Initiating software process improvement in very small enterprises: Experience with a light assessment tool

The paper concerns software process improvement in Very Small Enterprises (VSEs). It presents briefly a gradual methodology to initiate software process improvement in VSE through three steps approach and develops the first and most original step. This first step is based on a light evaluation achieved by means of a dedicated Micro-Evaluation approach. It has been experimented during 7 years in 86 organizations from three countries. The experience with that utilization tends to show that such a light approach is practicable and promising, at least for the targeted enterprises.     

MEMS微结构变形行为尺度效应的研究进展

微米尺寸驱动结构广泛应用于微机电系统（MEMS）和集成电路等微／纳米系统中。由于这些微米尺寸驱动结构的几何尺寸和其微结构尺寸均在微米至纳米范围,它们的弹性、塑性性能及其变形行为具有明显的尺度效应。简要概述了近年来国内外有关微米尺寸驱动结构的弹性性能和变形特性尺度效应的研究情况,介绍了高阶理论的发展和塑性应变理论与微极理论在尺度效应分析中的应用,并对MEMS今后需要重点研究的方向进行了展望。     

Consideration effect of uncertainty in power system reliability indices using radial basis function network and fuzzy logic theory

Reliability assessment of composite power systems is a critical and important part of power investigations especially in the market-driven environments. Therefore, the reliability indices as criteria for the comparison of the reliability of the power systems should be evaluated precisely and carefully. Because of the nonlinear behavior of the systems as the effect of different parameters like weather conditions, load pattern changes and some others, reliability indices always contain much uncertainty. In this paper a neuro-fuzzy based method is proposed to reduce the degree of the uncertainty in the reliability indices and therefore to evaluate the reliability of the composite power systems precisely. Fuzzy logic theory makes it possible to make use of the human experts knowledge in the reliability evaluations. Also by the use of RBFNN and its powerful characteristic to learn any nonlinear mapping between two states it would be possible to evaluate the reliability indices for every short time interval needed so that reliability evaluation in real time would be achievable and feasible.In this paper the RBFNN is trained by the training patterns that are achieved by the use of fuzzy logic theory, then the results are examined on a standard Reliability Test System (RTS-96).     

A unified framework for fault detection and isolation of sensor and actuator biases in linear time invariant systems using marginalized likelihood ratio test with uniform priors

This study aims to present a fault detection and isolation (FDI) framework based on the marginalized likelihood ratio (MLR) approach using uniform priors for fault magnitudes in sensors and actuators. The existing methods in the literature use either flat priors with infinite support or the Gamma distribution as priors for the fault magnitudes. In the current study, it is assumed that the fault magnitude is a realization of a uniform prior with known upper and lower limits. The method presented in this study performs detection of time of occurrence of the fault and isolation of the fault type simultaneously while the estimation of the fault magnitude is achieved using a least squares based approach. The newly proposed method is evaluated by application to a benchmark CSTR problem using Monte Carlo simulations and the results reveal that this method can estimate the time of occurrence of the fault and the fault magnitude more accurately compared to a generalized likelihood ratio (GLR) based approach applied to the same benchmark problem. Simulation results on a benchmark problem also show significantly lower misclassification rates.     

Numerical inversions of a source term in the FADE with a Dirichlet boundary condition using final observations

This paper deals with an inverse problem of determining a source term in the one-dimensional fractional advection-dispersion equation (FADE) with a Dirichlet boundary condition on a finite domain, using final observations. On the basis of the shifted Grünwald formula, a finite difference scheme for the forward problem of the FADE is given, by means of which the source magnitude depending upon the space variable is reconstructed numerically by applying an optimal perturbation regularization algorithm. Numerical inversions with noisy data are carried out for the unknowns taking three functional forms: polynomials, trigonometric functions and index functions. The reconstruction results show that the inversion algorithm is efficient for the inverse problem of determining source terms in a FADE, and the algorithm is also stable for additional data having random noises.