Estimation,modeling, and aggregation of missing survey data for prioritizing customer voices

It is widely acknowledged that understanding and prioritizing the voice of customer is a critical step in new product development. In this work, we propose a novel approach to handle missing and incomplete data while combining information from different surveys for prioritizing customer voices. Our new approach comprises of the following stages: estimating and representing missing and incomplete data; estimating intervals for the criteria used in analyzing data; mapping data on criteria to a common scale; modeling interval data using interval belief structure; and aggregating evidence and ranking customer voices using the interval evidential reasoning algorithm. We demonstrate our approach using a case study from automotive domain with a given criteria hierarchy for analyzing data from three different surveys. We propose new optimization formulations for estimating intervals of the criteria used in our case study and logical yet pragmatic transformation functions for mapping criteria values to a common scale.     

应急不完全信息环境下的混合多属性协同决策方法

科学的应急救援协同决策理论方法,不但能使应急管理系统更好地发挥作用,而且能使政府及公众的应急救援行为更加规范和有序.为此,针对应急环境下决策信息不完全的背景,研究构建了一类综合集成网络层次分析法(ANP)、证据理论(D-SEvidence Theory)以及改进的理想点法(TOPSIS)的混合多属性应急协同决策方法.其中ANP用于处理应急救援方案非独立和相互联系的评价指标权重的确立,D-S Theory用于处理不完全信息条件下多个部门对应急救援候选方案的不同评价信息融合,改进的理想点法(TOPSIS)则用于最终候选应急救援方案的排序.研究结果表明,所提出的混合多属性协同决策方法不仅在理论上有所集成创新,而且在实际应用中可以有效解决应急环境下多部门或多环节协同决策问题.     

Combination of partially non-distinct beliefs: The cautious-adaptive rule

The combination rule is critical in an evidence based fusion process. The conjunctive rule is most common eventhough when the cognitive independence – distinctness – assumption is often questionable. A new combination rule is tested here in both discrete and continuous cases, accounting for a partial non-distinctness between evidences. It is based on ‘generalized discounting’, that we define for separable basic belief assignments (bbas) or basic belief densities (bbds), to be applied to the source correlation derived from the cautious rule. This correlation can be specified in both considered cases of consonant bbas/bbds (as proposed by Dubois et al.) and separable bbas/bbds (as proposed by Denœux). Then, the so-called ‘cautious-adaptive’ rule varies between the conjunctive rule and the cautious one, depending on the discounting level. In the Gaussian case with standard deviation σ, the evidence non-distinctness will be parameterized by a factor [0,1] dividing σ. It leads to the generalized discounting needed in the cautious-adaptive formulation.     

Prospective elementary teachers use of representation to reason algebraically

We used a teaching experiment to evaluate the preparation of preservice teachers to teach early algebra concepts in the elementary school with the goal of improving their ability to generalize and justify algebraic rules when using pattern-finding tasks. Nearly all of the elementary preservice teachers generalized explicit rules using symbolic notation but had trouble with justifications early in the experiment. The use of isomorphic tasks promoted their ability to justify their generalizations and to understand the relationship of the coefficient and y-intercept to the models constructed with pattern blocks. Based on critical events in the teaching experiment, we developed a scale to map changes in preservice teachers’ understanding. Features of the tasks emerged that contributed to this understanding.     

数据驱动视角下飞机故障预测与健康管理系统设计及验证

由于故障预测与健康管理系统（PHM）是涉及多层次、多领域、耦合度高的复杂系统,很难从整体对PHM系统进行设计和验证,所以始终没有成熟的工具和方法。从数据驱动的视角,研究了飞机PHM系统设计及验证。以数据为主线,采用了四种方法实现PHM系统设计及验证：1）采用系统建模语言（SysML）进行PHM系统的架构设计;2）采用基于事件的方法将被观测系统的行为状态化,实现PHM系统中资源的充分利用,降低局部的资源压力;3）采用基于模型的推理方法,实现了系统状态模型的应用;4）采用多线程、异构能量耦合等方法定义了一个PHM仿真框架,并在该框架指导下开发了一个仿真环境,并针对机载配电系统设置了几种测试用例进行诊断推理,对提到的各种方法进行了验证。研究结果表明,上述方法是合理有效的,可以构建描述完整、资源均衡的PHM系统架构。     

