Dynamic monitoring and control of software project effort based on an effort buffer

The improvement to the monitoring and control efficiency of software project effort is a challenge for project management research. We propose to overcome this challenge through the use of a model for the buffer determination and monitoring of software project effort. This software project effort buffer was originally determined on the basis of a risk management factor analysis with total consideration for project managers’ risk preference. The effort buffer was next allocated to different stages according to the buffer allocation cardinal. An effort deviation monitoring and control model was then established based on the grey prediction model, including the establishment of a deviation monitoring and control model, a simulation test of the accuracy and the deviation prediction algorithm flow chart. The method system was eventually applied to an actual project and compared with the actual project data. The results show that the relative error test accuracy of the proposed model is qualified according to the test standard of the grey model, signifying that it could be used for the prediction of effort deviation and decision-making. The proposed model could use the dynamic control system to monitor and control software project effort in an effective manner.     

Optimal solution for novel grey polynomial prediction model

The grey prediction model, as a time-series analysis tool, has been used in various fields only with partly known distribution information. The grey polynomial model is a novel method to solve the problem that the original sequence is in accord with a more general trend rather than the special homogeneous or non-homogeneous trend, but how to select the polynomial order still needs further study. In this paper the tuned background coefficient is introduced into the grey polynomial model and then the algorithmic framework for polynomial order selection, background coefficient search and parameter estimation is proposed. The quantitative relations between the affine transformation of accumulating sequence and the parameter estimates are deduced. The modeling performance proves to be independent of the affine transformation. The numerical example and application are carried out to assess the modeling efficiency in comparison with other conventional models.     

A Conformal Geometric Algebra Based Clustering Method and Its Applications

Clustering is one of the most useful methods for understanding similarity among data. However, most conventional clustering methods do not pay sufficient attention to the geometric distributions of data. Geometric algebra (GA) is a generalization of complex numbers and quaternions able to describe spatial objects and the geometric relations between them. This paper uses conformal GA (CGA), which is a part of GA. This paper transforms data from a real Euclidean vector space into a CGA space and presents a new clustering method using conformal vectors. In particular, this paper shows that the proposed method was able to extract the geometric clusters which could not be detected by conventional methods.     

Order reduction of linear discrete systems using a genetic algorithm

Order reduction of linear discrete systems using classical methods of optimization is well understood and developed by various workers. The present effort is towards development of a method of linear discrete system order reduction using a genetic algorithm (GA) to get rid of usual difficulties of classical methods. The method developed is applied to a variety of systems and the reduced order models are obtained using the method. The results reported are encouraging and more work can be initiated in this area using a GA.     

基于Vague集距离与灰色关联的区域产业技术供需协同发展评价研究

技术供需有效协同是实现产业中高端发展的重要驱动,本文综合考虑产业技术供需子系统发展水平、供需子系统间耦合作用及协同效应等因素,将Vague集相似性度量与距离协同模型结合,运用灰色关联分析计算技术供需子系统间的灰色关联度,建立了基于Vague集距离与灰色关联的区域高技术产业技术供需复合系统的综合协同发展模型,并利用该模型对2009年至2016年京津冀地区高技术产业技术供需协同水平数据进行了实证分析。实证分析结果表明,京津冀地区技术供需协同发展处于高协同低发展阶段,综合协同发展水平增速缓慢;供需子系统间相互作用关系显著;北京和天津的技术供给发展较快,需求相对不足,而河北需求旺盛,但供给不足。因此,兼顾技术供需高质量发展与区域技术供需协同匹配是创新引领产业发展的有效路径。     

Evaluating business performance of wealth management banks

This study applies the balanced scorecard method to build a performance evaluation framework for wealth management (WM) banks. Next, the study builds a framework for dealing with sub-criteria using the Delphi method and finally, the article evaluates the business performance of WM banks in Taiwan by applying the analytical hierarchy process (AHP) and grey relational analysis (GRA). Importantly, the proposed model can benefit the banking sector in assessing the business performance of WM banks, making it highly applicable for bank managers and financial sector analysts.     

ARMA model order and parameter estimation using genetic algorithms

A new method for simultaneously determining the order and the parameters of autoregressive moving average (ARMA) models is presented in this article. Given an ARMA (p, q) model in the absence of any information for the order, the correct order of the model (p, q) as well as the correct parameters will be simultaneously determined using genetic algorithms (GAs). These algorithms simply search the order and the parameter spaces to detect their correct values using the GA operators. The proposed method works on the principle of maximizing the GA fitness value relying on the deviation between the actual plant output, with or without an additive noise, and the estimated plant output. Simulation results show in detail the efficiency of the proposed approach. In addition to that, a practical model identification and parameter estimation is conducted in this article with results obtained as desired. The new method is compared with other well-known methods for ARMA model order and parameter estimation.     

不完全信息下的区间直觉模糊数的多属性决策方法

针对属性权重信息不完全的区间直觉模糊的多属性决策问题,提出灰色关联分析的决策方法.该方法首先确定各属性下的最佳和最劣方案,确定各方案与理想方案的灰色关联系数,然后在属性权重信息不完全的情况下,建立基于理想点的最优决策模型,求出属性权重,进而根据与理想方案的相对贴近度对各方案进行排序,最后用实例对该方法进行了说明,理论分析和数据结果表明了方法的可行性和有效性.     

