一种有效折扣证据源的冲突证据合成方法

为解决证据组合规则中一票否决和弱决策证据在低冲突情况下出现的反直观推理的现象，提出一种有效折扣证据源的冲突证据合成方法。首先根据证据的信任函数和似然函数求得证据间的相似度，然后求出证据之间的支持程度，并确定折扣因子，最后采用Dempster组合规则合成利用折扣因子修正后的证据源。数值算例分析结果表明，改进后的证据组合方法可以有效地处理证据冲突。     

Integrated rough VIKOR for customer-involved design concept evaluation combining with customers’ preferences and designers’ perceptions

Customer-involved design concept evaluation (DCE) allows customers to take part in evaluating the design alternatives to get more popular design concept. Traditional customer-involved DCE methods still focus on the collection of customer responses and only consider cost and benefit characteristics of design criteria in multi-criteria decision-making (MCDM) based evaluation process. Few studies have customized the decision-making algorithms specifically aimed at customers’ preferences. This paper further explores the customers’ influences in the early stages of the product design development, and proposes a new rough number based MCDM model (i.e., VIKOR) incorporating customers’ preferences for design specifications along with designers’ perceptions for the characteristics of design criteria (cost and benefit) to perform concept evaluation under subjective environment, and this proposed method is named as integrated rough VIKOR (IR-VIKOR). The objective of this study is to identify the best design concept which maximizes the satisfactions of expectations from most customers as well as conforms to the characteristics of design criteria. Firstly, Shannon entropy is used to obtain the weightings and relative importance ratings of design criteria from the customers’ preferences. Secondly, the customers’ preferences for design attribute values, the importance ratings of design criteria and the characteristics of design criteria are combined together to define the ideal solutions to calculate the rough evaluation index of each design alternative in IR-VIKOR, and finally the ranking result is provided by IR-VIKOR to determine the best design concept. A practical design example is introduced to illustrate the evaluation process of this proposed method, and the empirical comparisons are further carried out to validate its superiority for DCE. Through the sensitivity analysis experiments including i) inside IR-VIKOR, and ii) between IR-VIKOR and other classical MCDM methods, the proposed method is proved to be a reliable and feasible customer-involved DCE approach.     

Influence of interfaces roughness on elastic properties of layered media

We present a numerical study of the effect of the interfaces’ roughness on layered media’s upscaled elastic parameters. First, we consider a layered model with two types of elastic materials, assuming that the interfaces are not flat but rough, and apply the numerical upscaling technique to estimate the effective elastic properties of such models. After that, we apply a downscaling technique to reconstruct a layered media with flat interfaces but with uncertainties in elastic moduli of the layers. Next, we compute the covariance of the elements of the reconstructed stiffness matrix and prove that the logarithm of this matrix is linearly related to the logarithms of the standard deviation and the correlation length of the interfaces of the original problem. Finally, we use generated dataset to estimate covariance matrices of the stiffness matrix for arbitrary interface roughnesses.     

Prioritizing the alternatives of the natural gas grid conversion to hydrogen using a hybrid interval rough based Dombi MARCOS model

The aim of this study is to disclose an integrated multi-criteria decision making model as an instrument able to be used for hydrogen gas grid development via natural gas network conversion by considering four main criteria and sixteen sub-criteria. The proposed model consists of two stages using interval rough based Dombi MARCOS (Measurement Alternatives and Ranking according to the COmpromise Solution) method. In the first stage, the interval rough Bonferroni logarithmic methodology of additive weights is applied for determining the weight coefficients. In the second stage, the improved interval rough MARCOS method is used to rank the alternatives. A case study for hydrogen gas grid development in Romania set out the applicability and effectiveness of the proposed model. The proposed model is analysed from various points of view so that it can be used effectively in reality not only theoretically. This study analyzes four technical alternatives for hydrogen gas grid development. The results show that the conversion for mixtures natural gas with hydrogen a transporting a mix of gases is the best alternative among the four alternatives, for the next period and in the specific context described by this study. The current technical-economic criteria are rather a constraint when discussing gas infrastructures. For choosing a sustainable solution, a major role comes for decision makers, along with the desire to pay fairly from the consumer.     