基于Zadeh推理的故障空调噪音识别

通过声音来实现故障诊断在工业领域应用比较广泛。本文的主要目标是给出故障空调在运行状态下的声音,使用Zadeh推理的方法对故障空调的声音进行识别,进而推断出空调内部具体哪个部件或零件发生故障。本文首先对声音进行快速傅里叶变换,根据三分之一倍频程对整个频率进行划分,并对生成的频率进行标准化处理;当空调内部的零部件发生损坏时,噪音峰值就会在某一段或几段频率中有升高,从而提取每个频程的峰值作为特征向量。通过“专家经验”得出一个逻辑关系R矩阵,使用matlab软件将特征向量与R矩阵进行点积运算得到故障诊断的结果,最后GUI界面呈现出结果。实验表明,本方法对空调噪音的识别诊断效果比较理想。最后给出本方法适用的一些条件。本方法的实现原理简单,在空调噪音故障诊断应用领域可以有一定的应用空间。     

基于模糊证据推理的汽车制造产业网络环境复杂性测度

环境复杂性是汽车制造产业网络复杂性的主要诱因。利用指标波动程度测度复杂性的思想,建立表征汽车制造产业网络外部环境的指标体系,构建指标变动程度的模糊信度结构模型,运用模糊证据推理算法结合熵值理论对多指标进行信息融合,并利用灰色关联分析方法评估复杂性测度结果。通过2002~2012年汽车行业数据的实证研究,结果表明:经济环境与国际环境的波动是导致汽车制造产业网络复杂性的最主要因素,技术环境和市场环境次之;近10年来我国汽车制造产业网络外部环境经历了两次较大波动,总体复杂性呈现增长趋势。本研究可为汽车产业环境复杂性测度及应对提供思路参考。     

红外光谱技术在物证鉴定中的应用

物证鉴定过程中,利用仪器分析技术快速准确的检测物证的化学组成会有助于获取更多案件相关的信息,常用的技术手段有色谱、质谱和光谱技术。法庭科学实验室涉及鉴定的物证种类繁多、情形复杂,需要根据物证的种类和仪器特性决定使用何种分析方法。比如气相色谱-质谱联用技术具有高分辨率、高灵敏度、强鉴别能力的优点,常被用于火灾现场易燃液体等物证的检测分析。法庭科学实验室更常见的应用情形是对与案件有关的各种有机物证(墨迹、油漆、塑料等)及无机物(矿物、无机填料等)进行快速有效分析。红外光谱法是鉴定化合物和测定分子结构的最有效的常规分析方法之一,尤其在法庭科学领域物证鉴定中被广泛应用,红外光谱技术可用来确定物证的种类,同时红外光谱又是一种有效的比对检验手段,检验结果可以为法庭断案提供证据。该文综述了红外光谱技术在法庭科学文件检验、油脂检验、塑料检验、油漆检验、纤维检验以及常见毒品等物证检验领域的应用现状及最新的研究进展,并对红外光谱技术在物证鉴定领域的应用前景进行了展望,以期从实际应用的角度为法庭科学研究人员提供红外光谱技术在该领域的研究概貌。     

运用Mercury软件辅助高中晶体结构教学

针对晶体结构教学的抽象性,运用Mercury软件辅助教学,该软件具有读取晶体信息文件、显示或编辑多种样式的晶体结构图等功能,能实现晶体配位数、晶体堆积方式、晶胞模型建构、晶胞微观结构分析等知识点的可视化教学,使教学有据可依,有助于发展学生宏观辨识与微观探析、证据推理与模型认知的化学学科核心素养,是信息技术与学科教学整合的有效应用。     

QML-Morven: A novel framework for learning qualitative differential equation models using both symbolic and evolutionary approaches

In this paper, a novel qualitative differential equation model learning (QML) framework named QML-Morven is presented. QML-Morven employs both symbolic and evolutionary approaches as its learning strategies to deal with models of different complexity. Based on this framework, a series of experiments were designed and carried out to: (1) investigate factors that influence the learning precision and minimum data requirement for successful learning; (2) address the scalability issue of QML systems.