainty associated to the ER classification image is evaluated and proved to be useful for improved classification accuracy.

Risk assessment of water security in Haihe River Basin during drought periods based on D-S evidence theory

The weights of the drought risk index （DRI）, which linearly combines the reliability, resiliency, and vulnerability, are difficult to obtain due to complexities in water security during drought periods. Therefore, drought entropy was used to determine the weights of the three critical indices. Conventional simulation results regarding the risk load of water security during drought periods were often regarded as precise. However, neither the simulation process nor the DRI gives any consideration to uncertainties in drought events. Therefore, the Dempster-Shafer （D-S） evidence theory and the evidential reasoning algorithm were introduced, and the DRI values were calculated with consideration of uncertainties of the three indices. The drought entropy and evidential reasoning algorithm were used in a case study of the Haihe River Basin to assess water security risks during drought periods. The results of the new DRI values in two scenarios were compared and analyzed. It is shown that the values of the DRI in the D-S evidence algorithm increase slightly from the original results of Zhang et al. （2005）, and the results of risk assessment of water security during drought periods are reasonable according to the situation in the study area. This study can serve as a reference for further practical application and planning in the Haihe River Basin, and other relevant or similar studies.

Damage effectiveness assessment method for anti-ship missiles based on double hierarchy linguistic term sets and evidence theory

The research on the damage effectiveness assessment of anti-ship missiles involves system science and weapon science, and has essential strategic research significance. With comprehensive analysis of the specific process of the damage assessment process of anti-missile against ships, a synthetic damage effectiveness assessment process is proposed based on the double hierarchy linguistic term set and the evidence theory. In order to improve the accuracy of the expert ’s assessment information, double hierarchy linguistic terms are used to describe the assessment opinions of experts. In order to avoid the loss of experts ’ original information caused by information fusion rules, the evidence theory is used to fuse the assessment information of various experts on each case. Good stability of the assessment process can be reflected through sensitivity analysis, and the fluctuation of a certain parameter does not have an excessive influence on the assessment results. The assessment process is accurate enough to be reflected through comparative analysis and it has a good advantage in damage effectiveness assessment.

A variable weight approach for evidential reasoning

A variable weight approach was proposed to handle the probability deficiency problem in the evidential reasoning （ER） approach. The probability deficiency problem indicated that the inadequate information in the assessment result should be less than that in the input. However, it was proved that under certain circumstances, the ER approach could not solve the probability deficiency problem. The variable weight approach was based on two assumptions： 1） the greater weight should be given to the rule with more adequate information; 2） the greater weight should be given to the rules with less disparate information. Assessment results of two notional case studies show that 1） the probability deficiency problem is solved using the proposed variable weight approach, and 2） the information with less inadequacy and more disparity is provided for the decision makers to help reach a consensus.

Integration-centric approach to system readiness assessment based on evidential reasoning

An integration-centric approach is proposed to handle inadequate information in the system readiness level （SRL） assessment using the evidential reasoning （ER） algorithm. Current SRL assessment approaches cannot be applied to handle inadequate information as the input. The ER-based approach is proposed to synthesize inadequate input information and an integration-centric perspective is applied to reduce the computational complexity. Two case studies are performed to validate the efficiency of the proposed approach. And these studies are also performed to study how the inadequate information will affect the assessment result. And the differences caused by the system＇s structure. The importance of the system＇s structure in the SRL assessment is demonstrated and the contributions made in this study are summarized as conclusions.

Data-Driven Model for Risk Assessment of Cable Fire in Utility Tunnels Using Evidential Reasoning Approach

Cable fire is one of the most important events for operation and maintenance(O&M)safety in underground utility tunnels(UUTs).Since there are limited studies about cable fire risk assessment,a comprehensive assessment model is proposed to evaluate the cable fire risk in different UUT sections and improve O&M efficiency.Considering the uncertainties in the risk assessment,an evidential reasoning(ER)approach is used to combine quantitative sensor data and qualitative expert judgments.Meanwhile,a data transformation technique is contributed to transform continuous data into a five-grade distributed assessment.Then,a case study demonstrates how the model and the ER approach are established.The results show that in Shenzhen,China,the cable fire risk in District 8,B Road is the lowest,while more resources should be paid in District 3,C Road and District 25,C Road,which are selected as comparative roads.Based on the model,a data-driven O&M process is proposed to improve the O&M effectiveness,compared with traditional methods.This study contributes an effective ER-based cable fire evaluation model to improve the O&M efficiency of cable fire in UUTs.

