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.

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.

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.

On the evidential reasoning rule for dependent evidence combination

Evidential Reasoning(ER)rule,which can combine multiple pieces of independent evidence conjunctively,is widely applied in multiple attribute decision analysis.However,the assumption of independence among evidence is often not satisfied,resulting in ER rule inapplicable.In this paper,an Evidential Reasoning rule for Dependent Evidence combination(ERr-DE)is developed.Firstly,the aggregation sequence of multiple pieces of evidence is determined according to evidence reliability.On this basis,a calculation method of evidence Relative Total Dependence Coefficient(RTDC)is proposed using the distance correlation method.Secondly,as a discounting factor,RTDC is introduced into the ER rule framework,and the ERr-DE model is formulated.The aggregation process of two pieces of dependent evidence by ERr-DE is investigated,which is then generalized to aggregate multiple pieces of non-independent evidence.Thirdly,sensitivity analysis is carried out to investigate the relationship between the model output and the RTDC.The properties of sensitivity coefficient are explored and mathematically proofed.The conjunctive probabilistic reasoning process of ERr-DE and the properties of sensitivity coefficient are verified by two numerical examples respectively.Finally,the practical application of the ERr-DE is validated by a case study on the performance assessment of satellite turntable system.

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%。

Operational effectiveness evaluation based on the reduced conjunctive belief rule base

To address the issue of rule premise combination explosion in the construction of the traditional complete conjunctive belief rule base(BRB),this paper introduces an orthogonal design method to reduce the conjunctive BRB.The reasoning method based on reduced conjunctive BRB is designed with the help of the conversion technology from conjunctive BRB to disjunctive BRB.Finally,the operational mission effectiveness evaluation is taken as an example to verify the proposed method.The results show that the method proposed in this paper is feasible and effective.

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.

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。

专利创造性判断中公知常识认定问题研究

公知常识是专利创造性判断中常见理由,但既有的法律法规对其的认定规则并不完善,专利审查和司法裁判中对通过举证证明和说理证明来认定公知常识的顺序和界限并没有明确的规定。因此,在坚持专利创造性判断中,保护具有创造性劳动的前提下,应当明确举证证明和说理证明的先后顺序,并有必要通过引入“众所周知的事实”来对需要举证证明和说理证明的公知常识进行划分。在此基准下,尝试扩大公知常识的载体范围以适应日新月异的技术发展,并使得公知常识的认定趋于客观化和公开化。

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。

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。

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。

A Multi-dimensional Trust-aware Cloud Service Selection Mechanism Based on Evidential Reasoning Approach

In the last few years, cloud computing as a new computing paradigm has gone through significant development, but it is also facing many problems. One of them is the cloud service selection problem. As increasingly boosting cloud services are offered through the internet and some of them may be not reliable or even malicious, how to select trustworthy cloud services for cloud users is a big challenge. In this paper, we propose a multi-dimensional trust-aware cloud service selection mechanism based on evidential reasoning(ER) approach that integrates both perception-based trust value and reputation based trust value, which are derived from direct and indirect trust evidence respectively, to identify trustworthy services. Here, multi-dimensional trust evidence, which reflects the trustworthiness of cloud services from different aspects, is elicited in the form of historical users feedback ratings. Then, the ER approach is applied to aggregate the multi-dimensional trust ratings to obtain the real-time trust value and select the most trustworthy cloud service of certain type for the active users. Finally, the fresh feedback from the active users will update the trust evidence for other service users in the future.

RIMER的作战行动方案评估方法研究

作战行动方案评估中存在指标种类多,存在不能回溯评估结果的情况。从作战行动所需能力出发,通过"作战任务"到"基本作战行动"再到"能力指标"的转换映射,建立相应的指标体系。然后建立置信规则库,将指标数据多样形式输入转换为一致的信度形式。运用证据推理对激活的规则进行组合,获得作战行动方案评估的结果。

