屏蔽系统寿命数据的统计分析综述(英文)

系统竞争失效数据在工程应用中广泛存在.屏蔽数据作为它的一种特殊数据形式在工程中有着重要的作用.本文首先介绍了屏蔽数据的形式及其与常规竞争失效数据的区别,然后对串联系统或并联系统,阐述了两种分析屏蔽数据的方法(极大似然法和贝叶斯方法),最后用这两种方法分析了一个实际例子.     

Generalised soft binomial American real option pricing model (fuzzy–stochastic approach)

The stochastic discrete binomial models and continuous models are usually applied in option valuation. Valuation of the real American options is solved usually by the numerical procedures. Therefore, binomial model is suitable approach for appraising the options of American type. However, there is not in several situations especially in real option methodology application at to disposal input data of required quality. Two aspects of input data uncertainty should be distinguished; risk (stochastic) and vagueness (fuzzy). Traditionally, input data are in a form of real (crisp) numbers or crisp-stochastic distribution function. Therefore, hybrid models, combination of risk and vagueness could be useful approach in option valuation. Generalised hybrid fuzzy–stochastic binomial American real option model under fuzzy numbers (T-numbers) and Decomposition principle is proposed and described. Input data (up index, down index, growth rate, initial underlying asset price, exercise price and risk-free rate) are in a form of fuzzy numbers and result, possibility-expected option value is also determined vaguely as a fuzzy set. Illustrative example of equity valuation as an American real call option is presented.     

Cost efficiency measures in data envelopment analysis with data uncertainty

This paper extends the classical cost efficiency (CE) models to include data uncertainty. We believe that many research situations are best described by the intermediate case, where some uncertain input and output data are available. In such cases, the classical cost efficiency models cannot be used, because input and output data appear in the form of ranges. When the data are imprecise in the form of ranges, the cost efficiency measure calculated from the data should be uncertain as well. So, in the current paper, we develop a method for the estimation of upper and lower bounds for the cost efficiency measure in situations of uncertain input and output data. Also, we develop the theory of efficiency measurement so as to accommodate incomplete price information by deriving upper and lower bounds for the cost efficiency measure. The practical application of these bounds is illustrated by a numerical example.     

An effective transformation in ranking using l1-norm in data envelopment analysis

Jahanshahloo et al. [G. R. Jahanshahloo, F. Hosseinzadeh Lotfi, N. Shoja, G. Tohidi, S. Razavyan, Ranking using l₁-norm in data envelopment analysis, Applied Mathematics and Computation, 153 (2004) 215-224] present a method for ranking extremely efficient decision making units (DMUs) in data envelopment analysis (DEA) by exploiting the leave-one-out idea and l₁-norm. It is shown that the proposed method is able to remove the existing difficulties in some methods. This paper suggests an effective procedure to transfer the proposed model from the nonlinear programming form into a linear programming form. We show that the model with this transformation is equivalent to the nonlinear model, while it is much easier to solve than the treatment in [1].     

Low-order self-tuner for fault-tolerant control of a class of unknown nonlinear stochastic sampled-data systems

Based on the modified state-space self-tuning control (STC) via the observer/Kalman filter identification (OKID) method, an effective low-order tuner for fault-tolerant control of a class of unknown nonlinear stochastic sampled-data systems is proposed in this paper. The OKID method is a time-domain technique that identifies a discrete input–output map by using known input–output sampled data in the general coordinate form, through an extension of the eigensystem realization algorithm (ERA). Then, the above identified model in a general coordinate form is transformed to an observer form to provide a computationally effective initialization for a low-order on-line “auto-regressive moving average process with exogenous (ARMAX) model”-based identification. Furthermore, the proposed approach uses a modified Kalman filter estimate algorithm and the current-output-based observer to repair the drawback of the system multiple failures. Thus, the fault-tolerant control (FTC) performance can be significantly improved. As a result, a low-order state-space self-tuning control (STC) is constructed. Finally, the method is applied for a three-tank system with various faults to demonstrate the effectiveness of the proposed methodology.     

A new robust DEA model and super-efficiency measure

Applications of traditional data envelopments analysis (DEA) models require knowledge of crisp input and output data. However, the real-world problems often deal with imprecise or ambiguous data. In this paper, the problem of considering uncertainty in the equality constraints is analyzed and by using the equivalent form of CCR model, a suitable robust DEA model is derived in order to analyze the efficiency of decision-making units (DMUs) under the assumption of uncertainty in both input and output spaces. The new model based on the robust optimization approach is suggested. Using the proposed model, it is possible to evaluate the efficiency of the DMUs in the presence of uncertainty in a fewer steps compared to other models. In addition, using the new proposed robust DEA model and envelopment form of CCR model, two linear robust super-efficiency models for complete ranking of DMUs are proposed. Two different case studies of different contexts are taken as numerical examples in order to compare the proposed model with other approaches. The examples also illustrate various possible applications of new models.     

