工业废酸与高铁赤泥制取聚合氯化铝铁的实验研究

介绍了常压下使用工业废盐酸和高铁赤泥制备聚合氯化铝铁絮凝剂的基本工艺.研究了液固比、温度、浸出时间对浸出率的影响,总结了制备聚合氯化铝铁的最佳工艺条件,并对产品进行了絮凝性能实验.该工艺为赤泥的综合利用和工业废酸处理开辟了一条新途径.     

基本放大器的复合教学法设计

文章以电子技术课程中的基本放大器知识点为教学示例,采用理论推导、软件仿真和程序设计相结合的复合教学设计,实现了知识严密性、直观性与趣味性的结合,增强了学生对重要知识点的理解与掌握.     

带泊松跳的中立型随机时滞微分方程解的指数稳定性

主要利用Laypunov泛函方法和随机分析理论,研究带泊松跳的中立型随机时滞微分方程解的存在惟一性和解的指数稳定性．所得结果覆盖了许多非线性时滞微分方程已经存在的某些理论,而且实验说明此结论比以往的结论更容易验证．     

THOR网在机器人装配系统装配质量预测和分析中的应用

装配质量对产品的性能和可靠性有直接的影响．基于一类高级的Petri网——THOR网，对机器人装配系统装配质量的预测进行了研究，提出了系统分析装配质量的模块化建模方法．该方法可以对各种装配策略和手段与装配质量间的关系进行研究，是实现机器人装配系统装配质量评价与控制的有力工具．     

基于不完美发送端信道状态信息的多输入多输出系统线性预编码传输方案

空时分组码和线性预编码可显著提高多输入多输出系统的性能,但空时分组码性能易受天线相关性的影响,线性预编码性能易受发送端信道状态信息(CSIT)误差的影响.提出一种空时分组码与线性预编码级联的方案,可在相关天线组和不完美的CSIT条件下,获得良好的性能增益.     

苦楝果实植物杀虫剂的开发研究

报道了用苦楝果实制备植物杀虫剂－“灭虫萜”的研究结果。通过室内、小区和大面积推广试验，使用“灭虫萜”乳剂，稀释浓度为２００－５００倍，对菜青虫防效为８９．６７％－９６．６７％、棉蚜防效为７６．７８％－８０．５３％等多种农业害虫有较好的防治效果。     

一种掺铒离子化合物的上转换发光的研究

合成了一种新型掺Er 离子化合物的上转换发光材料,测量了此材料在短波段的上转换发光光谱和激发光谱,初步研究了它们在短波段的上转换发光过程:短波段的上转换发光分别为三光子或四光子过程.还比较了掺杂敏化剂和不掺敏化剂两种情况下的上转换发光的变化情况     

An E.Commerce Recommender System Based on Content-Based Filtering

Content-based filtering E-commerce recommender system was discussed fully in this paper. Users＇ unique features can be explored by means of vector space model firstly. Then based on the qualitative value of products informa tion, the recommender lists were obtained. Since the system can adapt to the users＇ feedback automatically, its performance were enhanced comprehensively. Finally the evaluation of the system and the experimental results were presented.     

EDTA络合溶胶-凝胶法制备Ca3(VO4)2:Eu发光材料

以金属硝酸盐和EDTA为原料,用EDTA络合溶胶—凝胶法制备出Ca3(VO4)2:Eu发光材料,分别用FTIR、XRD、荧光分光光度计等手段对Ca3(VO4)2:Eu荧光粉进行表征,结果表明:仅在740℃焙烧即可得到颗粒细小,组分均匀,纯物相的Ca3(VO4)2:Eu发光材料。     

Yarn Quality Prediction and Diagnosis Based on Rough Set and Knowledge-Based Artificial Neural Network

In the spinning process, some key process parameters（ i. e.,raw material index inputs） have very strong relationship with the quality of finished products. The abnormal changes of these process parameters could result in various categories of faulty products. In this paper, a hybrid learning-based model was developed for on-line intelligent monitoring and diagnosis of the spinning process. In the proposed model, a knowledge-based artificial neural network（ KBANN） was developed for monitoring the spinning process and recognizing faulty quality categories of yarn. In addition,a rough set（ RS）-based rule extraction approach named RSRule was developed to discover the causal relationship between textile parameters and yarn quality. These extracted rules were applied in diagnosis of the spinning process, provided guidelines on improving yarn quality,and were used to construct KBANN. Experiments show that the proposed model significantly improve the learning efficiency, and its prediction precision is improved by about 5. 4% compared with the BP neural network model.