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.

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%。

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.

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.

A Processor Performance Prediction Method Based on Interpretable Hierarchical Belief Rule Base and Sensitivity Analysis

The prediction of processor performance has important referencesignificance for future processors. Both the accuracy and rationality of theprediction results are required. The hierarchical belief rule base (HBRB)can initially provide a solution to low prediction accuracy. However, theinterpretability of the model and the traceability of the results still warrantfurther investigation. Therefore, a processor performance prediction methodbased on interpretable hierarchical belief rule base (HBRB-I) and globalsensitivity analysis (GSA) is proposed. The method can yield more reliableprediction results. Evidence reasoning (ER) is firstly used to evaluate thehistorical data of the processor, followed by a performance prediction modelwith interpretability constraints that is constructed based on HBRB-I. Then,the whale optimization algorithm (WOA) is used to optimize the parameters.Furthermore, to test the interpretability of the performance predictionprocess, GSA is used to analyze the relationship between the input and thepredicted output indicators. Finally, based on the UCI database processordataset, the effectiveness and superiority of the method are verified. Accordingto our experiments, our prediction method generates more reliable andaccurate estimations than traditional models.

融合ER和分层BRB的CPU性能分析模型

为解决中央处理器(Central Processing Unit, CPU)性能分析所面临的分析指标复杂、分析过程不具有可解释性、分析结果不可追溯的问题,提出了一种融合ER(Evidence Reasoning)和分层BRB(Belief Rule Base)的CPU性能分析模型.首先,利用ER算法从不同层面对处理器影响因素进行指标评估,其次,通过分层BRB实现对CPU性能的综合分析,最后,采用鲸鱼优化算法(Whale Optimization Algorithm, WOA)对模型参数优化.通过UCI数据库(University of California Irvine, UCI)计算机硬件数据集验证了模型的有效性.整个分析模型建立在ER算法上,保证了模型推理的可解释性,而分层BRB方法解决了传统BRB的组合规则爆炸问题,同时结合优化算法有效的提高模型的准确度.

A New Prediction System Based on Self-Growth Belief Rule Base with Interpretability Constraints

Prediction systems are an important aspect of intelligent decisions.In engineering practice,the complex system structure and the external environment cause many uncertain factors in the model,which influence the modeling accuracy of the model.The belief rule base(BRB)can implement nonlinear modeling and express a variety of uncertain information,including fuzziness,ignorance,randomness,etc.However,the BRB system also has two main problems:Firstly,modeling methods based on expert knowledge make it difficult to guarantee the model’s accuracy.Secondly,interpretability is not considered in the optimization process of current research,resulting in the destruction of the interpretability of BRB.To balance the accuracy and interpretability of the model,a self-growth belief rule basewith interpretability constraints(SBRB-I)is proposed.The reasoning process of the SBRB-I model is based on the evidence reasoning(ER)approach.Moreover,the self-growth learning strategy ensures effective cooperation between the datadriven model and the expert system.A case study showed that the accuracy and interpretability of the model could be guaranteed.The SBRB-I model has good application prospects in prediction systems.

Milling Fault Detection Method Based on Fault Tree Analysis and Hierarchical Belief Rule Base

Expert knowledge is the key to modeling milling fault detection systems based on the belief rule base.The construction of an initial expert knowledge base seriously affects the accuracy and interpretability of the milling fault detection model.However,due to the complexity of the milling system structure and the uncertainty of the milling failure index,it is often impossible to construct model expert knowledge effectively.Therefore,a milling system fault detection method based on fault tree analysis and hierarchical BRB(FTBRB)is proposed.Firstly,the proposed method uses a fault tree and hierarchical BRB modeling.Through fault tree analysis(FTA),the logical correspondence between FTA and BRB is sorted out.This can effectively embed the FTA mechanism into the BRB expert knowledge base.The hierarchical BRB model is used to solve the problem of excessive indexes and avoid combinatorial explosion.Secondly,evidence reasoning(ER)is used to ensure the transparency of the model reasoning process.Thirdly,the projection covariance matrix adaptation evolutionary strategies(P-CMA-ES)is used to optimize the model.Finally,this paper verifies the validity model and the method’s feasibility techniques for milling data sets.

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.

事后道歉证据的证明逻辑——以长崎事件为例

被告人“道歉”,或者出于认罪被归为自白,或者出于礼貌、同情等其他理由被归为辩解。这两种解释相互矛盾,但都是用于直接证明案件主要事实的实质证据。道歉、提出和解等言语和行为,可以补强这种真实性可疑的作为主要证据的受害人控告犯罪之陈述,并进而间接推论案件事实,但是这种补强证据本身有时需要进一步的补强或者允许反证,其在程序法上可以达到转移举证责任的效果。从经验科学而言,以这类道歉、提议和解、采取事后补救措施等证据对案件事实进行的推论,是以或然性概括为大前提的设证推理,其结论是似真的,具有可辩驳性。从价值科学而言,这类证据的使用还需要法官在坚守无罪推定原则、排除合理怀疑证明标准的基础上,切实保持个人自由和社会防卫之间审慎平衡。