一种新的基于多样性赋权证据推理的集成学习方法

在集成学习中使用平均法、投票法作为结合策略无法充分利用基分类器的有效信息,且根据波动性设置基分类器的权重不精确、不恰当。以上问题会降低集成学习的效果,为了进一步提高集成学习的性能,提出将证据推理(evidence reasoning, ER)规则作为结合策略,并使用多样性赋权法设置基分类器的权重。首先,由多个深度学习模型作为基分类器、ER规则作为结合策略,构建集成学习的基本结构;然后,通过多样性度量方法计算每个基分类器相对于其他基分类器的差异性;最后,将差异性归一化实现基分类器的权重设置。通过多个图像数据集的分类实验,结果表明提出的方法较实验选取的其他方法准确率更高且更稳定,证明了该方法可以充分利用基分类器的有效信息,且多样性赋权法更精确。

基于改进模糊置信规则的意图识别方法

针对传统意图识别方法只能处理某种类型不确定性信息的不足,该文结合模糊集和DS证据理论优势提出一种模糊置信规则库(BRB)信息处理方法。首先在置信规则前提部分改进了前提属性的连接关系,根据数据集统计分布特点设计了模糊集分割,选取Cauchy型分布作为隶属度函数,较好地避免置信规则无法被有效激活进而导致系统无有效输出问题;其次融合处理辨识框架内不同类别的置信分布,建立规则权重和特征权重优化模型,构建了特征空间与类别空间之间的输入输出关系;在此基础上,计算未知意图数据在相应规则模糊域的匹配度和激活度,采用置信度最大原则进行识别决策。通过实验验证、参数敏感性及结果分析、时间复杂度分析,表明该文方法可以获得比其他识别方法更高的正确率,尤其是在小样本条件下更能体现出该方法的有效性和可靠性。

基于证据推理规则的大学生综合素质评估

大学生综合素质评估有助于发现综合素质培养过程中的不足,为大学生综合素质培养方案的完善提供客观的依据。大学生综合素质评估指标中包括学科成绩等定量信息和道德素养等定性信息,需要评估方法具有处理多元信息能力和信息融合能力。现有的评估方法中,存在数据类型和处理方法单一、不精确和不完整的信息得不到有效表达、无法描述数据可靠性等问题。提出基于证据推理规则的大学生综合素质评估模型,对多元数据进行标准化处理,使用层次分析法和数理统计方法求解证据权重及可靠度,将标准化数据通过证据推理规则算法进行信息融合,对大学生的综合素质进行评估。最后通过案例分析验证方法的有效性。

基于证据推理规则的信息融合故障诊断方法

本文针对不确定性故障特征信息的融合决策问题,给出基于证据推理(evidence reasoning,ER)规则的故障诊断方法.首先基于故障特征样本似然函数归一化的方法求取各传感器(信息源)提供的诊断证据;从传感器误差以及故障特征对各故障类型辨别能力的差异出发,给出获取诊断证据可靠性因子的方法;给出双目标优化模型训练得到诊断证据的重要性权重,最后利用ER规则融合经可靠性因子和重要性权重修正后的诊断证据,利用融合结果进行故障决策.该方法继承了Dempster-Shafer证据理论处理不确定性信息融合问题的优点,同时克服了它在实际应用中无法区分证据可靠性和重要性的不足,使得所获诊断证据更为客观、可信.最后,通过在多功能电机转子试验台上的故障诊断实验,验证了所提方法的有效性.

一种新的证据推理组合规则

证据推理是一种不确定推理的人工智能方法。首先研究几种组合规则的改进方法和造成其缺陷的机理 ,然后从分析冲突的来源入手 ,利用冲突信息提出一种新的组合规则——吸收法。仿真结果表明 ,新的组合规则在处理冲突方面的性能得到了明显改进 ,并且具有较强的鲁棒性。

基于置信规则库推理的轨道高低不平顺检测方法

针对机车振动特征与轨道高低不平顺状态之间存在的复杂非线性关系,提出利用置信规则库推理方法检测轨道高低不平顺故障。建立置信规则库(BRB),通过车厢和车轴测点的垂向振动特征确定不平顺状态。选取两测点的振动幅值作为输入,不平顺的安全等级作为输出,对于被输入激活的置信规则,通过证据推理(ER)算法将被激活规则后项中的置信结构进行融合,从融合结果中换算出不平顺的安全等级。为解决专家给定的初始置信规则库参数不精确问题,采用数值样本优化学习模型训练得到最优参数取值。利用国内某既有干线区段轨道上所获取的实测振动数据,对所提方法进行验证,表明训练后得到的置信规则库系统能够准确描述不平顺与振动特征之间的因果关系,从而给出精确的检测结果。