基于粗糙集和BP神经网络的刀具状态监测

在刀具磨损状态监测中,能够提取到的反映不同刀具磨损状态的特征量较大,基于神经网络的状态识别无法去掉冗余特征,会存在训练时间长和准确率降低等问题。针对这些问题,提出基于粗糙集-BP神经网络的刀具磨损状态监测方法,利用粗糙集对特征进行属性约简,去掉冗余信息,从而优化特征,并且减少神经网络的输入端数据,可以缩短神经网络的训练时间和提高识别的准确率。通过对实测刀具数据进行分析,证明了该方法的有效性。     

甘肃某镜铁矿尾矿磁化焙烧—弱磁选试验

甘肃某镜铁矿尾矿中尚含有22.39%的铁,且铁主要以镜铁矿形式存在,其次以菱铁矿形式存在。为了给该尾矿的综合利用提供技术支持,以甘肃某焦化厂生产的半焦化煤粉作为还原剂,对该尾矿进行了磁化焙烧—弱磁选工艺研究。结果表明:在煤粉与原尾矿的质量比为1.5%、温度为750℃的条件下磁化焙烧60 min,可使原尾矿中绝大部分的镜铁矿和菱铁矿转化为磁铁矿;焙烧矿磨至-0.074 mm占87.36%后经1次弱磁粗选和1次弱磁扫选—粗、扫选所得粗精矿按0.045 mm筛分—筛下物1次弱磁精选—精选精矿与筛上物合并,可以获得铁品位为54.57%、铁回收率为78.97%的最终铁精矿。     

基于QFD电动汽车技术特性重要度的确定方法研究

技术重要度是开展电动汽车综合设计优化的目标函数,提出了一种基于QFD的电动汽车关键技术重要度的综合确定方法。方法分析了电动汽车顾客需求和电动汽车技术指标,基于质量屋建立顾客需求重要度与技术特性重要度耦合模型,应用粗糙数进行顾客需求重要度排序,根据电动汽车市场竞争性评估对其进行修正;应用灰色关联法确定顾客需求与技术特性之间的相关关系,分析电动汽车技术特性自相关关系建立相应的修正决策系统。基于该方法,以本研究院研究车型与市场同类车型进行实例分析,证明了方法的有效性和可行性。     

基于高斯混合势化概率假设密度的脉冲多普勒雷达多目标跟踪算法

为在新兴的随机有限集(RFS)框架下充分利用多普勒信息跟踪杂波环境下的多目标,该文提出基于高斯混合势化概率假设密度(GM-CPHD)的脉冲多普勒雷达多目标跟踪(MTT)算法.该算法在标准GM-CPHD基础上,在使用位置量测更新状态后,再利用多普勒量测进行序贯更新,可获得更精确的似然函数和状态估计.仿真结果验证了该算法的有效性,表明在GM-CPHD基础上引入目标的多普勒信息可有效抑制杂波,显著改善跟踪性能.     

Rule induction for hierarchical attributes using a rough set for the selection of a green fleet

Rough set theory (RST) has been the subject of much study and numerous applications in many areas. However, most previous studies on rough sets have focused on finding rules where the decision attribute has a flat, rather than hierarchical structure. In practical applications, attributes are often organized hierarchically to represent general/specific meanings. This paper (1) determines the optimal decision attribute in a hierarchical level-search procedure, level by level, (2) merges the two stages, generating reducts and inducting decision rules, into a one-shot solution that reduces the need for memory space and the computational complexity and (3) uses a revised strength index to identify meaningful reducts and to improve their accuracy. The selection of a green fleet is used to validate the superiority of the proposed approach and its potential benefits to a decision-making process for transportation industry.