一种基于证据多视角的模糊C-means聚类算法

针对传统多视角聚类算法难以准确识别噪声和有效划分类间重叠区域样本的问题,提出一种基于证据多视角的模糊C均值(evidential multi-view fuzzy C-means,EMVFCM)聚类算法。首先,在证据推理框架下,研究一种改进的模糊C-means多视角聚类算法,通过优化改进的目标函数获得待测样本属于单类和噪声的信任值,从而识别出噪声数据。然后,由于重叠区域的样本不能被准确地划分类别,所以将其划分到相对应的复合类,这不仅能够表征数据样本类别的不精确性,还能降低错误分类的风险。最后,通过人工数据集和UCI数据集验证本文算法的性能并与相关算法对比。实验结果表明,本文算法较传统多视角聚类算法能更有效地处理数据中的噪声和重叠样本难以准确划分的问题。

法典化视角下刑事证明标准条款的完善

偏重哲学话语的理论争议无益于解决刑事证明标准的适用难题。在法典化改革的进程中,有必要厘清刑事证明标准条款自身的逻辑性和层次性,进而对现有法律文本的表述进行适当的调整和修改,明确证据收集的程序合法标准,明确证据查证属实的法定义务,确立定罪事实排除合理怀疑的程序规则,以实现发现案件真相和保障法官自由裁量权行使之间的价值平衡,最终实现刑事证明标准在程序规范下的合理适用。

飞行员应急处置能力评价模型

为提高飞行员在紧急情境下的应急处置能力,减少民航安全事故,基于决策模型和应激理论模型,分析飞行员任务过程,从飞行员运行安全能力和飞行员储备安全能力2个方面建立飞行员应急处置能力指标体系;运用模糊层次分析法(FAHP)建立包含安全运行能力B1和储备安全能力B2指标体系,结合专家意见确定二级指标的隶属度,得到飞行员应急处置能力的核心指标;通过证据推理(ER)算法合成民航安全领域相关专家综合评价飞行员应急处置能力的流程,并选取某航空公司2个机组飞行员进行实证分析。研究结果表明:评价飞行员应急处置能力模型很好地降低不确定性对评价结果的影响,从而显著提高评价结果的可靠性。

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.

论实践理性自然法之不证自明性

面对近代事实与价值二分法对自然法理论的冲击,当代自然法学家约翰·菲尼斯试图以自然法不证自明性跨越自然法认识论困境。然而,菲尼斯所提出的自然法不证自明性概念既继承了中世纪典范自然法学家托马斯·阿奎那的自明性传统,又包含了现代希尔伯特几何公理的自明性概念,二者在形而上学实在论立场上相互对立。这使菲尼斯的自然法不证自明性概念面临着本体论实在论与非实在论之间的张力,其对事实与价值二分法的回应以及自然法伦理学的构建也因此受到质疑。一种对自然法不证自明性的自洽理解可以从理论理性判断与实践理性判断的同一性之中得出,自然法原则作为理论理性判断是可推导的,但作为实践理性判断是无源出且不证自明的,这种理解也为弥合事实与价值之间的鸿沟提供了一种可能。

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 。

数字时代大数据辅助司法证明的构造及其风险防控

“大数据辅助证明”有助于提升事实认定的科学性与准确性。大数据辅助证明以整体主义为指引,在证据推理环节通过大数据技术高效组织数据,整合经验概括对个案中的推论命题和要件事实等的确定形成类型化指引;在证据评价环节可以辅助证据标准的数据化校验与证明力概率评价的科学化。如果对大数据智能产生非理性崇拜,那么其有可能异化为新的神明裁判方式,侵蚀理性主义传统,导致认知偏差难以得到控制,证明责任的界限模糊化,以及用“客观规律”代替“认识论概率”等问题,而且会增加事实认定在各方面的附随风险。为应对挑战,司法证明的重心应当从“信息规制”迈向“风险防控”,并坚守数据技术的辅助性,诉讼主体的认知交互性和证明的外部可检验性原则。当大数据辅助证明诱发的风险无法通过隔离、警示和对抗等手段预防时,应当合理分配证明过程中的风险。

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。

基于证据图推理的文档级实体关系抽取

[研究目的]为缓解文档级实体关系抽取任务中存在的句子噪声问题,提高文档级实体关系抽取性能,提出一种基于证据图推理的文档级实体关系抽取方法,为文档级实体关系抽取和知识发现研究提供参考。[研究方法]通过启发式规则捕获实体对间关系推理所需证据句路径信息;引入图结构学习思想将证据句路径信息融入异构文档图;基于关系图卷积网络进行关系推理以提升文档图对证据句信息的聚合能力;采用前馈神经网络对实体关系进行预测,实现文档级实体关系高效抽取。[研究结论]所提出的模型在国际公开文档级评测数据集CDR和GDA上F1值分别达到71.3%和85.4%,较基准模型EIDER提高1.2%与1.1%。实验结果表明该方法能够有效选择实体关系推理所需证据路径,提升文档级实体关系抽取性能。