基于犹豫模糊语言多属性群决策的VIKOR扩展方法

针对犹豫模糊语言多属性群决策问题,提出了一种基于可能度分布的VIKOR方法。该方法首先将基于犹豫模糊语言的评价信息转化成可能度分布值,定义了新的距离公式,避免了传统犹豫模糊语言评价信息在计算过程中造成的信息扭曲。然后,设计了基于最大群体效用与最小个体遗憾两个目标的群体信息集结优化模型,并给出多属性群决策的VIKOR扩展方法。运用一个交通建设方案选择的案例分析验证了方法的有效性和优越性。     

强跟踪自适应滤波器实现机动目标的精确跟踪

机动目标状态估计中的一个主要问题是:目标运动的突变性导致状态噪声无法进行统计预测.传统的EKF将噪声看成是高斯白噪声有着本质上的不足,因而无法实现稳定的跟踪.引入Sage-Husa滤波算法对有色噪声进行在线的估计,一定程度上弥补了目标运动模型不够合理的缺憾.在此基础上,从系统容错设计基本原理出发,用归一化残差功率法实时地检测可能出现的数值发散现象,一旦检测到发散,印通过一种改进的强跟踪自适应滤波器进行抑制,有效地提升了滤波的健硕性,实现了稳定跟踪.最后,针对高机动目标的运动特性,仿真验证采用变维滤波模型,用EKF对目标的简单机动进行跟踪,只有目标运动突变时才采用本文提出的算法,以提升计算的实时性.仿真结果表明此算法对高机动目标的跟踪是有效的.     

Sensors incipient fault detection and isolation of nuclear power plant using extended Kalman filter and Kullback–Leibler divergence

Sensor real-time monitoring is an indispensable to achieve reliable plant operation along with stricter safety and environmental measures. This paper presents a statistical algorithm for sensors time-varying incipient fault detection and isolation. The proposed approach formulates the fault detection index and fault signature using the extended Kalman filter. Algorithm relaxes assumption on a monitored system stability and a priori knowledge of the fault profile. Further, fault decision statistics has been devised using Kullback–Leibler Divergence (KLD) and mixed with an Exponential Weighted Moving Average (EWMA) control chart. Pressurized water reactor nuclear power plant temperature and neutron flux sensors incipient fault detection and isolation have been demonstrated to illustrate the effectiveness of proposed methodology.     

Dual Activity BLEG-1 from Bacillus lehensis G1 Revealed Structural Resemblance to B3 Metallo-β-Lactamase and Glyoxalase II: An Insight into Its Enzyme Promiscuity and Evolutionary Divergence

Metallo-β-lactamases (MBLs) are class B β-lactamases from the metallo-hydrolase-like MBL-fold superfamily which act on a broad range of β-lactam antibiotics. A previous study on BLEG-1 (formerly called Bleg1_2437), a hypothetical protein from Bacillus lehensis G1, revealed sequence similarity and activity to B3 subclass MBLs, despite its evolutionary divergence from these enzymes. Its relatedness to glyoxalase II (GLXII) raises the possibility of its enzymatic promiscuity and unique structural features compared to other MBLs and GLXIIs. This present study highlights that BLEG-1 possessed both MBL and GLXII activities with similar catalytic efficiencies. Its crystal structure revealed highly similar active site configuration to YcbL and GloB GLXIIs from Salmonella enterica, and L1 B3 MBL from Stenotrophomonas maltophilia. However, different from GLXIIs, BLEG-1 has an insertion of an active-site loop, forming a binding cavity similar to B3 MBL at the N-terminal region. We propose that BLEG-1 could possibly have evolved from GLXII and adopted MBL activity through this insertion.     