基于理想灰关联距离的课程评估方法

综合灰色系统理论、理想解法和欧氏距离,提出了一种新的基于理想关联距离度的课程评估方法,给出了建立评估模型的基本步骤.定量处理的指标,经过理想化、标准化后,定义关联数,由此计算关联距离度.通过灰色关联距离度,建立了一种接近最优方案远离最差方案的评估模型.并通过学院近期的课程评估实例分析,验证了该方法的准确性和可行性.     

A new multi-objective discrete robust optimization algorithm for engineering design

This paper proposes a novel multi-objective discrete robust optimization (MODRO) algorithm for design of engineering structures involving uncertainties. In the present MODRO procedure, grey relational analysis (GRA), coupled with principal component analysis (PCA), was used as a multicriteria decision making model for converting multiple conflicting objectives into one unified cost function. The optimization process was iterated using the successive Taguchi approach to avoid the limitation that the conventional Taguchi method fails to deal with a large number of design variables and design levels. The proposed method was first verified by a mathematical benchmark example and a ten-bar truss design problem; and then it was applied to a more sophisticated design case of full scale vehicle structure for crashworthiness criteria. The results showed that the algorithm is able to achieve an optimal design in a fairly efficient manner attributable to its integration with the multicriteria decision making model. Note that the optimal design can be directly used in practical applications without further design selection. In addition, it was found that the optimum is close to the corresponding Pareto frontier generated from the other approaches, such as the non-dominated sorting genetic algorithm II (NSGA-II), but can be more robust as a result of introduction of the Taguchi method. Due to its independence on metamodeling techniques, the proposed algorithm could be fairly promising for engineering design problems of high dimensionality.     

A random model approach for the LASSO

The least absolute selection and shrinkage operator (LASSO) is a method of estimation for linear models similar to ridge regression. It shrinks the effect estimates, potentially shrinking some to be identically zero. The amount of shrinkage is governed by a single parameter. Using a random model formulation of the LASSO, this parameter can be specified as the ratio of dispersion parameters. These parameters are estimated using an approximation to the marginal likelihood of the observed data. The observed score equations from the approximation are biased and hence are adjusted by subtracting an empirical estimate of the expected value. After estimation, the model effects can be tested (via simulation) as the distribution of the observed data given that all model effects are zero is known. Two related simulation studies are presented that show that dispersion parameter estimation results in effect estimates that are competitive with other estimation methods (including other LASSO methods).     

TOPSIS-GRA法下的企业动态信用评价方法及其应用

针对传统信用评价方法较少考虑到时间的延续性,且只注重信用基础值而忽视其发展趋势的问题,本文提出了一种具有风险抗性信用奖惩特征的TOPSIS-GRA的动态信用评价方法。首先,利用指标信息量诱导密度算子对静态数据进行综合集成,得到静态综合信用评价值,在此基础上构造动态信用评价加权决策矩阵;其次,在对矩阵进行TOPSIS法验算的过程中嵌入企业风险抗性信用奖惩点,进而得到包含奖惩性质的相对贴近度;再以GRA方法得到各受评企业理想的信用发展趋势关联度,结合两者最终得到融合风险抗性奖惩量、信用基础值和信用发展趋势三项特征的稳定科学的企业动态信用评价结果。最后,给出了一个实证分析,验证了该方法的有效性及合理性。     

A sensibility study of the autobinomial model estimation methods based on a feature similarity index

The estimation of parameters in the Autobinomial model is an important task for characterizing the content of an image and generating synthetic textures. This paper compares the performance of three estimation methods of the model: coding, maximum pseudo-likelihood and conditional least squares, under textures with different levels of additive contamination, using a feature similarity image index, via Monte Carlo studies. This novel framework quantifies the similarity between the original texture and its texture regenerated by each method. Differences in performance were tested with a Repeated Measures ANOVA model design. Simulation results show that the Conditional Least Squares method is associated with the highest value of the similarity image measure in contaminated textures, while Coding and Maximum Pseudo-Likelihood methods have a comparable behavior and there is no clear pattern whether to prefer one over the other. An application for landscape classification using real Landsat images with different spatial resolutions is described.     

Parameter estimation and model selection for mixtures of truncated exponentials

Bayesian networks with mixtures of truncated exponentials (MTEs) support efficient inference algorithms and provide a flexible way of modeling hybrid domains (domains containing both discrete and continuous variables). On the other hand, estimating an MTE from data has turned out to be a difficult task, and most prevalent learning methods treat parameter estimation as a regression problem. The drawback of this approach is that by not directly attempting to find the parameter estimates that maximize the likelihood, there is no principled way of performing subsequent model selection using those parameter estimates. In this paper we describe an estimation method that directly aims at learning the parameters of an MTE potential following a maximum likelihood approach. Empirical results demonstrate that the proposed method yields significantly better likelihood results than existing regression-based methods. We also show how model selection, which in the case of univariate MTEs amounts to partitioning the domain and selecting the number of exponential terms, can be performed using the BIC score.     