The ANN application in FEM modeling of mechanical properties of Al–Si alloy

Artificial neural network (ANN) is a nonlinear dynamic computational system suitable for simulations which are hard to be described by physical models where, rather than relying on a number of predetermined assumptions, data is used to form the model. In order to predict the mechanical properties of A356 including yield stress, ultimate tensile strength and elongation percentage, a relatively new approach that uses artificial neural network and finite element technique is presented which combines mechanical properties data in the form of experimental and simulated solidification conditions. It was observed that predictions of this study are consistent with experimental measurements for A356 alloy. The results of this research were also used for solidification codes of SUT CAST software.     

Nekhoroshev theorem for small amplitude solutions in nonlinear Schrödinger equations

We prove a Nekhoroshev type result [26,27] for the nonlinear Schr?dinger equation with vanishing or periodic boundary conditions on ; here is a parameter and is a function analytic in a neighborhood of the origin and such that , . More precisely, we consider the Cauchy problem for (0.1) with initial data which extend to analytic entire functions of finite order, and prove that all the actions of the linearized system are approximate constants of motion up to times growing faster than any negative power of the size of the initial datum. The proof is obtained by a method which applies to Hamiltonian perturbations of linear PDE's with the following properties: (i) the linear dynamics is periodic (ii) there exists a finite order Birkhoff normal form which is integrable and quasi convex as a function of the action variables. Eq. (0.1) satisfies (i) and (ii) when restricted to a level surface of , which is an integral of motion. The main technical tool used in the proof is a normal form lemma for systems with symmetry which is also proved here. Received June 23, 1997; in final form June 1, 1998     

Integrated exact,hybrid and metaheuristic learning methods for confidentiality protection

A vital task facing government agencies and commercial organizations that report data is to represent the data in a meaningful way and simultaneously to protect the confidentiality of critical components of this data. The challenge is to organize and disseminate data in a form that prevents such critical components from being inferred by groups bent on corporate espionage, to gain competitive advantages, or having a desire to penetrate the security of the information underlying the data. Controlled tabular adjustment is a recently developed approach for protecting sensitive information by imposing a special form of statistical disclosure limitation on tabular data. The underlying model gives rise to a mixed integer linear programming problem involving both continuous and discrete (zero-one) variables. We develop stratified ordered (s-ordered) heuristics and a new meta-heuristic learning approach for solving this model, and compare their performance to previous heuristics and to an exact algorithm embodied in the state-of-the-art ILOG- CPLEX software. Our new approaches are based on partitioning the problem into its discrete and continuous components, first creating an s-ordered heuristic that reduces the number of binary variables through a grouping procedure that combines an exact mathematical programming model with constructive heuristics. To gain further advantages we then replace the mathematical programming model with an evolutionary scatter search approach that makes it possible to extend the method to large problems with over 9000 entries. Finally, we introduce a new metaheuristic learning method that significantly improves the quality of solutions obtained.     

DEA与数据挖掘

使用Wei和Yan给出的凸多面体的"和形式"与"交形式"相互转化的方法,得到"交形式"的生产可能集TWY,以及由此判别相对效率的方法,研究DEA用于数据挖掘,处理具有"海量"决策单元的相对效率评价(包括技术有效性和规模收益递增、不变、递减以及"拥挤"迹象).给出的方法只需使用一个"交形式"的生产可能集,与本作者先前的"DEA评测机"相比,可以节省很多计算量.方法是对DEA评测机的一种新的改进,是对数据挖掘领域的一个补充.     

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The hybrid censoring scheme is a mixture of type-I and type-II censoring schemes. It is a popular censoring scheme in the literature of life data analysis. Mixed exponential distribution (MED) models is a class of favorable models in reliability statistics. Nevertheless, there is no much discussion to focus on parameters estimation for MED models with hybrid censored samples. We will address this problem in this paper. The EM (Expectation-Maximization) algorithm is employed to derive the closed form of the maximum likelihood estimators (MLEs). Finally, Monte Carlo simulations and a real-world data analysis are conducted to illustrate the proposed method.     

