Optimal seismic design of steel structures by an efficient soft computing based algorithm

The main aim of this study is to propose advanced soft computing techniques for the optimal seismic design of real steel structures subjected to natural ground motion records. For the solution of the optimization problem an efficient combination of the particle swarm optimization (PSO) and adaptive virtual sub-population (AVSP) algorithms is proposed. Also an efficient combination of the adaptive neuro-fuzzy inference system (ANFIS), wavelet transforms (WT) and radial basis function (RBF) neural networks, termed as fuzzy wavelet radial basis function (FWRBF), is proposed to accurately predict the structural responses. The numerical results demonstrate the computational advantages of the proposed methodology.     

A Bayesian belief network analysis of factors influencing wildfire occurrence in Swaziland

The impacts of wildfires on ecosystems and the factors contributing to their occurrence are increasingly receiving global attention. Advances in satellite remote sensing and information technology provide an opportunity to study these complex interrelationships. A Bayesian belief network (BBN) model was developed from a set of 12 biotic, abiotic and human variables to determine factors that influence wildfire activity in Swaziland using wildfire data from the Terra and Aqua satellites' Moderate Resolution Imaging Spectroradiometer (MODIS) for the period 2001–2007. These were geospatially integrated in the geographic information system (GIS) software ArcView and input into the software Netica for BBN analyses. Land cover, elevation, and climate (mean annual rainfall and mean annual temperature) were found to be strong predictors of wildfire occurrence, while aspect had the least influence on the wildfire occurrence. The model had a high predictive accuracy with an error rate of 9.62%, and an area under the receiver-operating characteristic (ROC) curve of 0.961. The study demonstrates how domain or field knowledge and limited empirical and GIS data can be combined within a BBN model to assist in determining key fire management interventions and lays the foundation for the future development of advanced and dynamic models.     

Inference under imprecise probability assessments

Under partial knowledge, the use of the precise probability might be misleading. However, it is possible to process imprecise assessments, such as comparative previsions or grades of previsions. Rules for checking their coherence with the theoretical model and for making inference are given. Sometimes the derived conclusions might provide us with a complete answer for a given problem. In any case, technical tools can measure the imprecision of the answer and reveal if the analysis is thorough enough.     

Tool wear detection with fuzzy classification and wavelet fuzzy neural network

In the paper, a new method of tool wear detection with cutting conditions and detected signals is presented, which includes the model of wavelet fuzzy neural network with acoustic emission (AE) and the model of fuzzy classification with motor current. The results of tool wear estimated by cutting conditions and detected signals (spindle motor current, feed motor current and AE) are fused by fuzzy inference. Experimental results show that the method of tool wear detection is reliable and practical.     

基于模糊理论和三段论推理的电影情感分类

人类情感反应具有模糊属性,针对直接应用视频底层特征识别情感类型难以提高精确度的问题,本研究在情感关联底层特征的基础上引入了模糊隶属函数,采用最大隶属度模糊化原则对底层特征进行处理,最终得到一种蕴含情感信息的特征来描述视频片段,为随后采用三段论推理实现电影情感分类提供了合适的特征,这种特征能缩短情感鸿沟。三类基本情感的分类精确度都超过84％,与现有方法相比,分类精确度平均提高了9.33％。因此本研究所提出的算法能有效地提高电影情感分类的精确度。     

描述逻辑系统UEVN中概念的包含关系

描述逻辑中的非标准推理是目前研究者们所关注的焦点问题,它主要包括：最具体概念、最小公共包含、匹配问题及概念的重写等。过去人们主要研究那些不含数量限制的描述逻辑系统,该文研究的是描述逻辑系统UEVN中的一种重要的非标准推理,它同时含有了并、存在约束量词、全称约束量词和数字限制。利用定义UEVN中概念的描述树及描述树之间的同态关系,给出了概念之间包含关系的充要条件。     

不同蕴涵算子下三I方法的解

以R_M、R_Z、R₀三个蕴涵算子为基础,研究了11种形式的三I算法的解,并在此基础上给出了这11种解的同一形式：B*（y）=SUP{A*（x）∧φ（x,y）},其中φ：X×Y→[0,1]表示某一函数,而且φ（x,y）与E_Y的选取依赖蕴涵算子R_i的选取。     

实体语法系统框架下的化工过程调控流图模型

针对复杂化工过程多尺度“三传一反”机理建模问题,提出基于实体语法系统的复杂化工过程调控流图模型,并以连续搅拌釜式反应器（CSTR）系统为例介绍了这类模型在具体问题中的应用。结果表明,调控流图作为一种图式化模型,能够将复杂化工过程多尺度的“三传一反”过程用同一形式表达,降低了建模难度,提高了模型的表达能力,有利于利用图论、定性仿真、微分方程等理论与方法对系统进行综合分析。调控流图继承了实体语法系统跨尺度建模和良好扩展性的特征,为宏观、微观等多尺度信息的融合提供了基础。     

命题逻辑系统中公式的概率真度理论

通过引入赋值密度函数、边缘密度函数等概念给出了连续值命题逻辑系统中公式概率真度的定义,明确了概率真度在[0,1]中的分布情况,并得到了一些概率真度的推理规则;引入相似度,给出了伪距离的定义,确定了二者之间的关系。     

Non‐linear model reduction for uncertainty quantification in large‐scale inverse problems

We present a model reduction approach to the solution of large‐scale statistical inverse problems in a Bayesian inference setting. A key to the model reduction is an efficient representation of the non‐linear terms in the reduced model. To achieve this, we present a formulation that employs masked projection of the discrete equations; that is, we compute an approximation of the non‐linear term using a select subset of interpolation points. Further, through this formulation we show similarities among the existing techniques of gappy proper orthogonal decomposition, missing point estimation, and empirical interpolation via coefficient‐function approximation. The resulting model reduction methodology is applied to a highly non‐linear combustion problem governed by an advection–diffusion‐reaction partial differential equation (PDE). Our reduced model is used as a surrogate for a finite element discretization of the non‐linear PDE within the Markov chain Monte Carlo sampling employed by the Bayesian inference approach. In two spatial dimensions, we show that this approach yields accurate results while reducing the computational cost by several orders of magnitude. For the full three‐dimensional problem, a forward solve using a reduced model that has high fidelity over the input parameter space is more than two million times faster than the full‐order finite element model, making tractable the solution of the statistical inverse problem that would otherwise require many years of CPU time. Copyright © 2009 John Wiley & Sons, Ltd.