基于无监督聚类的约简支撑向量机

为解决标准支撑向量机算法所面临的巨大的计算量问题,Lee和Mangasarian提出了约简支撑向量机算法;但他们选取的“支撑向量”是从训练样本里面任意选的,其分类结果受随机性影响比较大。该文利用简单的无监督聚类算法,在样本空间中选取了一些具有较强代表性的样本作为“支撑向量”,再运用约简支撑向量机算法,有效地减少了运算量。实验验证文中方法可以用较少的“支撑向量”来得到较高的识别率,同时运行时间也大大缩短。     

无线移动网中的队列降质接纳控制

提出了在无线移动网中基于队列的降质接纳控制算法,它区分了实时业务和非实时业务,同时考虑了切换优先和业务优先两种优先级。在网络带宽资源不足时,通过降低某些可容忍质量下降的呼叫的质量并与队列缓存呼叫相结合的方式,来获得小区中高优先级业务的新呼叫阻塞率和切换呼叫中断率均很低的性能改善。性能分析结果显示该算法简单易实现,具有较低的新呼叫阻塞率、切换呼叫中断率和较高的系统利用率。     

左江水利枢纽工程的质量监督

叙述了广西左江水利枢纽工程的质量监督体系、质量监督工作方法以及已验工程质量情况。     

酶法制取罗非鱼动物蛋白水解液的研究

利用木瓜蛋白酶、中性蛋白酶、酸性蛋白酶、碱性蛋白酶和菠萝蛋白酶对罗非鱼肉进行水解制备水解动物 蛋白,以水解度(DH)为指标对水解过程进行分析,选取中性蛋白酶作为适宜的单酶并研究其酶法水解最适宜的工 艺条件。为进一步提高水解度,进行了中性蛋白酶与酸性蛋白酶复合酶解的研究。     

一类关于不确定性机器人的鲁棒控制策略

基于计算力矩结构,研究参数和结构不确定的机器人轨迹跟踪的鲁棒控制策略.其 特点是利用了机器人不确定动力学的集中包络函数,在该包络函数已知的情况下,设计的非 线性连续补偿控制律能够有效消除系统的不确定性影响,保证系统达到三种不同的稳定性结 果.另外,在该包络函数参数未知时,还没计了一个新颖的在线辨识器,可保证系统指数意义 下的渐近收敛或一致有界.     

Methodology selecting packaging materials combined multi-sensory experience and fuzzy three-stage network DEA model

Materials selection involves thousands of material types and evaluation parameters. Considering multisensory experience for packaging materials selection is an important trend but a difficult duty that involves uncertainty due to the subjectivity of humans. The main aim of this study is to establish a model to determine the consumer sensory experience of packaging materials as a function supporting packaging material selection development. When the consumer interacts before and after opening the packaging, vision and tactility are the foremost sensory feedback types, which are difficult to measure for fuzziness and uncertainty. Thus, given the consumer senses of vision and tactility, this paper addresses the relationship between sensory experience and packaging material properties. This is creatively formulated in three stages from the physical properties and the psycho-physical to the affective levels. The proposed approach that combines fuzzy theory and three-stage network data envelopment analysis (DEA) is used to rank 12 toning lotion bottle packaging materials. It is validated that this model allows a clear decision-making basis for material selection in packaging design, and physiological signal tests provided key data to support material selection. Moreover, it realized the function of predicting physiological signal according to material properties by backpropagation neural network (BPNN).