A novel hybrid multivariate nonlinear grey model for forecasting the traffic-related emissions

In this paper, we proposed a novel forecasting method using grey system theory for the traffic-related emissions at a national level. In our tests, grey relational analysis was used to identify time lags between input and output variables. We introduced a multivariate nonlinear grey model based on the kernel method to improve the accuracy of traffic-related emissions prediction. By solving a convex optimization problem instead of using an ordinary least squares estimation, the proposed model overcame the limitations of the classic grey forecasting models. A model confidence set test on the realistic results of forecasting traffic-related emissions in European Union member countries showed that the proposed model demonstrated a marked superiority over robust linear regression and support vector regression. Based on the non-methane volatile organic compounds from road transport and the relevant factors of the emission from 2004 to 2016, a more stringent European Union emission reduction commitment to the road transport for each year from 2020 to 2029 was suggested. We also investigated the advantages of the proposed model via the analysis on convergence, robustness, and sensitivity.     

灰色面板数据视域下的相似性和接近性关联度模型拓展

灰色面板数据包含研究对象诸多信息,由于数据类型和结构较为复杂,目前还没有测度其相似性和接近性的关联度模型,针对这一问题。首先,通过投影方法将灰色面板数据转化为样本关于指标的时间序列行为矩阵,矩阵每行为指标的时间序列;然后,定义一般灰数的距离测度和运算法则;最后,基于两折线间斜率与面积的视角,测度相似性和接近性关联系数。进而构建灰色面板数据的相似性和接近性关联度模型,并研究了该模型的性质。实例表明该模型在测度面板数据类型为一般灰数时的相似性和接近性方面具有良好的效果。     

Bank-level estimates of market power

The aim of this study is to provide an empirical methodology for the estimation of market power of individual banks. The new method employs the well-known model of Panzar and Rosse (1987) and proposes its estimation using the local regression technique. Local regression yields coefficient estimates equal to the number of observations and, thus, market power is estimated for each bank at each point in time. In addition, a number of restrictive assumptions regarding the properties of the production function of banks are relaxed. A panel of banks from transition countries that has been recently employed by Delis (2010) to obtain market power estimates using the Panzar and Rosse model at the country level is used for comparative purposes. We find that country averages of the bank-level results exhibit a very close relationship with standard, industry-level Panzar-Rosse estimates. However, the empirical results suggest that many banks in countries with fairly competitive banking systems deviate significantly from the country averages and that market power varies substantially across banks in each country.     

Locally extra-optimal regularizing algorithms and a posteriori estimates of the accuracy for ill-posed problems with discontinuous solutions

Local a posteriori estimates of the accuracy of approximate solutions to ill-posed inverse problems with discontinuous solutions from the classes of functions of several variables with bounded variations of the Hardy or Giusti type are studied. Unlike global estimates (in the norm), local estimates of accuracy are carried out using certain linear estimation functionals (e.g., using the mean value of the solution on a given fragment of its support). The concept of a locally extra-optimal regularizing algorithm for solving ill-posed inverse problems, which has an optimal in order local a posteriori estimate, was introduced. A method for calculating local a posteriori estimates of accuracy with the use of some distinguished classes of linear functionals for the problems with discontinuous solutions is proposed. For linear inverse problems, the method is bases on solving specialized convex optimization problems. Examples of locally extra-optimal regularizing algorithms and results of numerical experiments on a posteriori estimation of the accuracy of solutions for different linear estimation functionals are presented.     

房地产板块与金融板块指数相关性研究——基于Copula-Kermel模型的分析

近年来,房地产市场与金融市场的关联关系越来越紧密.选取2001年7月3日至2011年9月30日房地产板块与金融板块指数日收益率数据,利用非参数核密度估计单指数收益率的边缘分布,采用Copula方法定量刻画两者的相关结构及尾部相关性.实证结果表明:T-Student-Copula是描述房地产和金融板块指数日收益率的最佳Copula函数形式,且两者具有较强的上尾和下尾相关关系,因此投资者不能通过投资这两类股票降低投资组合风险.另外,政府在制定宏观经济政策时,一方面需注意在采取措施促进金融行业发展时,要防范房地产泡沫的加剧,另一方面还需注意在对房地产业进行调控时,要防止金融业的衰退.     

信息产业发展对区域产业结构高度化的作用机制

构建了信息产业发展与区域产业结构高度化的指标体系,利用基于OWA算子的灰色关联模型融合方法测算了电子信息产业发展与区域产业结构高度化的关联程度.结果表明,电子信息产业的发展是区域产业结构高度化的重要支撑力.东部地区的信息产业发展与区域产业结构高度化指标体系中的环保资金投入增加量和第三产业增加值的关联度较高.中部、西部地区的信息产业发展与新兴产业产值的关联程度较大.从全国范围来看,信息产业的科技创新转化能力对产业结构高度化的推动作用仍较低.