基于特征和知识的精密注塑模设计和制造系统

采用先进的CAD/CAM技术对传统模具设计制造方法进行改造,是提高模具设计制造质量和效率的关键.此文提出基于特征和知识的才法构造精密注塑模设计和制造系统,并介绍了系统的总体方案和系统包含的各模块功能.这一系统是浙江省科委八五攻关项目“精密注塑模设计和制造(CAD/CAM)技术”的核心.     

Prediction of CO maximum ground level concentrations in the Bay of Algeciras, Spain using artificial neural networks

The region of the Bay of Algeciras is a very industrialized area where very few air pollution studies have been carried out. The main objective of this work has been the use of artificial neural networks (ANNs) as a predictive tool of high levels of ambient carbon monoxide (CO). Two approaches have been used: multilayer perceptron models (MLPs) with backpropagation learning rule and k-Nearest Neighbours (k-nn) classifiers, in order to predict future peaks of carbon monoxide. A resampling strategy with twofold cross-validation allowed the statistical comparison of the different topologies and models considered in the study. The procedure of random resampling permits an adequate and robust multiple comparisons of the tested models and allow us to select a group of best models.