An adaptive network based fuzzy inference system–auto regression–analysis of variance algorithm for improvement of oil consumption estimation and policy making: The cases of Canada,United Kingdom,and South Korea

This paper presents an adaptive network based fuzzy inference system (ANFIS)–auto regression (AR)–analysis of variance (ANOVA) algorithm to improve oil consumption estimation and policy making. ANFIS algorithm is developed by different data preprocessing methods and the efficiency of ANFIS is examined against auto regression (AR) in Canada, United Kingdom and South Korea. For this purpose, mean absolute percentage error (MAPE) is used to show the efficiency of ANFIS. The algorithm for calculating ANFIS performance is based on its closed and open simulation abilities. Moreover, it is concluded that ANFIS provides better results than AR in Canada, United Kingdom and South Korea. This is unlike previous expectations that auto regression always provides better estimation for oil consumption estimation. In addition, ANOVA is used to identify policy making strategies with respect to oil consumption. This is the first study that introduces an integrated ANFIS–AR–ANOVA algorithm with preprocessing and post processing modules for improvement of oil consumption estimation in industrialized countries.     

求解线性模型稳健参数估计的整数编码遗传算法

线性模型回归系数的一些稳健估计如LMS、LQS、LTS、LTA的应用越来越广泛,然而它们的精确计算依赖于NP难题,在遇到高维大规模数据集时不可能在较短时间内得到精确解.为尽快得到较高精度的近似解,提出了求解线性模型的稳健参数估计的整数编码遗传算法,通过计算机模拟试验验证了算法可以更快地找出全局最优解.     

均匀分布参数的无偏估计及其分布

讨论了均匀分布未知参数无偏估计量的分布密度,利用无偏估计量构造出一些新的样本函数,并且利用给出的样本函数推导出了未知参数的置信区间.所得到结果改善了现有的估计,易于计算.     

Identification of three-dimensional electric conductivity changes from time-lapse electromagnetic observations

We present a novel solution algorithm for 3D parameter identification based on low frequency electromagnetic data. With focus on large-scale applications such as monitoring of subsea oil production, CO₂ sequestration, and geothermal systems, the proposed solution algorithm is designed to meet challenges related to low parameter sensitivity, nonuniqueness of the inverse solutions, nonlinearity in the mapping from the data to the parameter space, and costly numerical simulations. Motivated by earlier investigations on the relation between sensitivity, nonlinearity and scale, the proposed solution approach is based on a reduced, composite parameter representation. Though a reduced representation restricts the solution space, flexibility with respect to which parameter functions that can be represented is obtained by facilitating the estimation of the structure and smoothness of the representation itself. Moreover, the resolution of the parameter function is detached from the computational grid and determined as part of the estimation. The performance of the proposed solution algorithm is illustrated through numerical examples for identification of underground electric conductivity changes from time-lapse electromagnetic observations.     

A self-optimizing inverse analysis method for estimation of cyclic elasto-plasticity model parameters

In this paper, a novel inverse analysis methodology call a Self-Optimizing Inverse Method (Self-OPTIM) has been presented, which inversely estimates cyclic elasto-plastic constitutive model parameters using global forces and displacement on the same partial boundaries and full-(or partial-) field displacement data. A novelty of the methodology is that it automatically self-estimates material parameters by updating “full-field” reference stresses and strains through two parallel nonlinear finite element simulations. Although a well-known classical cyclic plasticity model is chosen in this paper, it must be emphasized that the proposed Self-OPTIM method is a model-independent method, which means that any advanced model can be naturally integrated with the proposed methodology. Thus, using numerically generated test data of low-carbon steel specimens (AISI 1010), the proposed Self-OPTIM method has been verified showing its successful performance to estimate nonlinear isotropic and kinematic hardening parameters, yield stress, Young’s modulus and Poisson ratio. The effects of experimental noises from CCD camera and measurement errors of the boundary forces are also investigated for the Self-OPTIM method.     

Improved Kinetic Rate Constants for Nylon 6, Nylon 6,6, and Nylon 6/6,6 Copolymer

Nylon 6 and 6,6 literature data are collected over a wide range of water concentrations and temperatures (0 ≤ [W]₀ ≤ 40.8 wt%, 200 ≤ T ≤300 °C) and used to fit parameters in an updated batch reactor model. The resulting copolymerization model uses side reactions to account for the complex influence of water on kinetics and reaction equilibria. The proposed parameter estimates result in a significant improvement in the fit to the data, corresponding to a 73% reduction in the weighted‐least‐squares objective function compared to when the parameters of Arai et al. are used. Copolymerization simulations are conducted at industrially relevant conditions, shedding light on the complex influence of water and on the potential to include waste nylon 6 cyclic dimer in the feedstock. The model and parameter estimates will be helpful in future models of nylon 6/6,6 copolymerization in continuous reactor systems.     

Mechanical characterization of polytetrafluoroethylene polymer using full-field displacement method

The aim of this work is to estimate two important material properties of the polytetrafluoroethylene (PTFE) polymer by means of a single experimental test. The displacement fields around a crack tip are used for estimating the modulus of elasticity (or, Young's modulus) and Poisson's ratio. These parameters are evaluated by fitting linear fracture mechanic expression of displacement fields in the vicinity of the crack, for mode I, to the experimental data. Measurements of these displacements are carried out using digital image correlation (DIC) method. In this way, the experimental procedure is conducted by loading a double-edge-cracked plate specimen. In order to validate the results, two available experimental tests have been performed. The modulus of elasticity is determined by means of the tensile test, using a standard test machine. Moreover, the Poisson's ratio is obtained by measuring lateral compressive and longitudinal extensional strain using DIC method.     

On a model of sequential point patterns

A finite point process motivated by the cooperative sequential adsorption model is proposed. Analytical properties of the point process are considered in details. It is shown that the introduced point process is useful for modeling both aggregated and regular point patterns. A possible scheme of maximum likelihood estimation of the process parameters is briefly discussed. V. Shcherbakov is on leave from Laboratory of Large Random Systems, Faculty of Mathematics and Mechanics, Moscow State University, Moscow.