关于中国加入WTO后影视高等教育体系的构想

关于中国加入WTO以后,如何适应新的形势和面对新的竞争,从而构建新的影视高等教育体系?本文从以下三个方面进行了论述:21世纪是彩色读图时代,影视知识技术是人才所必备的三大基本知识技术之一;中国的影视教育必须构建学历教育、职业技能教育和成人继续教育相结合的国民教育体系以及四合一的教育教学模式和产学研相结合的人才培养体制及其运行机制。     

聚丙烯改性技术进展

介绍了近年来聚丙烯改性技术研究方面的进展．研究内容包括用结晶成核剂对聚丙烯的晶型进行设计，用橡胶、矿物填料等对聚丙烯进行改性．     

Viscosity of compressed cold lake bitumen

Viscosity data over a temperature range of 37-115°C and a pressure range of 0-10 MPa gauge are presented for a Cold Lake bitumen sample. These data show that the compression of Cold Lake bitumen results in a significant increase in its viscosity. Results are compared with those from a similar study on an Athabasca bitumen sample. Also presented are two correlations for the effects of pressure and temperature on the viscosity of Cold Lake bitumen.     

Gas hold-up in non-newtonian solutions in a bubble column with internals

Bubble characteristics and gas hold-up were studied in a two phase (air-aqueous CMC solution) bubble column provided with helical coils and straight tubes as internals. The effects of superficial gas velocity, rheological properties, and volume fraction covered by the internals, on gas hold-up were studied. Hold-up values determined directly and by simultaneous pressure drop measurements matched well. Enhancement of gas hold-up values up to 55 per cent was achieved in systems using internals. The gas hold-up results were also compared with the values obtained from correlations reported in the literature.     

存储器相关概念教学分析

为了改变存储器知识点“学生难学,教师难教”的局面,在教学过程中通过理论联系实际的方法介绍存储器相关概念,期望学生能够加深理解,为后续课程的学习及往后在工程实践中运用相关知识解决实际问题打下坚实的基础。     

基于引力搜索算法的分数阶变异时序回归GSA-TSGM(1,1)模型

为了利用分数阶累加算子在灰色短期预测中的高效性能,首次将分数阶累加算子引入变异时序回归模型以期取得更高的预测精度。主要方法如下：首先取湖北省链子崖某监测点1978—1987年的十年数据作为训练集并使用引力搜索算法确定最佳分数阶累加阶数,而1988—1993年的六年数据作为验证集验证提出的模型;其次对比了经典灰色模型GM(1,1)、分数阶累加灰色模型、变异时序回归模型TSGM(1,1)三种灰色模型。结果如下：首先修正了陈西江等人变异时序回归模型仿真时出现的错误,其次表明了相比于其他的模型,基于引力搜索算法的分数阶累加时序回归模型在进行灰色长期预测中具有较高的预测精度。因此,通过分数阶累加算子提高了灰色理论中长期预测模型的精度,为灰色长期预测提供了指导。     

The clustering-based case-based reasoning for imbalanced business failure prediction: a hybrid approach through integrating unsupervised process with supervised process

Case-based reasoning (CBR) is one of the main forecasting methods in business forecasting, which performs well in prediction and holds the ability of giving explanations for the results. In business failure prediction (BFP), the number of failed enterprises is relatively small, compared with the number of non-failed ones. However, the loss is huge when an enterprise fails. Therefore, it is necessary to develop methods (trained on imbalanced samples) which forecast well for this small proportion of failed enterprises and performs accurately on total accuracy meanwhile. Commonly used methods constructed on the assumption of balanced samples do not perform well in predicting minority samples on imbalanced samples consisting of the minority/failed enterprises and the majority/non-failed ones. This article develops a new method called clustering-based CBR (CBCBR), which integrates clustering analysis, an unsupervised process, with CBR, a supervised process, to enhance the efficiency of retrieving information from both minority and majority in CBR. In CBCBR, various case classes are firstly generated through hierarchical clustering inside stored experienced cases, and class centres are calculated out by integrating cases information in the same clustered class. When predicting the label of a target case, its nearest clustered case class is firstly retrieved by ranking similarities between the target case and each clustered case class centre. Then, nearest neighbours of the target case in the determined clustered case class are retrieved. Finally, labels of the nearest experienced cases are used in prediction. In the empirical experiment with two imbalanced samples from China, the performance of CBCBR was compared with the classical CBR, a support vector machine, a logistic regression and a multi-variant discriminate analysis. The results show that compared with the other four methods, CBCBR performed significantly better in terms of sensitivity for identifying the minority samples and generated high total accuracy meanwhile. The proposed approach makes CBR useful in imbalanced forecasting.     

城市异形排涝通道消能防冲新技术

该文针对感潮河段魁岐排涝二站?型复杂流道的不利冲刷问题,通过建立整体水工模型,研究城市异形排涝通道消能防冲新技术,提出导流坎、消力坎和消力墩联合消能防冲技术,探索其在宽幅水位变化、多工程交替运行及通航情况下的适应能力,并给出水力设计方法。研究表明,联合消能工对复杂工况适应良好,能有效解决异形排涝通道的消能防冲问题。     

New results on PWARX model identification based on clustering approach

This paper deals with the problem of piecewise auto regressive systems with exogenous input(PWARX) model identification based on clustering solution. This problem involves both the estimation of the parameters of the affine sub-models and the hyper planes defining the partitions of the state-input regression. The existing identification methods present three main drawbacks which limit its effectiveness. First, most of them may converge to local minima in the case of poor initializations because they are based on the optimization using nonlinear criteria. Second, they use simple and ineffective techniques to remove outliers. Third, most of them assume that the number of sub-models is known a priori. To overcome these drawbacks, we suggest the use of the density-based spatial clustering of applications with noise(DBSCAN) algorithm. The results presented in this paper illustrate the performance of our methods in comparison with the existing approach. An application of the developed approach to an olive oil esterification reactor is also proposed in order to validate the simulation results.     

多CPU控制器在大功率整流控制系统中的应用

介绍了利用多CPU来设计整流控制器，解决大功率整流装置产生的磁场对整流计算机控制系统产生的干扰。