大型复杂雕塑型面钢架特征曲线快速设计方法

为解决复杂雕塑型面钢架的特征曲线快速设计问题,基于几何约束建立设计问题的数学模型,并完成了数学问题的求解;针对特征曲线分支和多孤立图形的问题,采用广度优先遍历算法对特征曲线进行处理,剔除无用分支和孤立图形。通过设置保留参考平面来避免选择歧义的问题,解决了型面分块问题。采用复杂曲线转换为多段线的方法来解决复杂曲线加工设备不能处理的问题,并设计了转换精度分析方法。设计系统采用程序法建模的方式来进行设计系统的开发,并通过实例验证了设计系统的功能。

Symbiosis Analysis on Industrial Ecological System

Industrial ecological system is a sustainable mode of modern industry development. Industrial symbiosis, a sub-field of industrial ecology, engages traditionally separate industries in a collective approach, involving exchange of materials, energy, water, and/or by-products, to enhance competitive ability and environmental performance. To construct a symbiosis analysis method, this article employs a number of parameters embodying information about materials, energy and economics as the main essential parameters in system analysis and introduces symbiosis profit and symbiotic consumption elements as the economic indicators. A modeling and simulation program is designed using the agent-based modeling approach to simulate the evolvement of a hypothetical coal-based industrial system and the change of symbiosis conditions in the process of construction is examined. The simulation program built using the Swarm library, which is a freely available multi-agent simulation package, provides a useful demonstration for the symbiosis analysis method.

Modelling of Wage and Income Distributions Using the Method of L-Moments

Commonly used statistical procedure to describe the observed statistical sets is to use their conventional moments or cumulants. When choosing an appropriate parametric distribution for the data set is typically that parameters of a parametric distribution are estimated using the moment method of creating a system of equations in which the sample conventional moments lay in the equality of the corresponding moments of the theoretical distribution. However, the moment method of parameter estimation is not always convenient, especially for small samples. An alternative approach is based on the use of other characteristics, which the author calls L-moments. L-moments are analogous to conventional moments, but they are based on linear combinations of order statistics, i.e., L-statistics. Using L-moments is theoretically preferable to the conventional moments and consists in the fact that L-moments characterize a wider range of distribution. When estimating from sample L-moments, L-moments are more robust to the presence of outliers in the data. Experience also shows that, compared to conventional moments, L-moments are less prone to bias of estimation. Parameter estimates obtained using L-moments are mainly in the case of small samples often even more accurate than estimates of parameters made by maximum likelihood method. Using the method of L-moments in the case of small data sets from the meteorology is primarily known in statistical literature. This paper deals with the use of L-moments in the case for large data sets of income distribution （individual data） and wage distribution （data are ordered to form of interval frequency distribution of extreme open intervals）. This paper also presents a comparison of the accuracy of the method of L-moments with an accuracy of other methods of point estimation of parameters of parametric probability distribution in the case of large data sets of individual data and data ordered to form of interval frequency distribution.

Rapid Convergence of a New Wave Iterative Algorithm Used to Model a Patch Structure

The wave iterative method is a numerical method used in the electromagnetic modeling of high frequency electronic circuits. The object of the authors＇ study is to improve the convergence speed of this method by adding a new algorithm based on filtering techniques. This method requires a maximum number of iterations, noted Nmax, to achieve the convergence to the optimal value. This number wilt be reduced in order to reduce the computing time. The remaining iterations until Nmax will be calculated by the new algorithm which ensures a rapid convergence to the optimal result.

Bayesian Reliability Assessment and Degradation Modeling with Calibrations and Random Failure Threshold

A degradation model with a random failure threshold is presented for the assessment of reliability by the Bayesian approach. This model is different from others in that the degradation process is proceeding under pre-specified periodical calibrations. And here a random threshold distribution instead of a constant threshold which is difficult to determine in practice is used. The system reliability is defined as the probability that the degradation signals do not exceed the random threshold. Based on the posterior distribution estimates of degradation performance, two models for Bayesian reliability assessments are presented in terms of the degradation performance and the distribution of random failure threshold. The methods proposed in this paper are very useful and practical for multi-stage system with uncertain failure threshold. This study perfects the degradation modeling approaches and plays an important role in the remaining useful life estimation and maintenance decision making.