Hybrid Bayesian estimation tree learning with discrete and fuzzy labels

Classical decision tree model is one of the classical machine learning models for its simplicity and effectiveness in applications. However, compared to the DT model, probability estimation trees (PETs) give a better estimation on class probability. In order to get a good probability estimation, we usually need large trees which are not desirable with respect to model transparency. Linguistic decision tree (LDT) is a PET model based on label semantics. Fuzzy labels are used for building the tree and each branch is associated with a probability distribution over classes. If there is no overlap between neighboring fuzzy labels, these fuzzy labels then become discrete labels and a LDT with discrete labels becomes a special case of the PET model. In this paper, two hybrid models by combining the naive Bayes classifier and PETs are proposed in order to build a model with good performance without losing too much transparency. The first model uses naive Bayes estimation given a PET, and the second model uses a set of small-sized PETs as estimators by assuming the independence between these trees. Empirical studies on discrete and fuzzy labels show that the first model outperforms the PET model at shallow depth, and the second model is equivalent to the naive Bayes and PET.     

Dm-KDE: dynamical kernel density estimation by sequences of KDE estimators with fixed number of components over data streams

In many data stream mining applications, traditional density estimation methods such as kernel density estimation, reduced set density estimation can not be applied to the density estimation of data streams because of their high computational burden, processing time and intensive memory allocation requirement. In order to reduce the time and space complexity, a novel density estimation method Dm-KDE over data streams based on the proposed algorithm m-KDE which can be used to design a KDE estimator with the fixed number of kernel components for a dataset is proposed. In this method, Dm-KDE sequence entries are created by algorithm m-KDE instead of all kernels obtained from other density estimation methods. In order to further reduce the storage space, Dm-KDE sequence entries can be merged by calculating their KL divergences. Finally, the probability density functions over arbitrary time or entire time can be estimated through the obtained estimation model. In contrast to the state-of-the-art algorithm SOMKE, the distinctive advantage of the proposed algorithm Dm-KDE exists in that it can achieve the same accuracy with much less fixed number of kernel components such that it is suitable for the scenarios where higher on-line computation about the kernel density estimation over data streams is required.We compare Dm-KDE with SOMKE and M-kernel in terms of density estimation accuracy and running time for various stationary datasets. We also apply Dm-KDE to evolving data streams. Experimental results illustrate the effectiveness of the proposed method.     

飞机座舱人机布局评价方法研究

针对当前座舱工效学重要性日益提升,信息量不断增加,机构愈加繁杂等特点,传统的以单个或多个部件为对象进行的人-机布局评价方法,往往造成综合评价的片面性,导致研发周期长,效率低等问题。考虑到工效学评价模糊性,提炼并建构了基于当前座舱特点的人机布局评价体系,提出基于不确定语言加权平均（ULWA）算子的多属性决策组合的研究方法,建立飞机座舱人机布局评价模型,并进行了推理和验证。     

基于不确定语言信息的物流供应商选择

为解决传统供应商的选择与评价过程中专家评判的模糊性和不确定性问题,提出了一种基于不确定语言信息的物流服务供应商选择方法。该方法以不确定语言变量来表征专家的评判从而避免了决策信息的丢失。继而建立了物流服务质量评价属性的权重优化模型,并构造拉格朗日函数来求解该模型,得到评价属性的最优权重。接着利用不确定语言加权几何平均（ULWG）算子集结专家评判信息,则得到供应商的优先排序。给出了基于不确定语言信息的供应商选择的算法步骤和应用实例。     

语言多属性决策评价采掘工作面通风效果

针对采掘工作面通风效果评价过程中专家更习惯于运用语言评语评价通风效果,基于语言多属性决策方法给出一种基于语言评价标度的评价模型来评价采掘工作面通风效果,并实例验证了该方法的可行性和简便性。     

基于协同网络信息的房地产开发联盟成员选择研究

房地产开发联盟是进行房地产开发的有效联合,何种类型企业间的联合才能达到强强联合的效果,这是值得关注的。文章利用协同网络信息的多指标决策方法对房地产开发联盟的可能参与伙伴,进行了优选排序;采用模糊数对语言变量进行处理,对可能参与伙伴的个体和协同信息进行综合处理,从而选出最佳的开发联盟成员。