Academic Freedom,Feminism and the Probabilistic Conception of Evidence

There is a current debate about the extent to which Academic Freedom should be permitted in our universities.On the one hand,we have traditionalists who maintain that Academic Freedom should be unrestricted:people who have the appropriate qualifications and accomplishments should be allowed to develop theories about how the world is,or ought to be,as they see fit.On the other hand,we have post-traditional philosophers who argue against this degree of Academic Freedom.I consider a conservative version of post-traditional philosophy that permits restrictions on Academic Freedom only if the following conditions are met,Condition 1:The dissemination of the results of a given research project R must cause significant harm to some people,especially to people from oppressed groups.Condition 2:Condition 1 must possess strong empirical support,and which accepts the following assumptions:(1)there is a world of objective facts that is,in principle,discoverable,(2)rational means are the means of discovering it and,(3)rational means requires strong empirical support.I define strong empirical support for an hypothesis h on evidence e in probabilistic terms,as a ratio of posterior to prior probabilities substantially exceeding 1.I now argue in favour of a research policy that accepts unrestricted Academic Freedom.My argument is that there is a formal and general quandary that arises within the standard theory of probability when we apply this account of empirical support to a set of possible causal hypotheses framed in such a way that the“reverse probabilities”,pr(e/h)are 1.I consider various possible ways to escape this quandary,none of which are without difficulties,concluding that a research policy allowing for unrestricted Academic Freedom is probably the best that we can hope for.

Build Evidence Chain Relational Model Based on Chinese Judgment Document

The reasoning of judgment documents is the touchstone of justice. Attaching importance to the reasoning of judgment documents is essentially the embodiment of judiciary civilization. In order to promote the reform of judgment documents reasoning and improve the level of it, the technology of automated judgment documents reasoning evaluation has to be studied on. How to build evidence chain relational model is the basis and key to this technology.An approach is proposed to build evidence chain relational model based on Chinese judgment documents. Using automated text preprocessing for Chinese judgment documents creates semi-structured XML documents and extracts evidence set and fact set. The method of key elements extraction is used to obtain the keywords of evidence and facts. Calculating the degree of association can work out the connection points of evidence chain relational model. Tabular display and graphical display of evidence chain relational model can be realized.

Dempster-Shafer证据推理在数据融合中的应用

介绍了Dempster-Shafer证据推理的基本概念和理论,并将数据融合思想引入到机械设备故障诊断中,采用Dempster-Shafer证据推理进行融合计算,验证了这一方法的有效性.

基于Dempster-Shafer证据推理的多传感器信息融合技术及应用

本文详细阐明了基于 D S证据推理的多传感器信息融合的原理及目标识别的方法。同时 。

基于改进ER的生鲜冷链物流服务质量评估方法

针对生鲜冷链物流服务质量评价信息高度冲突问题,采用改进的证据推理(evidence reasoning,ER)方法处理指标评估信息。首先,考虑各专家对生鲜冷链物流服务供应商的指标评估具有不确定性特点,提出采用ER方法处理每个供应商的指标评估信息;其次,考虑各专家对每个供应商的评估信息具有高度冲突问题,采用cosine相似函数衡量冲突变化程度,利用证据间的一致性计算证据权重,并对评估信息进行修正;然后,再运用证据推理方法集成修正后的供应商评估信息;最后以某企业选择生鲜冷链物流服务供应商为例进行分析,并将分析结果与对比方法计算的结果相对比。结果表明,所提方法能有效解决高度冲突问题,并能降低因冲突引起的不确定性。

基于ER算法的潜艇鱼雷武器系统损伤等级评估

损伤等级评估研究对于潜艇维修计划的制定,抢修任务的分配具有十分重要的意义。本文根据武器系统指标体系和损伤评估影响因素的特点,提出了基于多属性决策ER算法的损伤等级评估模型。该模型不仅能够组合多个损伤因子的评估问题,而且能够处理由不确定信息产生的不确定性,并给出未知信息的信任度,是潜艇战场损伤等级评估的有效工具。评估结果表明,该模型能够综合考虑评估过程中的不确定性信息,对于制定科学合理的武器系统损伤处理决断具有重要意义。

高铀U-ZrH_x-Er细棒燃料氢化开裂原因分析

采用金相显微镜(OM)和带能谱的扫描电子显微镜(SEM-EDS)分别对用粗铀和精铀熔炼得到的铀锆铒(U-Zr-Er)合金样品及其氢化样品的微观组织、氢化样品的成分进行分析。结果表明,含精铀的氢化样品中杂质元素Fe和C含量比含粗铀的氢化样品低,且成分均匀性较好;含粗铀的氢化样品中U、C和少量Fe元素构成的析出物膨胀系数与锆基体差异较大,当锆基体随着吸氢量的增加而发生体积膨胀时,U、C和少量Fe元素构成的大块析出物作为第二相钉扎在基体内,引起应力集中而造成样品开裂。含精铀的氢化样品中U和以碳化锆形式存在的C元素在氢化锆基体中分布比较均匀,不存在应力过于集中的状况,氢化样品不会产生开裂。