An Artificial Immune System for Clustering Amino Acids in the Context of Protein Function Classification

This paper addresses the classification task of data mining (a form of supervised learning) in the context of an important bioinformatics problem, namely the prediction of protein functions. This problem is cast as a hierarchical classification problem. The protein functions to be predicted correspond to classes that are arranged in a hierarchical structure (this takes the form of a class tree). The main contribution of this paper is to propose a new Artificial Immune System that creates a new representation for proteins, in order to maximize the predictive accuracy of a hierarchical classification algorithm applied to the corresponding protein function prediction problem.      

WRAO and OWA learning using Levenberg–Marquardt and genetic algorithms

The generalized Weighted Relevance Aggregation Operator (WRAO) is a non-additive aggregation function. The Ordered Weighted Aggregation Operator (OWA) (or its generalized form: Generalized Ordered Weighted Aggregation Operator (GOWA)) is more restricted with the additivity constraint in its weights. In addition, it has an extra weights reordering step making it hard to learn automatically from data. Our intension here is to compare the efficiency (or effectiveness) of learning these two types of aggregation functions from empirical data. We employed two methods to learn WRAO and GOWA: Levenberg–Marquardt (LM) and a Genetic Algorithm (GA) based method. We use UCI (University of California Irvine) benchmark data to compare the aggregation performance of non-additive WRAO and additive GOWA. We found that the non-constrained aggregation function WRAO was learnt well automatically and produced consistent results, while GOWA was learnt less well and quite inconsistently.     

源项反演问题的条件稳定性

对于一维扩散方程的源项反演问题,探讨了反问题数据的相容性并应用积分恒等式方法建立了非线性源项反演的一种稳定性.     

单调序列的非齐次GM(1,1)模型

GM(1,1)模型的白化解为齐次指数形式,而一般数据呈非齐次指数形式,存在形式上的差异.本文运用非齐次级比与非齐次指数函数的对应关系,对原始序列中相邻数据做差处理,得到新的序列,将非齐次指数序列转换为齐次指数序列,再建立GM(1,1)模型.实例表明,运用初值优化和非齐次化能提高GM(1,1)模型的精度.     

A square root interest rate model fitting discrete initial term structure data

This paper presents one-factor and multifactor versions of a term structure model in which the factor dynamics are given by Cox/Ingersoll/Ross (CIR) type ‘square root’ diffusions with piece wise constant parameters. The model is fitted to initial term structures given by a finite number of data points, interpolating endogenously. Closed form and near closed form solutions for a large class of fixed income derivatives are derived in terms of a compound noncentral chi-square distribution. An implementation of the model is discussed where the initial term structure of volatility is fitted via cap prices.     

Incorporating the learning effect into data envelopment analysis to measure MSW recycling performance

The effect of organizational learning, which results in continuous improvement of organizational performance over time, has been widely discussed. The cumulative learning effect may form as a source of intellectual capital. Thus far, the static data envelopment analysis (DEA) model has not been used to examine the longitudinal learning effect. Therefore, a two-stage approach is developed together with the estimation of a latent learning effect using time-series data; the estimated learning effect is then used as an input in the DEA Slacks-Based Measure (SBM) model. The proposed DEA SBM model can be used to investigate the efficiency of the organizational learning effect of Municipal Solid Waste (MSW) recycling systems.     

A longitudinal Model for repeated interval-observed data with informative dropouts

We consider repeated measures interval-observed data with informative dropouts. We model the repeated outcomes via an unobserved random intercept and it is assumed that the probability of dropout during the study period is linearly related to the random intercept in a complementary log-log scale. Assuming the random effect follows the power variance function (PVF) family suggested by Hougaard (2000), we derive the marginal likelihood in a closed form. We evaluate the performance of the maximum likelihood estimation via simulation studies and apply the proposed method to a real data set.     

A non-parametric test for composite hypotheses in survival analysis

Summary For survival data with several concomitant (regressor) variables a large sample non-parametric procedure is presented which provides significance tests of hypotheses about a subset of the concomitant variables. This non-iterative procedure resembles linear model methodology in simplicity and form. The method is useful to eliminate unimportant concomitant variables prior to estimation of model parameters.     

Least-squares Spline Regression with Block-diagonal Variance Matrices

A numerical method of solution is presented for the least squaresfitting of experimental data by spline functions in the casewhere the data errors are correlated and for which the variancematrix is specified. The method is general in that it permits(a) splines of any order, (b) the knots of the spline to bearbitrary in number and position, and (c) variance matricesthat are block diagonal in form. Since limiting forms of (c)are diagonal and full variance matrices, the method can handle,as special cases, both conventional spline regression problemsand spline regression problems with general, unstructured variancematrices. An application to gamma spectrometry, in which theblocks of the variance matrix have special structure, is fullytreated.