A two-phase approach for setting targets in DEA with negative data

Conventional DEA models have been introduced to deal with non-negative data. In the real world, in some occasions, we have outputs and/or inputs, which can take negative data. In DEA literature some approaches have been presented for evaluating performance of units, which operate with negative data. In this paper, firstly, we give a brief review of these works, then we present a new additive based approach in this framework. The proposed model is designed to provide a target with non-negative value associated with negative components for each observed unit, failed by other methods. An empirical application in banking is then used to show the applicability of the proposed method and make a comparison with the other approaches in the literature.     

A bargaining game model for measuring performance of two-stage network structures

Data envelopment analysis (DEA) is a method for measuring the efficiency of peer decision making units (DMUs), where the internal structures of DMUs are treated as a black-box. Recently DEA has been extended to examine the efficiency of DMUs that have two-stage network structures or processes, where all the outputs from the first stage are intermediate measures that make up the inputs to the second stage. The resulting two-stage DEA model not only provides an overall efficiency score for the entire process, but also yields an efficiency score for each of the individual stages. The current paper develops a Nash bargaining game model to measure the performance of DMUs that have a two-stage structure. Under Nash bargaining theory, the two stages are viewed as players and the DEA efficiency model is a cooperative game model. It is shown that when only one intermediate measure exists between the two stages, our newly developed Nash bargaining game approach yields the same results as applying the standard DEA approach to each stage separately. Two real world data sets are used to demonstrate our bargaining game model.     

Mining axiomatic fuzzy set association rules for classification problems

In this paper, we propose a novel method to mine association rules for classification problems namely AFSRC (AFS association rules for classification) realized in the framework of the axiomatic fuzzy set (AFS) theory. This model provides a simple and efficient rule generation mechanism. It can also retain meaningful rules for imbalanced classes by fuzzifying the concept of the class support of a rule. In addition, AFSRC can handle different data types occurring simultaneously. Furthermore, the new model can produce membership functions automatically by processing available data. An extensive suite of experiments are reported which offer a comprehensive comparison of the performance of the method with the performance of some other methods available in the literature. The experimental result shows that AFSRC outperforms most of other methods when being quantified in terms of accuracy and interpretability. AFSRC forms a classifier with high accuracy and more interpretable rule base of smaller size while retaining a sound balance between these two characteristics.     

Gaining insight into student satisfaction using comprehensible data mining techniques

As a consequence of the heightened competition on the education market, the management of educational institutions often attempts to collect information on what drives student satisfaction by e.g. organizing large scale surveys amongst the student population. Until now, this source of potentially very valuable information remains largely untapped. In this study, we address this issue by investigating the applicability of different data mining techniques to identify the main drivers of student satisfaction in two business education institutions. In the end, the resulting models are to be used by the management to support the strategic decision making process. Hence, the aspect of model comprehensibility is considered to be at least equally important as model performance. It is found that data mining techniques are able to select a surprisingly small number of constructs that require attention in order to manage student satisfaction.     

在SDSS DR8恒星光谱中自动搜寻稀有天体

SDSS DR8海量光谱中包含许多有研究价值的稀有天体,如特殊白矮星(DZ,DQ,DC)、碳星、白矮主序双星、激变变星等,如何在海量光谱中自动搜寻稀有天体有着极其重要的意义。提出一种基于核密度估计和K-近邻(K-nearest neighbor, KNN)相结合的方法在SDSS DR8 信噪比大于5的546 383个恒星光谱中搜寻稀有天体。首先对光谱进行高斯核密度估计,选取概率最小的5 000个光谱作为稀有类,概率最大的300 000个光谱作为普通类,然后进行KNN分类,同时也将5 000个稀有光谱的K个最近邻也作为稀有的天体,结果共有21 193条光谱。为了方便分析,对这些光谱聚类后进行人工检查。这些光谱主要包括由于数据缺失、红化、流量定标不准引起的问题光谱、行星状星云、没有物理联系的光谱双星、类星体、特殊白矮星(DZ,DQ,DC)、碳星、白矮主序双星、激变变星等。通过和SIMBAD,NED,ADS及一些主要的文献交叉验证,我们新发现了3个DZ白矮星、1个白矮主序双星、2个伴星为G型星的激变变星,3个激变变星的候选体、6个DC白矮星,1个DC白矮星候选体和1个 BL Lacertae(BL lac)候选体。还发现了1个有CaⅡ三重发射线和MgⅠ发射线的DA白矮星和1个光谱上表现出发射线的晚M恒星但测光图上像是一个星云或星系。     

