The generalized discrete ‐centroid problem

The ‐centroid problem or leader–follower problem is generalized considering different customer choice rules where a customer may use facilities belonging to different firms, if the difference in travel distance (or time) is small enough. Assuming essential goods, some particular customer choice rules are analyzed. Linear programming formulations for the generalized ‐medianoid and ‐centroid problems are presented and an exact solution approach is applied. Some computational examples are included.     

论高等教育理念创新

阐述现代高等教育理念创新的具体内容,教育价值理念要从适应论到超越论飞跃;教育质量观念要从一元化向多元化转变;教育本土化理念要向教育国际化转变;树立“三创教育”理念。     

神经网络在缺陷识别中的应用

为了准确、快速地进行缺陷识别，介绍了一种新型的前馈神经网络模型，即径向基概率神经网络。与以往的算法相比，该方法具有分类识别精度高且速度快的优点。仿真获得了很好的结果。     

产业集群与中小企业发展

对中小企业的发展困境进行了论述，指出产业集群可促进中小企业的发展，并提出了产业集群形成的对策措施、自身的组织特点及运行规律。     

假乙内酰硫脲酸在铜-钼浮选分离中的抑制性能(英文)

将合成的假乙内酰硫脲酸(PGA)用作铜-钼分离抑制剂。该药剂闭路实验结果表明:假乙内酰硫脲酸在较小的用量下对黄铜矿有较强的抑制作用,经一次粗选、一次扫选、两次精选,可获得Mo品位大于26%、回收率大于89%的浮选指标,而用Na2S做抑制剂时钼的回收率下降了2%。药剂吸附量测试结果表明,PGA与丁基黄药在矿物表面发生竞争吸附,PGA在黄铜矿表面上的吸附量远大于在辉钼矿表面的。红外光谱分析表明,PGA在黄铜矿表面是化学吸附,而在辉钼矿表面属于物理吸附。前线轨道计算结果表明,在PAG分子中,硫原子是反应活性的中心。利用矿物、丁黄药及PGA的费米能级能量大小可以从电化学作用角来度解释PGA的抑制机理。     

模糊竞争神经网络在线辨识及在CSTR中的应用

基于改进的T-S模糊模型构造了一种自适应模糊竞争神经网络模型(FCNN),给出了网络的连接结构和学习算法。它依据模糊竞争学习算法确定系统的模糊空间和模糊规则数,得出每个样本对每条规则的适用程度,并利用卡尔曼滤波算法在线辨识FCNN的后件参数。将其应用于化工过程连续搅拌反应器(CSTR)的建模中,仿真结果表明,FCNN具有结构简洁、收敛速度快、辨识精度高等特点,可当作复杂系统建模的一种有效手段。     

关于国有重点煤炭企业平等竞争问题的探讨

为了把党的十五届四中全会精神落实到煤炭企业的生产经营实践之中 ,运用企业竞争机制的理论和方法 ,阐述了国有重点煤炭企业在社会主义市场经济中的竞争环境 ,剖析了企业当前面临的主要问题 ,分析了国有煤炭企业的竞争优势和竞争能力 ,提出了企业实现平等竞争应采取的一些具体思路 ,以推动国有重点煤炭企业全面进入市场。     

全球化加剧视野下的建筑学高素质人才培养策略探讨

建筑学高素质人才的培养问题是中国建筑教育界面临的重大问题。文章首先考察了全球化加剧视野下建筑学新的发展倾向,在此基础上分析了建筑学高素质人才应具备的素质结构,并结合硕士层次的当代欧洲建筑创作思潮课程,尝试探讨全球化视野下建筑学高素质人才的培养策略。     

Effect of competitive adsorption between sodium tripolyphosphate and naphthalene superplasticizer on fluidity of cement paste

The adsorption amount, ζ-potential of cement particles and fluidity of cement paste were tested to research the competitive adsorption between naphthalene superplasticizer(FDN) and STPP. The experimental results showed that the presence of STPP could significantly improve the fluidity of cement paste and reduce the fluidity loss with FDN. There existed a competitive adsorption between STPP and FDN. STPP and calcium ions formed complexes; they preferentially adsorbed onto surface of cement particles and preempt adsorption points of FDN; and it reduced adsorption amount of FDN. In the absence of STPP, saturation adsorption amount of FDN was 5.93 mg/g; but when the dosage of STPP was 0.1%, it reduced to 4.3 mg/g(about 72.5%). The adsorption amount of FDN was reduced by STPP, but ζ-potential of cement particles enhanced and fluidity of cement paste increased because of strong negative charge effect of the complexes. Adsorption of the complexes would delay Ca2+ into liquid and inhibit formation of active adsorption points. Then, content of FDN in liquid increased with the addition of STPP and ζ-potential of cement particles became stable. In this way, fluidity loss of cement paste reduced.     

竞争型径向基过程神经网络时序分类器

针对时序分类问题,提出一种竞争型径向基过程神经网络时序分类器.给出了复合竞争过程神经元单元的定义,引入复合竞争过程神经元隐层,利用竞争型径向基过程神经网络输入为时变函数的特点,由复合竞争过程神经元单元完成对过程式输入信息的模式匹配和时空聚合运算,给出了具体学习算法,省去了输出层线性连接权的计算,简化了网络结构和训练过程,提高了网络泛化能力.最后以UCI数据集多变量时序分类问题验证了分类器的有效性.