融合概率矩阵分解与ER规则的群组推荐方法

群组推荐需要同时考虑一组内所有成员的偏好,通过融合成员偏好进而向群组推荐项目。现有的对于组推荐方法的研究中大多都将相同的权重分配给群组中所有用户,而未考虑在现实生活中不同组成员的重要性和可靠性应不同。针对该问题,提出一种新的融合概率矩阵分解与证据推理(evidence reasoning,ER)规则的群组推荐方法(FPMF-ER),以改进群组推荐中个体预测和偏好融合的过程。联合用户关系信息对经典概率矩阵分解加以改进,以获取更为完整、精准的个人预测评分;在组成员偏好融合的过程中引入ER规则,根据组成员的权重和可靠性识别群组成员的影响力,使偏好融合更为合理、准确。为了验证该方法的有效性,在Book-Crossing数据集上进行了对比实验,实验结果表明,相较于最优的基准模型,FPMF-ER的推荐结果准确性和用户满意度分别至少提高了2.55%和2.06%。

基于IFS和ER的社保基金投资管理人选择研究

社保基金是经济社会运行的"稳定器"和"安全网",是社会保障事业健康发展的物质基础。在我国社保基金投资运作的委托投资模式下,投资管理人的选择是社保基金保值增值的关键。首先,构建了社保基金投资管理人选择的指标体系,然后,将直觉模糊集与证据推理(ER)方法相结合,建立了社保基金投资管理人选择的模型并进行了实证研究。实证结果表明,本文构建的指标体系以及提出的选择模型是合理的、有效的。

IIF-ER方法在应急物流设施选址中的应用

为解决不确定性条件下的应急物流设施选址评估的问题,建立基于区间直觉模糊(IIF)与证据推理(ER)的设施选址模型。首先,针对指标评估的不确定性问题,采用区间直觉模糊数(IIFV)表达专家评价信息;然后,考虑到传统IIF在信息集成中存在信息丢失的问题,采用ER集成方法集成评估信息;其次,利用逼近理性解排序法(TOPSIS)对应急物流设施选址方案进行排序;最后,以某城市建立应急物流设施点为例,采用传统IIF信息集成方法与所提方法对比分析,验证模型的有效性。结果表明:所提方法能解决不确定性条件下的应急物流设施选址决策问题,同时也能保证决策质量。

RIMER中学生学习专家系统的开发与设计

RIMER中学生学习专家系统是采用证据推理方法的新型学习型专家系统,实现了中学生有效的自我学习与评估,促进了中学生学习效率与质量的提高。在介绍RIMER中学生学习专家系统的总体结构基础上,阐述了RIM-ER中学生学习专家系统中的输入输出、正向推理、学习训练等各个模块的详细设计,并用共轭梯度算法实现了对置信库规则的训练学习。实验结果证明该算法高效实用,克服了Yang等给出的置信规则库在速度、简易性、收敛性等方面的诸多局限性。

不确定多属性决策中的改进ER算法

针对基于证据理论的不确定多属性决策模型,通过评价框架的推广,提出了一种改进算法,并证明该算法完全满足证据合成的四个定理,通过实例运算,进一步验证了新方法的有效性和合理性。

基于改进FMECA-FBN和ER的铝矾土海运物流风险评估

针对传统失效模式影响与危害性分析(FMECA)在风险评估中存在的诸多局限性,提出一种基于改进FMECA、模糊贝叶斯网络(FBN)和证据推理(ER)相结合的铝矾土海运物流风险评估模型。采用新的风险表征参数,通过将模糊集理论与专家知识相结合创建基于置信结构的模糊规则库系统,并利用贝叶斯网络推理技术实现对铝钒土海运物流失效模式危害度的重要性排序;而基于权值分配和矩阵分析的改进ER可有效融合失效模式评估结果,实现从整体角度对铝钒土海运物流系统进行风险评估;最后通过对我国进口铝矾土海运物流进行风险评估来验证模型的有效性与可行性。结果表明:我国进口铝矾土海运物流环节中最重要的风险因素是“海盗或恐怖主义袭击”和“工人暴动或罢工”,系统整体处于中高风险水平,该评估结果与实际情况相符,从而验证了模型的有效性和可靠性。该研究结果可为工程领域建立与完善系统性的风险评估模型与框架提供依据。