基于LabVIEW的砝码自动检定系统设计与实现

针对目前砝码检定工作操作过程复杂、检定效率低等问题,设计了一种基于LabVIEW的砝码自动检定系统。在符合砝码检定规程的情况下,系统实现了检定数据实时采集、数据处理、生成原始记录、管理标准器、查询历史数据和衡器控制等功能。相对现有的砝码检定方法有操作简单、高效、程序移植性好等优点。系统由主控计算机、串口转无线扩展卡、多台衡器设备及相应的标准砝码装置组成。通过使用串口转Wi-Fi模块,系统可实现控制多台衡器设备的需求。实验结果证明,系统运行稳定可靠,采集数据准确,容易扩展,且大幅度减轻了检定人员的劳动强度,在计量检定工作中具有一定的实用和推广价值。     

基于CAN总线的运动控制通讯系统设计

数字量驱动的运动控制要求通讯系统具有良好的实时性和可靠性。设计了一种基于CAN总线的多节点多通道的运动控制通讯系统,以FPGA为节点控制器,通过SJA1000收发运动控制数据,并行发送到各通道驱动器的数模转换器中。FPGA模块由Verilog HDL编写,通过参数传递和生成块的方法简化代码,提高系统的可移植性,移植时只需改变两个常量值即可适应各个节点不同的通道数和位宽。实验表明,通讯系统每帧耗时约120us,最高帧频可达8.5kHz,误码率低于10^-8,实时性和可靠性较好。     

便携式X射线荧光光谱法与原子吸收/原子荧光法测定土壤重金属的对比研究

应用便携式X射线荧光光谱仪(PXRF)分别在原位和实验室条件下对53个土壤样品中的Cu,Pb,As,Cr,Ni和Zn等重金属进行测定,并与原子吸收/原子荧光法测定值进行对比,建立一元线性回归模型分析PXRF数据质量。通过测定土壤样品原位含水量并选取部分样品进行室内水分定量实验,分析土壤水分对于PXRF测定结果的影响。结果表明,PXRF检出限分别为Cu: 10.6 mg·kg-1,Pb: 8.1 mg·kg-1,As: 5.7 mg·kg-1,Cr: 22.5 mg·kg-1,Ni: 21.6 mg·kg-1,Zn: 10.4 mg·kg-1;原位测定时Pb,Cr,Ni和Zn可以达到定量水平;经过风干磨细处理,Cu,Pb,Cr,Ni和Zn在实验室条件下可以达到定量水平,说明X射线荧光光谱法适用于土壤重金属的快速测定与评价。水分对于PXRF测定结果具有“稀释”作用,原位条件下土壤含水量<15%时与>25%时样品的平均相对误差分别为-17%与-31%;实验室条件下土壤含水量从风干土水平提高到30%,测定的平均相对误差由10%变为-24%。土壤水分升高可能会导致数据质量和准确性降低,建议原位测定时控制土壤含水量在25%以内。     

Determining Modes for Continuous Data Assimilation in 2D Turbulence

We study the number of determining modes necessary for continuous data assimilation in the two-dimensional incompressible Navier–Stokes equations. Our focus is on how the spatial structure of the body forcing affects the rate of continuous data assimilation and the number of determining modes. We treat this problem analytically by proving a convergence result depending on the H ^–1 norm of f and computationally by considering a family of forcing functions with identical Grashof numbers that are supported on different annuli in Fourier space. The rate of continuous data assimilation and the number of determining modes is shown to depend strongly on the length scales present in the forcing.     

Multicategory classification via discrete support vector machines

Discrete support vector machines (DSVM), originally proposed for binary classification problems, have been shown to outperform other competing approaches on well-known benchmark datasets. Here we address their extension to multicategory classification, by developing three different methods. Two of them are based respectively on one-against-all and round-robin classification schemes, in which a number of binary discrimination problems are solved by means of a variant of DSVM. The third method directly addresses the multicategory classification task, by building a decision tree in which an optimal split to separate classes is derived at each node by a new extended formulation of DSVM. Computational tests on publicly available datasets are then conducted to compare the three multicategory classifiers based on DSVM with other methods, indicating that the proposed techniques achieve significantly higher accuracies. This research was partially supported by PRIN grant 2004132117.